1
|
Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Giovanni Maldonado Briones H, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Augusto Joaquim O, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol. PLoS One 2024; 19:e0301638. [PMID: 38913670 PMCID: PMC11195963 DOI: 10.1371/journal.pone.0301638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/15/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. METHODS To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. DISCUSSION Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
Collapse
Affiliation(s)
- Obianuju Genevieve Aguolu
- Division of Epidemiology, College of Public Heath, The Ohio State University, Columbus, Ohio, United States of America
| | - Moses Chapa Kiti
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Kristin Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic–Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | | | | | | | - Gifta John
- Christian Medical College Vellore, Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Sara Kim
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Holin Chen
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | - Inci Yildirim
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| |
Collapse
|
2
|
Taube JC, Susswein Z, Colizza V, Bansal S. Respiratory disease contact patterns in the US are stable but heterogeneous. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306450. [PMID: 38712118 PMCID: PMC11071567 DOI: 10.1101/2024.04.26.24306450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across spatial scales and demographic and social classifications, whether there is seasonality to contact patterns, and what social behavior looks like at baseline in the absence of an ongoing pandemic. Methods We analyze spatiotemporal non-household contact patterns across 11 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of pre-pandemic data on contact in the US, we also use a regression approach to produce baseline contact estimates to fill this gap. Findings Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of white individuals and contacts at work or social events change the most under increased national incidence. Interpretation We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory disease transmission in the US.
Collapse
Affiliation(s)
- Juliana C. Taube
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Zachary Susswein
- Department of Biology, Georgetown University, Washington, DC, USA
| | | | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, DC, USA
| |
Collapse
|
3
|
Dall’Amico L, Kleynhans J, Gauvin L, Tizzoni M, Ozella L, Makhasi M, Wolter N, Language B, Wagner RG, Cohen C, Tempia S, Cattuto C. Estimating household contact matrices structure from easily collectable metadata. PLoS One 2024; 19:e0296810. [PMID: 38483886 PMCID: PMC10939291 DOI: 10.1371/journal.pone.0296810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/18/2023] [Indexed: 03/17/2024] Open
Abstract
Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
Collapse
Affiliation(s)
| | - Jackie Kleynhans
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laetitia Gauvin
- ISI Foundation, Turin, Italy
- Institute for Research on sustainable Development, UMR215 PRODIG, Aubervilliers, France
| | - Michele Tizzoni
- ISI Foundation, Turin, Italy
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | | | - Mvuyo Makhasi
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Nicole Wolter
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Brigitte Language
- Unit for Environmental Science and Management, Climatology Research Group, North-West University, Potchefstroom, South Africa
| | - Ryan G. Wagner
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Agincourt, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stefano Tempia
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ciro Cattuto
- ISI Foundation, Turin, Italy
- Department of Informatics, University of Turin, Turin, Italy
| |
Collapse
|
4
|
Nagpal S, Kumar R, Noronha RF, Kumar S, Gupta D, Amarchand R, Gosain M, Sharma H, Menon GI, Krishnan A. Seasonal variations in social contact patterns in a rural population in north India: Implications for pandemic control. PLoS One 2024; 19:e0296483. [PMID: 38386667 PMCID: PMC10883557 DOI: 10.1371/journal.pone.0296483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 12/11/2023] [Indexed: 02/24/2024] Open
Abstract
Social contact mixing patterns are critical to model the transmission of communicable diseases, and have been employed to model disease outbreaks including COVID-19. Nonetheless, there is a paucity of studies on contact mixing in low and middle-income countries such as India. Furthermore, mathematical models of disease outbreaks do not account for the temporal nature of social contacts. We conducted a longitudinal study of social contacts in rural north India across three seasons and analysed the temporal differences in contact patterns. A contact diary survey was performed across three seasons from October 2015-16, in which participants were queried on the number, duration, and characteristics of contacts that occurred on the previous day. A total of 8,421 responses from 3,052 respondents (49% females) recorded characteristics of 180,073 contacts. Respondents reported a significantly higher number and duration of contacts in the winter, followed by the summer and the monsoon season (Nemenyi post-hoc, p<0.001). Participants aged 0-9 years and 10-19 years of age reported the highest median number of contacts (16 (IQR 12-21), 17 (IQR 13-24) respectively) and were found to have the highest node centrality in the social network of the region (pageranks = 0.20, 0.17). A large proportion (>80%) of contacts that were reported in schools or on public transport involved physical contact. To the best of our knowledge, our study is the first from India to show that contact mixing patterns vary by the time of the year and provides useful implications for pandemic control. We compared the differences in the number, duration and location of contacts by age-group and gender, and studied the impact of the season, age-group, employment and day of the week on the number and duration of contacts using multivariate negative binomial regression. We created a social network to further understand the age and gender-specific contact patterns, and used the contact matrices in each season to parameterise a nine-compartment agent-based model for simulating a COVID-19 epidemic in each season. Our results can be used to parameterize more accurate mathematical models for prediction of epidemiological trends of infections in rural India.
Collapse
Affiliation(s)
| | - Rakesh Kumar
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States of America
| | | | | | - Mudita Gosain
- All India Institute of Medical Sciences, New Delhi, India
| | | | | | - Anand Krishnan
- All India Institute of Medical Sciences, New Delhi, India
| |
Collapse
|
5
|
Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Briones HGM, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Joaquim OA, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299472. [PMID: 38105989 PMCID: PMC10723497 DOI: 10.1101/2023.12.05.23299472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
Collapse
Affiliation(s)
| | | | - Kristin Nelson
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic – Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | | | | | | | - Gifta John
- Christian Medical College Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Georgia, USA
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Sara Kim
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Holin Chen
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | - Inci Yildirim
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| |
Collapse
|
6
|
Luka MM, Otieno JR, Kamau E, Morobe JM, Murunga N, Adema I, Nyiro JU, Macharia PM, Bigogo G, Otieno NA, Nyawanda BO, Rabaa MA, Emukule GO, Onyango C, Munywoki PK, Agoti CN, Nokes DJ. Rhinovirus dynamics across different social structures. NPJ VIRUSES 2023; 1:6. [PMID: 38665239 PMCID: PMC11041716 DOI: 10.1038/s44298-023-00008-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/30/2023] [Indexed: 04/28/2024]
Abstract
Rhinoviruses (RV), common human respiratory viruses, exhibit significant antigenic diversity, yet their dynamics across distinct social structures remain poorly understood. Our study delves into RV dynamics within Kenya by analysing VP4/2 sequences across four different social structures: households, a public primary school, outpatient clinics in the Kilifi Health and Demographics Surveillance System (HDSS), and countrywide hospital admissions and outpatients. The study revealed the greatest diversity of RV infections at the countrywide level (114 types), followed by the Kilifi HDSS (78 types), the school (47 types), and households (40 types), cumulatively representing >90% of all known RV types. Notably, RV diversity correlated directly with the size of the population under observation, and several RV type variants occasionally fuelled RV infection waves. Our findings highlight the critical role of social structures in shaping RV dynamics, information that can be leveraged to enhance public health strategies. Future research should incorporate whole-genome analysis to understand fine-scale evolution across various social structures.
Collapse
Affiliation(s)
- Martha M. Luka
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
- Department of Biochemistry and Biotechnology, Pwani University, Kilifi, Kenya
- Present Address: School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK
| | - James R. Otieno
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - Everlyn Kamau
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - John Mwita Morobe
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - Nickson Murunga
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - Irene Adema
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - Joyce Uchi Nyiro
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
| | - Peter M. Macharia
- Population & Health Impact Surveillance Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
- Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | | | | | | | - Maia A. Rabaa
- Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD), U.S. Centers of Disease Control and Prevention (CDC), Atlanta, GA USA
| | - Gideon O. Emukule
- U.S. Centers of Disease Control and Prevention (CDC), Nairobi, Kenya
| | - Clayton Onyango
- U.S. Centers of Disease Control and Prevention (CDC), Nairobi, Kenya
| | - Patrick K. Munywoki
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
- U.S. Centers of Disease Control and Prevention (CDC), Nairobi, Kenya
| | - Charles N. Agoti
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
- Department of Public Health, Pwani University, Kilifi, Kenya
| | - D. James Nokes
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research – Coast, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| |
Collapse
|
7
|
Gallagher KE, Nyiro J, Agoti CN, Maitha E, Nyagwange J, Karani A, Bottomley C, Murunga N, Githinji G, Mutunga M, Ochola‐Oyier LI, Kombe I, Nyaguara A, Kagucia EW, Warimwe G, Agweyu A, Tsofa B, Bejon P, Scott JAG, Nokes DJ. Symptom prevalence and secondary attack rate of SARS-CoV-2 in rural Kenyan households: A prospective cohort study. Influenza Other Respir Viruses 2023; 17:e13185. [PMID: 37752066 PMCID: PMC10522480 DOI: 10.1111/irv.13185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND We estimated the secondary attack rate of SARS-CoV-2 among household contacts of PCR-confirmed cases of COVID-19 in rural Kenya and analysed risk factors for transmission. METHODS We enrolled incident PCR-confirmed cases and their household members. At baseline, a questionnaire, a blood sample, and naso-oropharyngeal swabs were collected. Household members were followed 4, 7, 10, 14, 21 and 28 days after the date of the first PCR-positive in the household; naso-oropharyngeal swabs were collected at each visit and used to define secondary cases. Blood samples were collected every 1-2 weeks. Symptoms were collected in a daily symptom diary. We used binomial regression to estimate secondary attack rates and survival analysis to analyse risk factors for transmission. RESULTS A total of 119 households with at least one positive household member were enrolled between October 2020 and September 2022, comprising 503 household members; 226 remained in follow-up at day 14 (45%). A total of 43 secondary cases arose within 14 days of identification of the primary case, and 81 household members remained negative. The 7-day secondary attack rate was 4% (95% CI 1%-10%), the 14-day secondary attack rate was 28% (95% CI 17%-40%). Of 38 secondary cases with data, eight reported symptoms (21%, 95% CI 8%-34%). Antibody to SARS-CoV-2 spike protein at enrolment was not associated with risk of becoming a secondary case. CONCLUSION Households in our setting experienced a lower 7-day attack rate than a recent meta-analysis indicated as the global average (23%-43% depending on variant), and infection is mostly asymptomatic in our setting.
Collapse
Affiliation(s)
- Katherine E. Gallagher
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- Department of Infectious Diseases EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
| | - Joyce Nyiro
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | | | - Eric Maitha
- Ministry of HealthGovernment of KenyaNairobiKenya
| | | | - Angela Karani
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | - Christian Bottomley
- Department of Infectious Diseases EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
| | | | | | - Martin Mutunga
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | | | - Ivy Kombe
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | - Amek Nyaguara
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | | | - George Warimwe
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- Nuffield Department of MedicineOxford UniversityOxfordUK
| | - Ambrose Agweyu
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- Department of Infectious Diseases EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
| | - Benjamin Tsofa
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
| | - Philip Bejon
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- Nuffield Department of MedicineOxford UniversityOxfordUK
| | - J. Anthony G. Scott
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- Department of Infectious Diseases EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
- Nuffield Department of MedicineOxford UniversityOxfordUK
| | - David James Nokes
- KEMRI‐Wellcome Trust Research Programme (KWTRP)KilifiKenya
- School of Life Sciences and the Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER)University of WarwickCoventryUK
| |
Collapse
|
8
|
Gómez-Pérez GP, de Groot R, Abajobir AA, Wainaina CW, Rinke de Wit TF, Sidze E, Pradhan M, Janssens W. Reduced incidence of respiratory, gastrointestinal and malaria infections among children during the COVID-19 pandemic in Western Kenya: An analysis of facility-based and weekly diaries data. J Glob Health 2023; 13:06024. [PMID: 37448326 DOI: 10.7189/jogh.13.06024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023] Open
Abstract
Background Epidemics can cause significant disruptions of essential health care services. This was evident in West-Africa during the 2014-2016 Ebola outbreak, raising concerns that COVID-19 would have similar devastating consequences for the continent. Indeed, official facility-based records show a reduction in health care visits after the onset of COVID-19 in Kenya. Our question is whether this observed reduction was caused by lower access to health care or by reduced incidence of communicable diseases resulting from reduced mobility and social contacts. Methods We analysed monthly facility-based data from 2018 to 2020, and weekly health diaries data digitally collected by trained fieldworkers between February and November 2020 from 342 households, including 1974 individuals, in Kisumu and Kakamega Counties, Kenya. Diaries data was collected as part of an ongoing longitudinal study of a digital health insurance scheme (Kakamega), and universal health coverage implementation (Kisumu). We assessed the weekly incidence of self-reported medical symptoms, formal and informal health-seeking behaviour, and foregone care in the diaries and compared it with facility-based records. Linear probability regressions with household fixed-effects were performed to compare the weekly incidence of health outcomes before and after COVID-19. Results Facility-based data showed a decrease in health care utilization for respiratory infections, enteric illnesses, and malaria, after start of COVID-19 measures in Kenya in March 2020. The weekly diaries confirmed this decrease in respiratory and enteric symptoms, and malaria / fever, mainly in the paediatric population. In terms of health care seeking behaviour, our diaries data find a temporary shift in consultations from health care centres to pharmacists / chemists / medicine vendors for a few weeks during the pandemic, but no increase in foregone care. According to the diaries, for adults the incidence of communicable diseases/symptoms rebounded after COVID-19 mobility restrictions were lifted, while for children the effects persisted. Conclusions COVID-19-related containment measures in Western Kenya were accompanied by a decline in respiratory infections, enteric illnesses, and malaria / fever mainly in children. Data from a population-based survey and facility-based records aligned regarding this finding despite the temporary shift to non-facility-based consultations and confirmed that the drop in utilization of health care services was not due to decreased accessibility, but rather to a lower incidence of these infections.
Collapse
Affiliation(s)
- Gloria P Gómez-Pérez
- Amsterdam Institute of Global Health and Development, Amsterdam, the Netherlands
- PharmAccess Foundation, Amsterdam, the Netherlands
| | - Richard de Groot
- Amsterdam Institute of Global Health and Development, Amsterdam, the Netherlands
| | | | - Caroline W Wainaina
- African Population and Health Research Centre, Nairobi, Kenya
- Universiteit Utrecht, Amsterdam, the Netherlands
| | - Tobias F Rinke de Wit
- Amsterdam Institute of Global Health and Development, Amsterdam, the Netherlands
- PharmAccess Foundation, Amsterdam, the Netherlands
| | - Estelle Sidze
- African Population and Health Research Centre, Nairobi, Kenya
| | - Menno Pradhan
- Amsterdam Institute of Global Health and Development, Amsterdam, the Netherlands
- Vrije Universiteit, Amsterdam, the Netherlands
- Universiteit van Amsterdam, the Netherlands
| | - Wendy Janssens
- Amsterdam Institute of Global Health and Development, Amsterdam, the Netherlands
- Vrije Universiteit, Amsterdam, the Netherlands
| |
Collapse
|
9
|
Leung WTM, Meeyai A, Holt HR, Khieu B, Chhay T, Seng S, Pok S, Chiv P, Drake T, Rudge JW. Social contact patterns relevant for infectious disease transmission in Cambodia. Sci Rep 2023; 13:5542. [PMID: 37015945 PMCID: PMC10072808 DOI: 10.1038/s41598-023-31485-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 03/13/2023] [Indexed: 04/06/2023] Open
Abstract
Social mixing patterns are key determinants of infectious disease transmission. Mathematical models parameterised with empirical data from contact pattern surveys have played an important role in understanding epidemic dynamics and informing control strategies, including for SARS-CoV-2. However, there is a paucity of data on social mixing patterns in many settings. We conducted a community-based survey in Cambodia in 2012 to characterise mixing patterns and generate setting-specific contact matrices according to age and urban/rural populations. Data were collected using a diary-based approach from 2016 participants, selected by stratified random sampling. Contact patterns were highly age-assortative, with clear intergenerational mixing between household members. Both home and school were high-intensity contact settings, with 27.7% of contacts occurring at home with non-household members. Social mixing patterns differed between rural and urban residents; rural participants tended to have more intergenerational mixing, and a higher number of contacts outside of home, work or school. Participants had low spatial mobility, with 88% of contacts occurring within 1 km of the participants' homes. These data broaden the evidence-base on social mixing patterns in low and middle-income countries and Southeast Asia, and highlight within-country heterogeneities which may be important to consider when modelling the dynamics of pathogens transmitted via close contact.
Collapse
Affiliation(s)
- William T M Leung
- Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK
| | - Aronrag Meeyai
- Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK
- Department of Epidemiology, Faculty of Mahidol Public Health, Mahidol University, Bangkok, 10400, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LG, UK
| | - Hannah R Holt
- Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
| | - Borin Khieu
- Livestock Development for Community Livelihood Organization, St 181, Phnom Penh, Cambodia
| | - Ty Chhay
- Livestock Development for Community Livelihood Organization, St 181, Phnom Penh, Cambodia
| | - Sokeyra Seng
- Livestock Development for Community Livelihood Organization, St 181, Phnom Penh, Cambodia
| | - Samkol Pok
- Livestock Development for Community Livelihood Organization, St 181, Phnom Penh, Cambodia
- National Institute of Science, Technology and Innovation, Ministry of Industry, Science, Technology and Innovation, National Road 2, Phnom Penh, Cambodia
| | - Phiny Chiv
- Livestock Development for Community Livelihood Organization, St 181, Phnom Penh, Cambodia
| | - Tom Drake
- Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK
| | - James W Rudge
- Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK
- Department of Epidemiology, Faculty of Mahidol Public Health, Mahidol University, Bangkok, 10400, Thailand
| |
Collapse
|
10
|
Shi C, Wang X, Ye S, Deng S, Cong B, Lu B, Li Y. Understanding the risk of transmission of respiratory viral infections in childcare centres: protocol for the DISeases TrANsmission in ChildcarE (DISTANCE) multicentre cohort study. BMJ Open Respir Res 2023; 10:e001617. [PMID: 37028911 PMCID: PMC10083867 DOI: 10.1136/bmjresp-2023-001617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
INTRODUCTION Childcare centre is considered a high-risk setting for transmission of respiratory viruses. Further evidence is needed to understand the risk of transmission in childcare centres. To this end, we established the DISeases TrANsmission in ChildcarE (DISTANCE) study to understand the interaction among contact patterns, detection of respiratory viruses from environment samples and transmission of viral infections in childcare centres. METHODS AND ANALYSIS The DISTANCE study is a prospective cohort study in multiple childcare centres of Jiangsu Province, China. Study subjects will be childcare attendees and teaching staff of different grades. A range of information will be collected from the study subjects and participating childcare centres, including attendance, contact behaviours (collected by onsite observers), respiratory viral infection (weekly respiratory throat swabs tested by multiplex PCR), presence of respiratory viruses on touch surfaces of childcare centres and weekly follow-up survey on respiratory symptoms and healthcare seeking among subjects tested positive for any respiratory viruses. Detection patterns of respiratory viruses from study subjects and environment samples, contact patterns, and transmission risk will be analysed by developing statistical and mathematical models as appropriate. The study has been initiated in September 2022 in 1 childcare centre in Wuxi City, with a total of 104 children and 12 teaching staff included in the cohort; data collection and follow-up is ongoing. One more childcare centre in Nanjing City (anticipated to include 100 children and 10 teaching staff) will start recruitment in 2023. ETHICS AND DISSEMINATION The study has received ethics approval from Nanjing Medical University Ethics Committee (No. 2022-936) and ethics approval from Wuxi Center for Disease Control and Prevention Ethics Committee (No. 2022-011). We plan to disseminate the study findings mainly through publications in peer-reviewed journals and presentations in academic conferences. Aggregated research data will be shared freely to researchers.
Collapse
Affiliation(s)
- Chao Shi
- Department of Disease Control, Wuxi Centre for Disease Control and Prevention, Wuxi, Jiangsu, China
| | - Xin Wang
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sheng Ye
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shuyu Deng
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Bingbing Cong
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Bing Lu
- Department of Disease Control, Wuxi Centre for Disease Control and Prevention, Wuxi, Jiangsu, China
| | - You Li
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| |
Collapse
|
11
|
Waterlow NR, Radhakrishnan S, Dawa J, van Leeuwen E, Procter SR, Lambach P, Bresee J, Mazur M, Eggo RM, Jit M. Potential health and economic impact of paediatric vaccination using next-generation influenza vaccines in Kenya: a modelling study. BMC Med 2023; 21:106. [PMID: 36949456 PMCID: PMC10032252 DOI: 10.1186/s12916-023-02830-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Influenza is a major year-round cause of respiratory illness in Kenya, particularly in children under 5. Current influenza vaccines result in short-term, strain-specific immunity and were found in a previous study not to be cost-effective in Kenya. However, next-generation vaccines are in development that may have a greater impact and cost-effectiveness profile. METHODS We expanded a model previously used to evaluate the cost-effectiveness of seasonal influenza vaccines in Kenya to include next-generation vaccines by allowing for enhanced vaccine characteristics and multi-annual immunity. We specifically examined vaccinating children under 5 years of age with improved vaccines, evaluating vaccines with combinations of increased vaccine effectiveness, cross-protection between strains (breadth) and duration of immunity. We evaluated cost-effectiveness using incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefits (INMBs) for a range of values for the willingness-to-pay (WTP) per DALY averted. Finally, we estimated threshold per-dose vaccine prices at which vaccination becomes cost-effective. RESULTS Next-generation vaccines can be cost-effective, dependent on the vaccine characteristics and assumed WTP thresholds. Universal vaccines (assumed to provide long-term and broad immunity) are most cost-effective in Kenya across three of four WTP thresholds evaluated, with the lowest median value of ICER per DALY averted ($263, 95% Credible Interval (CrI): $ - 1698, $1061) and the highest median INMBs. At a WTP of $623, universal vaccines are cost-effective at or below a median price of $5.16 per dose (95% CrI: $0.94, $18.57). We also show that the assumed mechanism underlying infection-derived immunity strongly impacts vaccine outcomes. CONCLUSIONS This evaluation provides evidence for country-level decision makers about future next-generation vaccine introduction, as well as global research funders about the potential market for these vaccines. Next-generation vaccines may offer a cost-effective intervention to reduce influenza burden in low-income countries with year-round seasonality like Kenya.
Collapse
Affiliation(s)
- Naomi R Waterlow
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK.
| | - Sreejith Radhakrishnan
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G61 1QH, UK
| | - Jeanette Dawa
- Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Washington State University - Global Health Kenya, Nairobi, Kenya
| | - Edwin van Leeuwen
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
- Statistics, Modelling and Economics Department, UK Health Security Agency, London, NW9 5EQ, UK
| | - Simon R Procter
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
| | - Philipp Lambach
- Immunization Vaccines and Biologicals Department, World Health Organization, Geneva, Switzerland
| | | | | | - Rosalind M Eggo
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
| | - Mark Jit
- Centre for Mathematical Modeling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC14 7HT, UK
| |
Collapse
|
12
|
Ofori SK, Schwind JS, Sullivan KL, Chowell G, Cowling BJ, Fung ICH. Age-Stratified Model to Assess Health Outcomes of COVID-19 Vaccination Strategies, Ghana. Emerg Infect Dis 2023; 29:360-370. [PMID: 36626878 PMCID: PMC9881782 DOI: 10.3201/eid2902.221098] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
We assessed the effect of various COVID-19 vaccination strategies on health outcomes in Ghana by using an age-stratified compartmental model. We stratified the population into 3 age groups: <25 years, 25-64 years, and ≥65 years. We explored 5 vaccination optimization scenarios using 2 contact matrices, assuming that 1 million persons could be vaccinated in either 3 or 6 months. We assessed these vaccine optimization strategies for the initial strain, followed by a sensitivity analysis for the Delta variant. We found that vaccinating persons <25 years of age was associated with the lowest cumulative infections for the main matrix, for both the initial strain and the Delta variant. Prioritizing the elderly (≥65 years of age) was associated with the lowest cumulative deaths for both strains in all scenarios. The consensus between the findings of both contact matrices depended on the vaccine rollout period and the objective of the vaccination program.
Collapse
|
13
|
Nixon E, Silvonen T, Barreaux A, Kwiatkowska R, Trickey A, Thomas A, Ali B, Treneman-Evans G, Christensen H, Brooks-Pollock E, Denford S. A mixed methods analysis of participation in a social contact survey. Epidemics 2022; 41:100635. [PMID: 36182804 PMCID: PMC7615368 DOI: 10.1016/j.epidem.2022.100635] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data. METHODS We conducted focus groups with university students who had (n = 13) and had never (n = 14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data. RESULTS The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation. CONCLUSIONS Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy.
Collapse
Affiliation(s)
- Emily Nixon
- School of Biological Sciences, University of Bristol, Bristol, UK; School of Population Health Sciences, University of Bristol, Bristol, UK; Department of Mathematical Sciences, University of Liverpool, Liverpool, UK.
| | - Taru Silvonen
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Antoine Barreaux
- Bristol Veterinary School, University of Bristol, Bristol, UK; INTERTRYP (Univ. Montpellier, CIRAD, IRD), Montpellier, France
| | - Rachel Kwiatkowska
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Adam Trickey
- School of Population Health Sciences, University of Bristol, Bristol, UK
| | - Amy Thomas
- School of Population Health Sciences, University of Bristol, Bristol, UK
| | - Becky Ali
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Georgia Treneman-Evans
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Hannah Christensen
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Ellen Brooks-Pollock
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Sarah Denford
- School of Population Health Sciences, University of Bristol, Bristol, UK; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| |
Collapse
|
14
|
Dobreva Z, Gimma A, Rohan H, Djoudalbaye B, Tshangela A, Jarvis CI, van Zandvoort K, Quaife M. Characterising social contacts under COVID-19 control measures in Africa. BMC Med 2022; 20:344. [PMID: 36221094 PMCID: PMC9553295 DOI: 10.1186/s12916-022-02543-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Early in the COVID-19 pandemic, countries adopted non-pharmaceutical interventions (NPIs) such as lockdowns to limit SARS-CoV-2 transmission. Social contact studies help measure the effectiveness of NPIs and estimate parameters for modelling SARS-CoV-2 transmission. However, few contact studies have been conducted in Africa. METHODS We analysed nationally representative cross-sectional survey data from 19 African Union Member States, collected by the Partnership for Evidence-based Responses to COVID-19 (PERC) via telephone interviews at two time points (August 2020 and February 2021). Adult respondents reported contacts made in the previous day by age group, demographic characteristics, and their attitudes towards COVID-19. We described mean and median contacts across these characteristics and related contacts to Google Mobility reports and the Oxford Government Response Stringency Index for each country at the two time points. RESULTS Mean reported contacts varied across countries with the lowest reported in Ethiopia (9, SD=16, median = 4, IQR = 8) in August 2020 and the highest in Sudan (50, SD=53, median = 33, IQR = 40) in February 2021. Contacts of people aged 18-55 represented 50% of total contacts, with most contacts in household and work or study settings for both surveys. Mean contacts increased for Ethiopia, Ghana, Liberia, Nigeria, Sudan, and Uganda and decreased for Cameroon, the Democratic Republic of Congo (DRC), and Tunisia between the two time points. Men had more contacts than women and contacts were consistent across urban or rural settings (except in Cameroon and Kenya, where urban respondents had more contacts than rural ones, and in Senegal and Zambia, where the opposite was the case). There were no strong and consistent variations in the number of mean or median contacts by education level, self-reported health, perceived self-reported risk of infection, vaccine acceptance, mask ownership, and perceived risk of COVID-19 to health. Mean contacts were correlated with Google mobility (coefficient 0.57, p=0.051 and coefficient 0.28, p=0.291 in August 2020 and February 2021, respectively) and Stringency Index (coefficient -0.12, p = 0.304 and coefficient -0.33, p=0.005 in August 2020 and February 2021, respectively). CONCLUSIONS These are the first COVID-19 social contact data collected for 16 of the 19 countries surveyed. We find a high reported number of daily contacts in all countries and substantial variations in mean contacts across countries and by gender. Increased stringency and decreased mobility were associated with a reduction in the number of contacts. These data may be useful to understand transmission patterns, model infection transmission, and for pandemic planning.
Collapse
Affiliation(s)
- Zlatina Dobreva
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
| | - Amy Gimma
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Hana Rohan
- UK Public Health Rapid Support Team, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Akhona Tshangela
- Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia
| | - Christopher I Jarvis
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kevin van Zandvoort
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Matthew Quaife
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
15
|
Goldsmith JJ, Campbell PT, Villanueva-Cabezas JP, Chisholm RH, McKinnon M, Gurruwiwi GG, Dhurrkay RG, Dockery AM, Geard N, Tong SYC, McVernon J, Gibney KB. Capturing Household Structure and Mobility within and between Remote Aboriginal Communities in Northern Australia Using Longitudinal Data: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12002. [PMID: 36231301 PMCID: PMC9566160 DOI: 10.3390/ijerph191912002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/13/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
UNLABELLED Cultural practices and development level can influence a population's household structures and mixing patterns. Within some populations, households can be organized across multiple dwellings. This likely affects the spread of infectious disease through these communities; however, current demographic data collection tools do not record these data. METHODS Between June and October 2018, the Contact And Mobility Patterns in remote Aboriginal Australian communities (CAMP-remote) pilot study recruited Aboriginal mothers with infants in a remote northern Australian community to complete a monthly iPad-based contact survey. RESULTS Thirteen mother-infant pairs (participants) completed 69 study visits between recruitment and the end of May 2019. Participants reported they and their other children slept in 28 dwellings during the study. The median dwelling occupancy, defined as people sleeping in the same dwelling on the previous night, was ten (range: 3.5-25). Participants who completed at least three responses (n = 8) slept in a median of three dwellings (range: 2-9). Each month, a median of 28% (range: 0-63%) of the participants travelled out of the community. Including these data in disease transmission models amplified estimates of infectious disease spread in the study community, compared to models parameterized using census data. CONCLUSIONS The lack of data on mixing patterns in populations where households can be organized across dwellings may impact the accuracy of infectious disease models for these communities and the efficacy of public health actions they inform.
Collapse
Affiliation(s)
- Jessie J. Goldsmith
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Patricia T. Campbell
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia
| | - Juan Pablo Villanueva-Cabezas
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Rebecca H. Chisholm
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Melita McKinnon
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, Casuarina, NT 0811, Australia
| | - George G. Gurruwiwi
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, Casuarina, NT 0811, Australia
| | - Roslyn G. Dhurrkay
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, Casuarina, NT 0811, Australia
| | - Alfred M. Dockery
- Bankwest Curtin Economics Centre, Curtin University, Bentley, WA 6102, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Steven Y. C. Tong
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Victorian Infectious Diseases Service, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3050, Australia
| | - Jodie McVernon
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Katherine B. Gibney
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Victorian Infectious Diseases Service, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3050, Australia
| |
Collapse
|
16
|
Thindwa D, Jambo KC, Ojal J, MacPherson P, Dennis Phiri M, Pinsent A, Khundi M, Chiume L, Gallagher KE, Heyderman RS, Corbett EL, French N, Flasche S. Social mixing patterns relevant to infectious diseases spread by close contact in urban Blantyre, Malawi. Epidemics 2022; 40:100590. [PMID: 35691100 PMCID: PMC9176177 DOI: 10.1016/j.epidem.2022.100590] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/08/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION Understanding human mixing patterns relevant to infectious diseases spread through close contact is vital for modelling transmission dynamics and optimisation of disease control strategies. Mixing patterns in low-income countries like Malawi are not well known. METHODOLOGY We conducted a social mixing survey in urban Blantyre, Malawi between April and July 2021 (between the 2nd and 3rd wave of COVID-19 infections). Participants living in densely-populated neighbourhoods were randomly sampled and, if they consented, reported their physical and non-physical contacts within and outside homes lasting at least 5 min during the previous day. Age-specific mixing rates were calculated, and a negative binomial mixed effects model was used to estimate determinants of contact behaviour. RESULTS Of 1201 individuals enroled, 702 (58.5%) were female, the median age was 15 years (interquartile range [IQR] 5-32) and 127 (10.6%) were HIV-positive. On average, participants reported 10.3 contacts per day (range: 1-25). Mixing patterns were highly age-assortative, particularly those within the community and with skin-to-skin contact. Adults aged 20-49 y reported the most contacts (median:11, IQR: 8-15) of all age groups; 38% (95%CI: 16-63) more than infants (median: 8, IQR: 5-10), who had the least contacts. Household contact frequency increased by 3% (95%CI: 2-5) per additional household member. Unemployed participants had 15% (95%CI: 9-21) fewer contacts than other adults. Among long range (>30 m away from home) contacts, secondary school children had the largest median contact distance from home (257 m, IQR 78-761). HIV-positive status in adults >=18 years-old was not associated with changed contact patterns (rate ratio: 1.01, 95%CI: (0.91-1.12)). During this period of relatively low COVID-19 incidence in Malawi, 301 (25.1%) individuals stated that they had limited their contact with others due to COVID-19 precautions; however, their reported contacts were 8% (95%CI: 1-13) higher. CONCLUSION In urban Malawi, contact rates, are high and age-assortative, with little reported behavioural change due to either HIV-status or COVID-19 circulation. This highlights the limits of contact-restriction-based mitigation strategies in such settings and the need for pandemic preparedness to better understand how contact reductions can be enabled and motivated.
Collapse
Affiliation(s)
- Deus Thindwa
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi.
| | - Kondwani C Jambo
- Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - John Ojal
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; KEMRI-Wellcome Research Programme, Geographic Medicine Centre, Kilifi, Kenya
| | - Peter MacPherson
- Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK
| | - Mphatso Dennis Phiri
- Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | | | - McEwen Khundi
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi
| | - Lingstone Chiume
- Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi
| | - Katherine E Gallagher
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; KEMRI-Wellcome Research Programme, Geographic Medicine Centre, Kilifi, Kenya
| | - Robert S Heyderman
- NIHR Global Health Research Unit on Mucosal Pathogens, Research Department of Infection, Division of Infection and Immunity, University College London, London, UK
| | - Elizabeth L Corbett
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK
| | - Neil French
- Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, UK
| | - Stefan Flasche
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| |
Collapse
|
17
|
Weerasuriya CK, Harris RC, McQuaid CF, Gomez GB, White RG. Updating age-specific contact structures to match evolving demography in a dynamic mathematical model of tuberculosis vaccination. PLoS Comput Biol 2022; 18:e1010002. [PMID: 35452459 PMCID: PMC9067655 DOI: 10.1371/journal.pcbi.1010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 05/04/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
We investigated the effects of updating age-specific social contact matrices to match evolving demography on vaccine impact estimates. We used a dynamic transmission model of tuberculosis in India as a case study. We modelled four incremental methods to update contact matrices over time, where each method incorporated its predecessor: fixed contact matrix (M0), preserved contact reciprocity (M1), preserved contact assortativity (M2), and preserved average contacts per individual (M3). We updated the contact matrices of a deterministic compartmental model of tuberculosis transmission, calibrated to epidemiologic data between 2000 and 2019 derived from India. We additionally calibrated the M0, M2, and M3 models to the 2050 TB incidence rate projected by the calibrated M1 model. We stratified age into three groups, children (<15y), adults (≥15y, <65y), and the elderly (≥65y), using World Population Prospects demographic data, between which we applied POLYMOD-derived social contact matrices. We simulated an M72-AS01E-like tuberculosis vaccine delivered from 2027 and estimated the per cent TB incidence rate reduction (IRR) in 2050 under each update method. We found that vaccine impact estimates in all age groups remained relatively stable between the M0–M3 models, irrespective of vaccine-targeting by age group. The maximum difference in impact, observed following adult-targeted vaccination, was 7% in the elderly, in whom we observed IRRs of 19% (uncertainty range 13–32), 20% (UR 13–31), 22% (UR 14–37), and 26% (UR 18–38) following M0, M1, M2 and M3 updates, respectively. We found that model-based TB vaccine impact estimates were relatively insensitive to demography-matched contact matrix updates in an India-like demographic and epidemiologic scenario. Current model-based TB vaccine impact estimates may be reasonably robust to the lack of contact matrix updates, but further research is needed to confirm and generalise this finding. Mathematical models are increasingly used to predict the impact of new and existing tools, e.g., vaccines, that aim to control the transmission of infectious diseases. Within these models, investigators often assume that individuals contact each other according to specific patterns, particularly between and within different age groups. These patterns are typically derived from surveys of social contact or other models and reflect the particular age composition of their source population. However, when models are set over long time scales, e.g., decades, population age composition is likely to change. Despite this reality, few models update their contact patterns to match changing age composition. Furthermore, none have assessed whether their final estimates of disease-control intervention impact are affected by updating contact patterns. We measured whether different techniques to update social contact patterns to match evolving demography produce different vaccine impact estimates, using a mathematical model of tuberculosis set in an India-like scenario between 2025–2050. We found that vaccine impact was stable across a range of different update methods. Thus, existing model-based vaccine impact estimates may be stable to a lack of these updates, but further work is required to confirm these findings.
Collapse
Affiliation(s)
- Chathika Krishan Weerasuriya
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- * E-mail:
| | - Rebecca Claire Harris
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher Finn McQuaid
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Gabriela B. Gomez
- Department of Global Health & Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Richard G. White
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| |
Collapse
|
18
|
Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys. Sci Rep 2022; 12:4690. [PMID: 35304478 PMCID: PMC8931780 DOI: 10.1038/s41598-022-07488-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 02/01/2022] [Indexed: 12/02/2022] Open
Abstract
The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over [Formula: see text] of the Hungarian population to simultaneously record contact matrices through a longitudinal online and sequence of representative phone surveys. To correct non-representative biases characterising the online data, by using census data and the representative samples we develop a reconstruction method to provide a scalable, cheap, and flexible way to dynamically obtain closer-to-representative contact matrices. Our results demonstrate that although some conventional socio-demographic characters correlate significantly with the change of contact numbers, the strongest predictors can be collected only via surveys techniques and combined with census data for the best reconstruction performance. We demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and to inform epidemic models with crucial data.
Collapse
|
19
|
Rocha Filho TM, Mendes JFF, Murari TB, Nascimento Filho AS, Cordeiro AJA, Ramalho WM, Scorza FA, Almeida ACG, Moret MA. Optimization of COVID-19 vaccination and the role of individuals with a high number of contacts: A model based approach. PLoS One 2022; 17:e0262433. [PMID: 35259169 PMCID: PMC8903293 DOI: 10.1371/journal.pone.0262433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/24/2021] [Indexed: 01/24/2023] Open
Abstract
We report strong evidence of the importance of contact hubs (or superspreaders) in mitigating the current COVID-19 pandemic. Contact hubs have a much larger number of contacts than the average in the population, and play a key role on the effectiveness of vaccination strategies. By using an age-structures compartmental SEIAHRV (Susceptible, Exposed, Infected symptomatic, Asymptomatic, Hospitalized, Recovered, Vaccinated) model, calibrated from available demographic and COVID-19 incidence, and considering separately those individuals with a much greater number of contacts than the average in the population, we show that carefully choosing who will compose the first group to be vaccinated can impact positively the total death toll and the demand for health services. This is even more relevant in countries with a lack of basic resources for proper vaccination and a significant reduction in social isolation. In order to demonstrate our approach we show the effect of hypothetical vaccination scenarios in two countries of very different scales and mitigation policies, Brazil and Portugal.
Collapse
Affiliation(s)
- Tarcísio M. Rocha Filho
- International Center for Condensed Matter Physics, Universidade de Brasília, Brasília, DF, Brazil
- Instituto de Física, Universidade de Brasília, Brasília, DF, Brazil
- * E-mail:
| | - José F. F. Mendes
- Departamento de Física & I3N, Universidade de Aveiro, Aveiro, Portugal
| | | | | | - Antônio J. A. Cordeiro
- Centro Universitário SENAI CIMATEC, Salvador, BA, Brazil
- Instituto Federal de Educação e Tecnologia da Bahia, Feira de Santana, BA, Brazil
| | - Walter M. Ramalho
- FCE and Núcleo de Medicina Tropical, Universidade de Brasília, Brasília, DF, Brazil
| | - Fúlvio A. Scorza
- Disciplina de Neurociência, Escola Paulista de Medicina/Universidade Federal de São Paulo (EPM/UNIFESP), São Paulo, SP, Brazil
| | | | - Marcelo A. Moret
- Centro Universitário SENAI CIMATEC, Salvador, BA, Brazil
- Universidade do Estado da Bahia, Salvador, BA, Brazil
| |
Collapse
|
20
|
Potter GE, Carnegie NB, Sugimoto JD, Diallo A, Victor JC, Neuzil KM, Halloran ME. Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal. J R Stat Soc Ser C Appl Stat 2022; 71:70-90. [PMID: 35721226 PMCID: PMC9202735 DOI: 10.1111/rssc.12522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between clusters. Our time-varying estimates reveal a reduction in seasonal influenza from the intervention and a nonsignificant increase in H1N1 pandemic influenza. We estimate an additive change in overall cumulative incidence (which was 6.13% in the control arm) of -0.68 percentage points during Year 1 of the study (95% CI: -2.53, 1.18). When H1N1 pandemic infections were excluded from analysis, the estimated change was -1.45 percentage points and was significant (95% CI, -2.81, -0.08). Because cross-cluster contamination was low (0-3% of contacts for most villages), an estimator assuming no contamination was only slightly attenuated (-0.65 percentage points). These findings are encouraging for studies carefully designed to minimize spillover. Further work is needed to estimate contamination - and its effect on estimation - in a variety of settings.
Collapse
Affiliation(s)
- Gail E. Potter
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, and the Emmes Company, Rockville Maryland, USA
| | | | - Jonathan D. Sugimoto
- University of Washington and Epidemiologic Research and Information Center, Veterans Affairs Puget Sound Health Care System and Fred Hutchinson Cancer Research Center, Seattle Washington, USA
| | - Aldiouma Diallo
- Institut de Recherche pour le Développement, Niakhar Senegal
| | | | | | - M. Elizabeth Halloran
- University of Washington Department of Biostatistics and Fred Hutchinson Cancer Research Center, Seattle Washington, USA
| |
Collapse
|
21
|
Tetteh JNA, Nguyen VK, Hernandez-Vargas EA. Network models to evaluate vaccine strategies towards herd immunity in COVID-19. J Theor Biol 2021; 531:110894. [PMID: 34508758 PMCID: PMC8426151 DOI: 10.1016/j.jtbi.2021.110894] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 08/24/2021] [Accepted: 08/31/2021] [Indexed: 11/29/2022]
Abstract
Vaccination remains a critical element in the eventual solution to the COVID-19 public health crisis. Many vaccines are already being mass produced and supplied in many countries. However, the COVID-19 vaccination programme will be the biggest in history. Reaching herd immunity will require an unprecedented mass immunisation campaign that will take several months and millions of dollars. Using different network models, COVID-19 pandemic dynamics of different countries can be recapitulated such as in Italy. Stochastic computational simulations highlight that peak epidemic sizes in a population strongly depend on the network structure. Assuming a vaccine efficacy of at least 80% in a mass vaccination program, at least 70% of a given population should be vaccinated to obtain herd immunity, independently of the network structure. If the vaccine efficacy reports lower levels of efficacy in practice, then the coverage of vaccination would be needed to be even higher. Simulations suggest that the "Ring of Vaccination" strategy, vaccinating susceptible contact and contact of contacts, would prevent new waves of COVID -19 meanwhile a high percent of the population is vaccinated.
Collapse
Affiliation(s)
- Josephine N A Tetteh
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany; Institut für Mathematik, Goethe-Universität, Frankfurt am Main, Germany
| | | | - Esteban A Hernandez-Vargas
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany; Instituto de Matemáticas, Universidad Nacional Autonoma de Mexico, Boulevard Juriquilla 3001, Santiago de Querétaro, Qro. 76230, Mexico.
| |
Collapse
|
22
|
Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, Diallo A, Dodd PJ, Grijalva CG, Kiti MC, Krishnan A, Kumar R, Kumar S, Kwok KO, Lanata CF, le Polain de Waroux O, Leung K, Mahikul W, Melegaro A, Morrow CD, Mossong J, Neal EF, Nokes DJ, Pan-Ngum W, Potter GE, Russell FM, Saha S, Sugimoto JD, Wei WI, Wood RR, Wu J, Zhang J, Walker P, Whittaker C. Social contact patterns and implications for infectious disease transmission: a systematic review and meta-analysis of contact surveys. eLife 2021; 10:70294. [PMID: 34821551 PMCID: PMC8765757 DOI: 10.7554/elife.70294] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions. Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1). Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch. These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions. Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups. In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens. These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.
Collapse
Affiliation(s)
- Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver John Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mélodie Monod
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, United States
| | - Aldiouma Diallo
- VITROME, Institut de Recherche pour le Developpement, Dakar, Senegal
| | - Peter J Dodd
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Carlos G Grijalva
- Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, Nashville, United States
| | | | - Anand Krishnan
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kumar
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States
| | - Kin O Kwok
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | | | | | - Kathy Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wiriya Mahikul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Milano, Italy
| | - Carl D Morrow
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Eleanor Fg Neal
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - D James Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Gail E Potter
- National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville, United States
| | - Fiona M Russell
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - Siddhartha Saha
- US Centers for Disease Control and Prevention, New Delhi, India
| | - Jonathan D Sugimoto
- Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs, Seattle, United States
| | - Wan In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Robin R Wood
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Joseph Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Shanghai, China
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| |
Collapse
|
23
|
Polonsky JA, Böhning D, Keita M, Ahuka-Mundeke S, Nsio-Mbeta J, Abedi AA, Mossoko M, Estill J, Keiser O, Kaiser L, Yoti Z, Sangnawakij P, Lerdsuwansri R, Vilas VJDR. Novel Use of Capture-Recapture Methods to Estimate Completeness of Contact Tracing during an Ebola Outbreak, Democratic Republic of the Congo, 2018-2020. Emerg Infect Dis 2021; 27:3063-3072. [PMID: 34808076 PMCID: PMC8632194 DOI: 10.3201/eid2712.204958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Despite its critical role in containing outbreaks, the efficacy of contact tracing, measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of contact tracing by applying unilist capture-recapture methods on data from the 2018–2020 outbreak of Ebola virus disease in the Democratic Republic of the Congo. To compute sensitivity, we applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved case-patients with any contacts and infected contacts. Geometric distributions were the best-fitting models. Our results indicate that contact tracing efforts identified almost all (n = 792, 99%) of case-patients with any contacts but only half (n = 207, 48%) of case-patients with infected contacts, suggesting that contact tracing efforts performed well at identifying contacts during the listing stage but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the ongoing coronavirus pandemic.
Collapse
|
24
|
Del Fava E, Adema I, Kiti MC, Poletti P, Merler S, Nokes DJ, Manfredi P, Melegaro A. Individual's daily behaviour and intergenerational mixing in different social contexts of Kenya. Sci Rep 2021; 11:21589. [PMID: 34732732 PMCID: PMC8566563 DOI: 10.1038/s41598-021-00799-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
We investigated contact patterns in diverse social contexts in Kenya and the daily behaviours that may play a pivotal role in infection transmission to the most vulnerable leveraging novel data from a 2-day survey on social contacts and time use (TU) from a sample of 1407 individuals (for a total of 2705 person days) from rural, urban formal, and informal settings. We used TU data to build six profiles of daily behaviour based on the main reported activities, i.e., Homestayers (71.1% of person days), Workers (9.3%), Schoolers (7.8%), or locations at increasing distance from home, i.e., Walkers (6.6%), Commuters (4.6%), Travelers (0.6%). In the rural setting, we observed higher daily contact numbers (11.56, SD 0.23) and percentages of intergenerational mixing with older adults (7.5% of contacts reported by those younger than 60 years vs. less than 4% in the urban settings). Overall, intergenerational mixing with older adults was higher for Walkers (7.3% of their reported contacts), Commuters (8.7%), and Homestayers (5.1%) than for Workers (1.5%) or Schoolers (3.6%). These results could be instrumental in defining effective interventions that acknowledge the heterogeneity in social contexts and daily routines, either in Kenya or other demographically and culturally similar sub-Saharan African settings.
Collapse
Affiliation(s)
- Emanuele Del Fava
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
- Max Planck Institute for Demographic Research, Rostock, Germany
| | - Irene Adema
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Moses C Kiti
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | | | - D James Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| | | | - Alessia Melegaro
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.
- Department of Social and Political Sciences, Bocconi University, Milan, Italy.
| |
Collapse
|
25
|
Liu CY, Berlin J, Kiti MC, Del Fava E, Grow A, Zagheni E, Melegaro A, Jenness SM, Omer SB, Lopman B, Nelson K. Rapid Review of Social Contact Patterns During the COVID-19 Pandemic. Epidemiology 2021; 32:781-791. [PMID: 34392254 PMCID: PMC8478104 DOI: 10.1097/ede.0000000000001412] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/02/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Physical distancing measures aim to reduce person-to-person contact, a key driver of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. In response to unprecedented restrictions on human contact during the coronavirus disease 2019 (COVID-19) pandemic, studies measured social contact patterns under the implementation of physical distancing measures. This rapid review synthesizes empirical data on the changing social contact patterns during the COVID-19 pandemic. METHOD We conducted a systematic review using PubMed, Medline, Embase, and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We descriptively compared the distribution of contacts observed during the pandemic to pre-COVID data across countries to explore changes in contact patterns during physical distancing measures. RESULTS We identified 12 studies reporting social contact patterns during the COVID-19 pandemic. Eight studies were conducted in European countries and eleven collected data during the initial mitigation period in the spring of 2020 marked by government-declared lockdowns. Some studies collected additional data after relaxation of initial mitigation. Most study settings reported a mean of between 2 and 5 contacts per person per day, a substantial reduction compared to pre-COVID rates, which ranged from 7 to 26 contacts per day. This reduction was pronounced for contacts outside of the home. Consequently, levels of assortative mixing by age substantially declined. After relaxation of initial mitigation, mean contact rates increased but did not return to pre-COVID levels. Increases in contacts post-relaxation were driven by working-age adults. CONCLUSION Information on changes in contact patterns during physical distancing measures can guide more realistic representations of contact patterns in mathematical models for SARS-CoV-2 transmission.
Collapse
Affiliation(s)
- Carol Y. Liu
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Juliette Berlin
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Moses C. Kiti
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Emanuele Del Fava
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
| | - André Grow
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
| | - Emilio Zagheni
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
| | - Alessia Melegaro
- Department of Social and Political Sciences, Centre for Research on Social Dynamics and Public Policy and Covid Crisis Lab, Bocconi University, Milan, Italy
| | - Samuel M. Jenness
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Saad B. Omer
- Department of Epidemiology of Microbial Diseases, Yale Institute of Global Health, Yale University, CT
| | - Benjamin Lopman
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Kristin Nelson
- From the Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| |
Collapse
|
26
|
McCreesh N, Dlamini V, Edwards A, Olivier S, Dayi N, Dikgale K, Nxumalo S, Dreyer J, Baisley K, Siedner MJ, White RG, Herbst K, Grant AD, Harling G. Impact of the Covid-19 epidemic and related social distancing regulations on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys. BMC Infect Dis 2021; 21:928. [PMID: 34496771 PMCID: PMC8424154 DOI: 10.1186/s12879-021-06604-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 08/23/2021] [Indexed: 12/11/2022] Open
Abstract
Background South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. Methods We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational (‘close interaction’) contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R0). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space (‘shared air’). Results Respondents in March–December 2019 (n = 1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n = 216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others’ homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15–29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R0 fell by 42% (95% plausible range 14–59%) between 2019 and June-July 2020. Conclusions Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted a potential limitation of social distancing measures in protecting older adults. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06604-8.
Collapse
Affiliation(s)
- Nicky McCreesh
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vuyiswa Dlamini
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Anita Edwards
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Njabulo Dayi
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Keabetswe Dikgale
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Siyabonga Nxumalo
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Kathy Baisley
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.,Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Mark J Siedner
- Harvard Medical School and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA
| | - Richard G White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa.,DSI-MRC South African Population Research Infrastructure Network, Durban, South Africa
| | - Alison D Grant
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa.,TB Centre, London School of Hygiene and Tropical Medicine, London, UK.,School of Laboratory and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, Durban, South Africa.,School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Guy Harling
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa. .,Institute for Global Health, University College London, London, UK. .,Department of Epidemiology & Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
| |
Collapse
|
27
|
Wamai RG, Hirsch JL, Van Damme W, Alnwick D, Bailey RC, Hodgins S, Alam U, Anyona M. What Could Explain the Lower COVID-19 Burden in Africa despite Considerable Circulation of the SARS-CoV-2 Virus? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8638. [PMID: 34444386 PMCID: PMC8391172 DOI: 10.3390/ijerph18168638] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/13/2021] [Accepted: 08/13/2021] [Indexed: 01/12/2023]
Abstract
The differential spread and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing Coronavirus Disease 2019 (COVID-19), across regions is a major focus for researchers and policy makers. Africa has attracted tremendous attention, due to predictions of catastrophic impacts that have not yet materialized. Early in the pandemic, the seemingly low African case count was largely attributed to low testing and case reporting. However, there is reason to consider that many African countries attenuated the spread and impacts early on. Factors explaining low spread include early government community-wide actions, population distribution, social contacts, and ecology of human habitation. While recent data from seroprevalence studies posit more extensive circulation of the virus, continuing low COVID-19 burden may be explained by the demographic pyramid, prevalence of pre-existing conditions, trained immunity, genetics, and broader sociocultural dynamics. Though all these prongs contribute to the observed profile of COVID-19 in Africa, some provide stronger evidence than others. This review is important to expand what is known about the differential impacts of pandemics, enhancing scientific understanding and gearing appropriate public health responses. Furthermore, it highlights potential lessons to draw from Africa for global health on assumptions regarding deadly viral pandemics, given its long experience with infectious diseases.
Collapse
Affiliation(s)
- Richard G. Wamai
- Department of Cultures, Societies, and Global Studies, Northeastern University, 201 Renaissance Park, 360 Huntington Ave., Boston, MA 02115, USA;
| | - Jason L. Hirsch
- Department of Cultures, Societies, and Global Studies, Northeastern University, 201 Renaissance Park, 360 Huntington Ave., Boston, MA 02115, USA;
| | - Wim Van Damme
- Department of Public Health, Institute of Tropical Medicine, B-2000 Antwerp, Belgium;
| | - David Alnwick
- DUNDEX (Deployable U.N.-Experienced Development Experts), FX68 Belturbet, Ireland;
| | - Robert C. Bailey
- School of Public Health, University of Illinois at Chicago, Chicago, IL 60607, USA;
| | - Stephen Hodgins
- School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada;
| | - Uzma Alam
- Researcher Africa Institute for Health Policy Foundation, Nairobi 020, Kenya;
| | - Mamka Anyona
- T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA;
| |
Collapse
|
28
|
Prem K, Zandvoort KV, Klepac P, Eggo RM, Davies NG, Cook AR, Jit M. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Comput Biol 2021. [PMID: 34310590 DOI: 10.1101/2020.07.22.20159772] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted.
Collapse
Affiliation(s)
- Kiesha Prem
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Kevin van Zandvoort
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Petra Klepac
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nicholas G Davies
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
29
|
Prem K, van Zandvoort K, Klepac P, Eggo RM, Davies NG, Cook AR, Jit M. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Comput Biol 2021; 17:e1009098. [PMID: 34310590 PMCID: PMC8354454 DOI: 10.1371/journal.pcbi.1009098] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/10/2021] [Accepted: 05/20/2021] [Indexed: 01/08/2023] Open
Abstract
Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted.
Collapse
Affiliation(s)
- Kiesha Prem
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Kevin van Zandvoort
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Petra Klepac
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Rosalind M. Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nicholas G. Davies
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
30
|
Prem K, Zandvoort KV, Klepac P, Eggo RM, Davies NG, Cook AR, Jit M. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Comput Biol 2021; 17:e1009098. [PMID: 34310590 DOI: 10.5281/zenodo.4889500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/10/2021] [Accepted: 05/20/2021] [Indexed: 05/20/2023] Open
Abstract
Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted.
Collapse
Affiliation(s)
- Kiesha Prem
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Kevin van Zandvoort
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Petra Klepac
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nicholas G Davies
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
31
|
Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, Diallo A, Dodd PJ, Grijalva CG, Kiti MC, Krishnan A, Kumar R, Kumar S, Kwok KO, Lanata CF, Le Polain de Waroux O, Leung K, Mahikul W, Melegaro A, Morrow CD, Mossong J, Neal EFG, Nokes DJ, Pan-ngum W, Potter GE, Russell FM, Saha S, Sugimoto JD, Wei WI, Wood RR, Wu JT, Zhang J, Walker PGT, Whittaker C. Social Contact Patterns and Implications for Infectious Disease Transmission: A Systematic Review and Meta-Analysis of Contact Surveys. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.06.10.21258720. [PMID: 34159341 PMCID: PMC8219108 DOI: 10.1101/2021.06.10.21258720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focussed on high-income settings. METHODS Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys we explored how contact characteristics (number, location, duration and whether physical) vary across income settings. RESULTS Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age-groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, but low-income settings were characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income-strata on the frequency, duration and type of contacts individuals made. CONCLUSIONS These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens, as well as the effectiveness of different non-pharmaceutical interventions. FUNDING This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1).
Collapse
Affiliation(s)
- Andria Mousa
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, UK
| | - Mélodie Monod
- Department of Mathematics, Imperial College London, London, UK
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA
| | - Aldiouma Diallo
- VITROME, Institut de Recherche pour le Developpement, Senegal
| | - Peter J Dodd
- School of Health and Related Research, University of Sheffield, UK
| | - Carlos G Grijalva
- Division of Pharmacoepidemiology, Department of Health Policy. Vanderbilt University Medical Center. Nashville, TN, USA
| | | | - Anand Krishnan
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kumar
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | | | - Kin On Kwok
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China
| | - Claudio F Lanata
- Instituto de Investigación Nutricional, Lima, Peru
- Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | | | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, New Territories, Hong Kong SAR, China
| | - Wiriya Mahikul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Carl D Morrow
- Desmond Tutu HIV Centre, Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
- Centre for Infectious Disease Epidemiology and Research (CIDER), School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town South Africa
| | | | - Eleanor FG Neal
- Infection & Immunity, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - David J Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences, University of Warwick, Coventry UK
| | - Wirichada Pan-ngum
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Gail E Potter
- National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville MD, USA
- The Emmes Company, Rockville MD, USA
| | - Fiona M Russell
- Infection & Immunity, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Siddhartha Saha
- Influenza Programme, US Centers for Disease Control and Prevention, India Office, US Embassy, New Delhi
| | - Jonathan D Sugimoto
- Seattle Epidemiologic Research and Information Center, Cooperative Studies Program, Office of Research and Development, United States Department of Veterans Affairs, USA
- Department of Epidemiology, University of Washington, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Wan In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Robin R Wood
- Desmond Tutu HIV Centre, Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, New Territories, Hong Kong SAR, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Patrick GT Walker
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| |
Collapse
|
32
|
Kleynhans J, Tempia S, McMorrow ML, von Gottberg A, Martinson NA, Kahn K, Moyes J, Mkhencele T, Lebina L, Gómez-Olivé FX, Wafawanaka F, Mathunjwa A, Cohen C. A cross-sectional study measuring contact patterns using diaries in an urban and a rural community in South Africa, 2018. BMC Public Health 2021; 21:1055. [PMID: 34078327 PMCID: PMC8172361 DOI: 10.1186/s12889-021-11136-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/24/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Describing contact patterns is crucial to understanding infectious disease transmission dynamics and guiding targeted transmission mitigation interventions. Data on contact patterns in Africa, especially South Africa, are limited. We measured and compared contact patterns in a rural and urban community, South Africa. We assessed participant and contact characteristics associated with differences in contact rates. METHODS We conducted a cross-sectional study nested in a prospective household cohort study. We interviewed participants to collect information on persons in contact with for one day. We described self-reported contact rates as median number people contacted per day, assessed differences in contact rates based on participant characteristics using quantile regression, and used a Poisson model to assess differences in contact rates based on contact characteristics within age groups. We also calculated cumulative person hours in contact within age groups at different locations. RESULTS We conducted 535 interviews (269 rural, 266 urban), with 17,252 contacts reported. The overall contact rate was 14 (interquartile range (IQR) 9-33) contacts per day. Those ≤18 years had higher contact rates at the rural site (coefficient 17, 95% confidence interval (95%CI) 10-23) compared to the urban site, for those aged 14-18 years (13, 95%CI 3-23) compared to < 7 years. No differences were observed for adults. There was a strong age-based mixing, with age groups interacting more with similar age groups, but also interaction of participants of all ages with adults. Children aged 14-18 years had the highest cumulative person hours in contact (116.3 rural and 76.4 urban). CONCLUSIONS Age played an important role in the number and duration of contact events, with children at the rural site having almost double the contact rate compared to the urban site. These contact rates can be utilized in mathematical models to assess transmission dynamics of infectious diseases in similar communities.
Collapse
Affiliation(s)
- Jackie Kleynhans
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa.
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Stefano Tempia
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Influenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa
- MassGenics, Duluth, Georgia, USA
| | - Meredith L McMorrow
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Influenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa
- United States Public Health Service, Rockville, MD, USA
| | - Anne von Gottberg
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Neil A Martinson
- Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg, South Africa
- Johns Hopkins University Center for Tuberculosis Research, Baltimore, MD, USA
- Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, University of the Witwatersrand, Johannesburg, South Africa
| | - Kathleen Kahn
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jocelyn Moyes
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Thulisa Mkhencele
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Limakatso Lebina
- Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - F Xavier Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Floidy Wafawanaka
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Azwifarwi Mathunjwa
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Cheryl Cohen
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| |
Collapse
|
33
|
McQuaid CF, Vassall A, Cohen T, Fiekert K, White RG. The impact of COVID-19 on TB: a review of the data. Int J Tuberc Lung Dis 2021; 25:436-446. [PMID: 34049605 PMCID: PMC8171247 DOI: 10.5588/ijtld.21.0148] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/23/2021] [Indexed: 12/12/2022] Open
Abstract
Early in the COVID-19 pandemic, models predicted hundreds of thousands of additional TB deaths as a result of health service disruption. To date, empirical evidence on the effects of COVID-19 on TB outcomes has been limited. Here we summarise the evidence available at a country level, identifying broad mechanisms by which COVID-19 may modify TB burden and mitigation efforts. From the data, it is clear that there have been substantial disruptions to TB health services and an increase in vulnerability to TB. Evidence for changes in Mycobacterium tuberculosis transmission is limited, and it remains unclear how the resources required and available for the TB response have changed. To advocate for additional funding to mitigate the impact of COVID-19 on the global TB burden, and to efficiently allocate resources for the TB response, requires a significant improvement in the TB data available.
Collapse
Affiliation(s)
- C F McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
| | - A Vassall
- Department of Global Health Development, Faculty of Public Health and Policy, LSHTM, London, UK
| | - T Cohen
- Yale School of Public Health, Laboratory of Epidemiology and Public Health, New Haven, CT, USA
| | - K Fiekert
- KNCV Tuberculosefonds, The Hague, the Netherlands
| | - R G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
| |
Collapse
|
34
|
Menkir TF, Jbaily A, Verguet S. Incorporating equity in infectious disease modeling: Case study of a distributional impact framework for measles transmission. Vaccine 2021; 39:2894-2900. [PMID: 33863575 PMCID: PMC8117973 DOI: 10.1016/j.vaccine.2021.03.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 02/22/2021] [Accepted: 03/04/2021] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Deterministic compartmental models of infectious diseases like measles typically reflect biological heterogeneities in the risk of infection and severity to characterize transmission dynamics. Given the known association of socioeconomic status and increased vulnerability to infection and mortality, it is also critical that such models further incorporate social heterogeneities. METHODS Here, we aimed to explore the influence of integrating income-associated differences in parameters of traditional dynamic transmission models. We developed a measles SIR model, in which the Susceptible, Infected and Recovered classes were stratified by income quintile, with income-specific transmission rates, disease-induced mortality rates, and vaccination coverage levels. We further provided a stylized illustration with secondary data from Ethiopia, where we examined various scenarios demonstrating differences in transmission patterns by income and in distributional vaccination coverage, and quantified impacts on disparities in measles mortality. RESULTS The income-stratified SIR model exhibited similar dynamics to that of the traditional SIR model, with amplified outbreak peaks and measles mortality among the poorest income group. All vaccination coverage strategies were found to substantially curb the overall number of measles deaths, yet most considerably for the poorest, with select strategies yielding clear reductions in measles mortality disparities. DISCUSSION The incorporation of income-specific differences can reveal distinct outbreak patterns across income groups and important differences in the subsequent effects of preventative interventions like vaccination. Our case study highlights the need to extend traditional modeling frameworks (e.g. SIR models) to be stratified by socioeconomic factors like income and to consider ensuing income-associated differences in disease-related morbidity and mortality. In so doing, we build on existing tools and characterize ongoing challenges in achieving health equity.
Collapse
Affiliation(s)
- Tigist Ferede Menkir
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Abdulrahman Jbaily
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Stéphane Verguet
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
| |
Collapse
|
35
|
Trentini F, Guzzetta G, Galli M, Zardini A, Manenti F, Putoto G, Marziano V, Gamshie WN, Tsegaye A, Greblo A, Melegaro A, Ajelli M, Merler S, Poletti P. Modeling the interplay between demography, social contact patterns, and SARS-CoV-2 transmission in the South West Shewa Zone of Oromia Region, Ethiopia. BMC Med 2021; 19:89. [PMID: 33832497 PMCID: PMC8032453 DOI: 10.1186/s12916-021-01967-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND COVID-19 spread may have a dramatic impact in countries with vulnerable economies and limited availability of, and access to, healthcare resources and infrastructures. However, in sub-Saharan Africa, a low prevalence and mortality have been observed so far. METHODS We collected data on individuals' social contacts in the South West Shewa Zone (SWSZ) of Ethiopia across geographical contexts characterized by heterogeneous population density, work and travel opportunities, and access to primary care. We assessed how socio-demographic factors and observed mixing patterns can influence the COVID-19 disease burden, by simulating SARS-CoV-2 transmission in remote settlements, rural villages, and urban neighborhoods, under school closure mandate. RESULTS From national surveillance data, we estimated a net reproduction number of 1.62 (95% CI 1.55-1.70). We found that, at the end of an epidemic mitigated by school closure alone, 10-15% of the population residing in the SWSZ would have been symptomatic and 0.3-0.4% of the population would require mechanical ventilation and/or possibly result in a fatal outcome. Higher infection attack rates are expected in more urbanized areas, but the highest incidence of critical disease is expected in remote subsistence farming settlements. School closure contributed to reduce the reproduction number by 49% and the attack rate of infections by 28-34%. CONCLUSIONS Our results suggest that the relatively low burden of COVID-19 in Ethiopia observed so far may depend on social mixing patterns, underlying demography, and the enacted school closures. Our findings highlight that socio-demographic factors can also determine marked heterogeneities across different geographical contexts within the same region, and they contribute to understand why sub-Saharan Africa is experiencing a relatively lower attack rate of severe cases compared to high-income countries.
Collapse
Affiliation(s)
- Filippo Trentini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Margherita Galli
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.,University of Udine, Udine, Italy
| | - Agnese Zardini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.,University of Trento, Trento, Italy
| | | | | | | | | | | | | | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.,Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.,Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
| |
Collapse
|
36
|
Hu S, Wang W, Wang Y, Litvinova M, Luo K, Ren L, Sun Q, Chen X, Zeng G, Li J, Liang L, Deng Z, Zheng W, Li M, Yang H, Guo J, Wang K, Chen X, Liu Z, Yan H, Shi H, Chen Z, Zhou Y, Sun K, Vespignani A, Viboud C, Gao L, Ajelli M, Yu H. Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China. Nat Commun 2021; 12:1533. [PMID: 33750783 PMCID: PMC7943579 DOI: 10.1038/s41467-021-21710-6] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/02/2021] [Indexed: 01/08/2023] Open
Abstract
Several mechanisms driving SARS-CoV-2 transmission remain unclear. Based on individual records of 1178 potential SARS-CoV-2 infectors and their 15,648 contacts in Hunan, China, we estimated key transmission parameters. The mean generation time was estimated to be 5.7 (median: 5.5, IQR: 4.5, 6.8) days, with infectiousness peaking 1.8 days before symptom onset, with 95% of transmission events occurring between 8.8 days before and 9.5 days after symptom onset. Most transmission events occurred during the pre-symptomatic phase (59.2%). SARS-CoV-2 susceptibility to infection increases with age, while transmissibility is not significantly different between age groups and between symptomatic and asymptomatic individuals. Contacts in households and exposure to first-generation cases are associated with higher odds of transmission. Our findings support the hypothesis that children can effectively transmit SARS-CoV-2 and highlight how pre-symptomatic and asymptomatic transmission can hinder control efforts.
Collapse
Affiliation(s)
- Shixiong Hu
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Wei Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Maria Litvinova
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
- ISI Foundation, Turin, Italy
| | - Kaiwei Luo
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Lingshuang Ren
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Qianlai Sun
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Xinghui Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ge Zeng
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Jing Li
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Lu Liang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhihong Deng
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Wen Zheng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Mei Li
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hao Yang
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Jinxin Guo
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kai Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xinhua Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ziyan Liu
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Han Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Huilin Shi
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yonghong Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Alessandro Vespignani
- ISI Foundation, Turin, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Lidong Gao
- Hunan Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Hunan Provincial Center for Disease Control and Prevention, Changsha, China.
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
- Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
| |
Collapse
|
37
|
Glynn JR, McLean E, Malava J, Dube A, Katundu C, Crampin AC, Geis S. Effect of Acute Illness on Contact Patterns, Malawi, 2017. Emerg Infect Dis 2021; 26:44-50. [PMID: 31855144 PMCID: PMC6924881 DOI: 10.3201/eid2601.181539] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The way persons interact when ill could profoundly affect transmission of infectious agents. To obtain data on these patterns in Africa, we recorded self-reported named contacts and opportunities for casual contact in rural northern Malawi. We interviewed 384 patients and 257 caregivers about contacts over three 24-hour periods: day of the clinic visit for acute illness, the next day, and 2 weeks later when well. For participants of all ages, the number of adult contacts and the proportion using public transportation was higher on the day of the clinic visit than later when well. Compared with the day after the clinic visit, well participants (2 weeks later) named a mean of 0.4 extra contacts; the increase was larger for indoor or prolonged contacts. When well, participants were more likely to visit other houses and congregate settings. When ill, they had more visitors at home. These findings could help refine models of infection spread.
Collapse
|
38
|
Mburu CN, Ojal J, Chebet R, Akech D, Karia B, Tuju J, Sigilai A, Abbas K, Jit M, Funk S, Smits G, van Gageldonk PGM, van der Klis FRM, Tabu C, Nokes DJ, Scott J, Flasche S, Adetifa I. The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya. BMC Med 2021; 19:35. [PMID: 33531015 PMCID: PMC7854026 DOI: 10.1186/s12916-021-01906-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/11/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. METHODS Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. RESULTS In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8-54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19-54), 46% (30-59), and 54% (43-64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25-56), 54% (43-63), and 67% (59-72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. CONCLUSION While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.
Collapse
Affiliation(s)
- C N Mburu
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - J Ojal
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - R Chebet
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - D Akech
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - B Karia
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - J Tuju
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - A Sigilai
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - K Abbas
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - M Jit
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - S Funk
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - G Smits
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - P G M van Gageldonk
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - F R M van der Klis
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - C Tabu
- National Vaccine and Immunisation Programme, Ministry of Health, Nairobi, Kenya
| | - D J Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| | - Jag Scott
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - S Flasche
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Imo Adetifa
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| |
Collapse
|
39
|
Grantz KH, Cummings DAT, Zimmer S, Vukotich Jr. C, Galloway D, Schweizer ML, Guclu H, Cousins J, Lingle C, Yearwood GMH, Li K, Calderone P, Noble E, Gao H, Rainey J, Uzicanin A, Read JM. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys. Sci Rep 2021; 11:2319. [PMID: 33504823 PMCID: PMC7840989 DOI: 10.1038/s41598-021-81673-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.
Collapse
Affiliation(s)
- Kyra H. Grantz
- grid.15276.370000 0004 1936 8091Department of Biology, University of Florida, Gainesville, FL 32611 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611 USA ,grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Derek A. T. Cummings
- grid.15276.370000 0004 1936 8091Department of Biology, University of Florida, Gainesville, FL 32611 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611 USA ,grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Shanta Zimmer
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA ,grid.241116.10000000107903411Department of Medicine, University of Colorado School of Medicine, Denver, CO 80045 USA
| | - Charles Vukotich Jr.
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - David Galloway
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Mary Lou Schweizer
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - Hasan Guclu
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.411776.20000 0004 0454 921XPresent Address: Department of Biostatistics and Medical Informatics, School of Medicine, Istanbul Medeniyet University, Istanbul, Turkey
| | - Jennifer Cousins
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Present Address: Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - Carrie Lingle
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,Present Address: Toledo Lucas County Health Department, Toledo, OH USA
| | - Gabby M. H. Yearwood
- grid.21925.3d0000 0004 1936 9000Department of Anthropology, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Kan Li
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,Present Address: Merck Pharmaceuticals, Philadelphia, PA USA
| | - Patti Calderone
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - Eva Noble
- grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Hongjiang Gao
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA
| | - Jeanette Rainey
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA ,grid.416738.f0000 0001 2163 0069Present Address: Division of Global Health Protection, US Centers for Disease Control and Prevention, Atlanta, GA USA
| | - Amra Uzicanin
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA
| | - Jonathan M. Read
- grid.9835.70000 0000 8190 6402Centre for Health Informatics Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW UK ,grid.10025.360000 0004 1936 8470Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 7BE UK
| |
Collapse
|
40
|
McCreesh N, Dlamini V, Edwards A, Olivier S, Dayi N, Dikgale K, Nxumalo S, Dreyer J, Baisley K, Siedner MJ, White RG, Herbst K, Grant AD, Harling G. Impact of social distancing regulations and epidemic risk perception on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.12.01.20241877. [PMID: 33300009 PMCID: PMC7724677 DOI: 10.1101/2020.12.01.20241877] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. Methods We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational ('close interaction') contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R0). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space ('shared air'). Results Respondents in March-December 2019 (n=1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n=216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others' homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15-29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R0 fell by 42% (95% plausible range 14-59%) between 2019 and June-July 2020. Discussion Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted the limitation of social distancing measures in protecting older adults. Funding Wellcome Trust, UKRI, DFID, European Union.
Collapse
Affiliation(s)
- Nicky McCreesh
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Vuyiswa Dlamini
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Anita Edwards
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Njabulo Dayi
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | | | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Kathy Baisley
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- DSI-MRC South African Population Research Infrastructure Network, South Africa
| | - Alison D. Grant
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- TB Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Laboratory and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Guy Harling
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Institute for Global Health, University College London, London, United Kingdom
- Department of Epidemiology & Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| |
Collapse
|
41
|
Hu S, Wang W, Wang Y, Litvinova M, Luo K, Ren L, Sun Q, Chen X, Zeng G, Li J, Liang L, Deng Z, Zheng W, Li M, Yang H, Guo J, Wang K, Chen X, Liu Z, Yan H, Shi H, Chen Z, Zhou Y, Sun K, Vespignani A, Viboud C, Gao L, Ajelli M, Yu H. Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32793929 DOI: 10.1101/2020.07.23.20160317] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Several mechanisms driving SARS-CoV-2 transmission remain unclear. Based on individual records of 1,178 SARS-CoV-2 infectors and their 15,648 contacts in Hunan, China, we estimated key transmission parameters. The mean generation time was estimated to be 5.7 (median: 5.5, IQR: 4.5, 6.8) days, with infectiousness peaking 1.8 days before symptom onset, with 95% of transmission events occurring between 8.8 days before and 9.5 days after symptom onset. Most of transmission events occurred during the pre-symptomatic phase (59.2%). SARS-CoV-2 susceptibility to infection increases with age, while transmissibility is not significantly different between age groups and between symptomatic and asymptomatic individuals. Contacts in households and exposure to first-generation cases are associated with higher odds of transmission. Our findings support the hypothesis that children can effectively transmit SARS-CoV-2 and highlight how pre-symptomatic and asymptomatic transmission can hinder control efforts.
Collapse
|
42
|
Chisholm RH, Crammond B, Wu Y, Bowen AC, Campbell PT, Tong SYC, McVernon J, Geard N. A model of population dynamics with complex household structure and mobility: implications for transmission and control of communicable diseases. PeerJ 2020; 8:e10203. [PMID: 33194407 PMCID: PMC7646303 DOI: 10.7717/peerj.10203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 09/27/2020] [Indexed: 01/09/2023] Open
Abstract
Households are known to be high-risk locations for the transmission of communicable diseases. Numerous modelling studies have demonstrated the important role of households in sustaining both communicable diseases outbreaks and endemic transmission, and as the focus for control efforts. However, these studies typically assume that households are associated with a single dwelling and have static membership. This assumption does not appropriately reflect households in some populations, such as those in remote Australian Aboriginal and Torres Strait Islander communities, which can be distributed across more than one physical dwelling, leading to the occupancy of individual dwellings changing rapidly over time. In this study, we developed an individual-based model of an infectious disease outbreak in communities with demographic and household structure reflective of a remote Australian Aboriginal community. We used the model to compare the dynamics of unmitigated outbreaks, and outbreaks constrained by a household-focused prophylaxis intervention, in communities exhibiting fluid vs. stable dwelling occupancy. We found that fluid dwelling occupancy can lead to larger and faster outbreaks in modelled scenarios, and may interfere with the effectiveness of household-focused interventions. Our findings suggest that while short-term restrictions on movement between dwellings may be beneficial during outbreaks, in the longer-term, strategies focused on reducing household crowding may be a more effective way to reduce the risk of severe outbreaks occurring in populations with fluid dwelling occupancy.
Collapse
Affiliation(s)
- Rebecca H Chisholm
- Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Bradley Crammond
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Yue Wu
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia
| | - Asha C Bowen
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia.,Infectious Diseases Department, Perth Children's Hospital, Perth, WA, Australia
| | - Patricia T Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.,Victorian Infectious Diseases Reference Laboratory Epidemiology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Steven Y C Tong
- Doherty Department, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Victorian Infectious Diseases Service, The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia
| | - Jodie McVernon
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.,Victorian Infectious Diseases Reference Laboratory Epidemiology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Nicholas Geard
- Victorian Infectious Diseases Reference Laboratory Epidemiology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Melbourne, VIC, Australia
| |
Collapse
|
43
|
Thindwa D, Pinsent A, Ojal J, Gallagher KE, French N, Flasche S. Vaccine strategies to reduce the burden of pneumococcal disease in HIV-infected adults in Africa. Expert Rev Vaccines 2020; 19:1085-1092. [PMID: 33269987 PMCID: PMC8315211 DOI: 10.1080/14760584.2020.1843435] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Streptococcus pneumoniae is the leading cause of invasive bacterial disease, globally. Despite antiretroviral therapy, adults infected with human immunodeficiency virus (HIV) are also at high risk of pneumococcal carriage and disease. Pneumococcal conjugate vaccines (PCVs) provide effective protection against vaccine serotype (VT) carriage and disease in children, and have been introduced worldwide, including most HIV-affected low- and middle-income countries. Unlike high-income countries, the circulation of VT persists in the PCV era in some low-income countries and results in a continued high burden of pneumococcal disease in HIV-infected adults. Moreover, no routine vaccination that directly protects HIV-infected adults in such settings has been implemented. AREAS COVERED Nonsystematic review on the pneumococcal burden in HIV-infected adults and vaccine strategies to reduce this burden. EXPERT OPINION We propose and discuss the relative merit of changing the infant PCV program to use (1a) a two prime plus booster dose schedule, (1b) a two prime plus booster dose schedule with an additional booster dose at school entry, to directly vaccinate (2a) HIV-infected adults or vaccinating (2b) HIV-infected pregnant women for direct protection, with added indirect protection to the high-risk neonates. We identify key knowledge gaps for such an evaluation and propose strategies to overcome them.
Collapse
Affiliation(s)
- Deus Thindwa
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK,Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre, Malawi,CONTACT Deus Thindwa Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, WC1E 7HT, London, United Kingdom
| | - Amy Pinsent
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK,Aquarius Population Health, London, UK
| | - John Ojal
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK,Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine, Coast, Kilifi, Kenya
| | - Katherine E Gallagher
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Neil French
- Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre, Malawi,Institute of Infection and Global Health, Department of Clinical Infection, Microbiology, and Immunology, University of Liverpool, Liverpool, UK
| | - Stefan Flasche
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
44
|
van Zandvoort K, Jarvis CI, Pearson CAB, Davies NG, Ratnayake R, Russell TW, Kucharski AJ, Jit M, Flasche S, Eggo RM, Checchi F. Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study. BMC Med 2020; 18:324. [PMID: 33050951 PMCID: PMC7553800 DOI: 10.1186/s12916-020-01789-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 09/22/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The health impact of COVID-19 may differ in African settings as compared to countries in Europe or China due to demographic, epidemiological, environmental and socio-economic factors. We evaluated strategies to reduce SARS-CoV-2 burden in African countries, so as to support decisions that balance minimising mortality, protecting health services and safeguarding livelihoods. METHODS We used a Susceptible-Exposed-Infectious-Recovered mathematical model, stratified by age, to predict the evolution of COVID-19 epidemics in three countries representing a range of age distributions in Africa (from oldest to youngest average age: Mauritius, Nigeria and Niger), under various effectiveness assumptions for combinations of different non-pharmaceutical interventions: self-isolation of symptomatic people, physical distancing and 'shielding' (physical isolation) of the high-risk population. We adapted model parameters to better represent uncertainty about what might be expected in African populations, in particular by shifting the distribution of severity risk towards younger ages and increasing the case-fatality ratio. We also present sensitivity analyses for key model parameters subject to uncertainty. RESULTS We predicted median symptomatic attack rates over the first 12 months of 23% (Niger) to 42% (Mauritius), peaking at 2-4 months, if epidemics were unmitigated. Self-isolation while symptomatic had a maximum impact of about 30% on reducing severe cases, while the impact of physical distancing varied widely depending on percent contact reduction and R0. The effect of shielding high-risk people, e.g. by rehousing them in physical isolation, was sensitive mainly to residual contact with low-risk people, and to a lesser extent to contact among shielded individuals. Mitigation strategies incorporating self-isolation of symptomatic individuals, moderate physical distancing and high uptake of shielding reduced predicted peak bed demand and mortality by around 50%. Lockdowns delayed epidemics by about 3 months. Estimates were sensitive to differences in age-specific social mixing patterns, as published in the literature, and assumptions on transmissibility, infectiousness of asymptomatic cases and risk of severe disease or death by age. CONCLUSIONS In African settings, as elsewhere, current evidence suggests large COVID-19 epidemics are expected. However, African countries have fewer means to suppress transmission and manage cases. We found that self-isolation of symptomatic persons and general physical distancing are unlikely to avert very large epidemics, unless distancing takes the form of stringent lockdown measures. However, both interventions help to mitigate the epidemic. Shielding of high-risk individuals can reduce health service demand and, even more markedly, mortality if it features high uptake and low contact of shielded and unshielded people, with no increase in contact among shielded people. Strategies combining self-isolation, moderate physical distancing and shielding could achieve substantial reductions in mortality in African countries. Temporary lockdowns, where socioeconomically acceptable, can help gain crucial time for planning and expanding health service capacity.
Collapse
Affiliation(s)
- Kevin van Zandvoort
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK.
| | - Christopher I Jarvis
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Carl A B Pearson
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
- South African Centre for Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, Republic of South Africa
| | - Nicholas G Davies
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Ruwan Ratnayake
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Timothy W Russell
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Adam J Kucharski
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Stefan Flasche
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Rosalind M Eggo
- Department of Infectious Disease Epidemiology, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street,, London, WC1E 7HT, UK
| | - Francesco Checchi
- Department of Infectious Disease Epidemiology, Health in Humanitarian Crises Centre, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| |
Collapse
|
45
|
Quaife M, van Zandvoort K, Gimma A, Shah K, McCreesh N, Prem K, Barasa E, Mwanga D, Kangwana B, Pinchoff J, Edmunds WJ, Jarvis CI, Austrian K. The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements. BMC Med 2020; 18:316. [PMID: 33012285 PMCID: PMC7533154 DOI: 10.1186/s12916-020-01779-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/09/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Many low- and middle-income countries have implemented control measures against coronavirus disease 2019 (COVID-19). However, it is not clear to what extent these measures explain the low numbers of recorded COVID-19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study, we collect contact data from residents of informal settlements around Nairobi, Kenya, to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number (R0). METHODS We conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, 4 weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7 pm and 5 am. Respondents were asked to report all direct physical and non-physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID-19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID-19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non-pandemic periods to estimate the change in R0. RESULTS We estimate that control measures reduced physical contacts by 62% and non-physical contacts by either 63% or 67%, depending on the pre-COVID-19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. Eighty-six percent of respondents reported a total or partial loss of income due to COVID-19, and 74% reported eating less or skipping meals due to having too little money for food. CONCLUSION COVID-19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R0 are consistent with the comparatively low epidemic growth in Kenya and other sub-Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term.
Collapse
Affiliation(s)
- Matthew Quaife
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
| | - Kevin van Zandvoort
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Amy Gimma
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kashvi Shah
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Nicky McCreesh
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kiesha Prem
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Edwine Barasa
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | | | | | | | - W John Edmunds
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher I Jarvis
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | |
Collapse
|
46
|
Oh HS, Ryu M. Prospective diary survey of preschool children's social contact patterns: A pilot study. CHILD HEALTH NURSING RESEARCH 2020; 26:393-401. [PMID: 35004483 PMCID: PMC8650865 DOI: 10.4094/chnr.2020.26.4.393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 08/24/2020] [Accepted: 09/19/2020] [Indexed: 11/06/2022] Open
Abstract
Purpose This pilot study aimed to describe children's social contact patterns and to analyze factors related to their social contacts. Methods The participants were 30 children aged ≥13 months to <7 years, whose teachers at childcare centers and parents at home were asked to maintain diaries of their social contacts prospectively for 24 hours. Data were collected from November 30, 2018, to January 7, 2019. Results The 30 participating children were in contact with 363 persons in a 24-hours period (mean, 12.1±9.1). The number of contacts showed significant relationships with day of the week (p<.001), number of family members/cohabitants (p=.015), area of residence (p=.003), and type of housing (p=.002). A multiple regression model showed significantly higher numbers of contacts on weekdays (B=10.64, p=.010). Physical versus non-physical types of contact showed significant differences in terms of duration, location, and frequency (p<.001). The duration of contacts showed significant relationships with their location and frequency (p<.001), while the frequency of contacts was significantly related to their location (p<.001). Conclusion This is the first survey describing the characteristics of Korean preschool children's social contacts. Further large-scale social contact studies of children should be conducted.
Collapse
Affiliation(s)
- Hyang Soon Oh
- Associate Professor, Department of Nursing, College of Life Science and Natural Resources, Sunchon National University, Suncheon, Korea
| | - Mikyung Ryu
- Assistant Professor, Department of Nursing, Daegu University, Daegu, Korea
| |
Collapse
|
47
|
Adema IW, Kamau E, Uchi Nyiro J, Otieno GP, Lewa C, Munywoki PK, Nokes DJ. Surveillance of respiratory viruses among children attending a primary school in rural coastal Kenya. Wellcome Open Res 2020; 5:63. [PMID: 33102784 PMCID: PMC7569485 DOI: 10.12688/wellcomeopenres.15703.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2020] [Indexed: 01/07/2023] Open
Abstract
Background: Respiratory viruses are primary agents of respiratory tract diseases. Knowledge on the types and frequency of respiratory viruses affecting school-children is important in determining the role of schools in transmission in the community and identifying targets for interventions. Methods: We conducted a one-year (term-time) surveillance of respiratory viruses in a rural primary school in Kilifi County, coastal Kenya between May 2017 and April 2018. A sample of 60 students with symptoms of ARI were targeted for nasopharyngeal swab (NPS) collection weekly. Swabs were screened for 15 respiratory virus targets using real time PCR diagnostics. Data from respiratory virus surveillance at the local primary healthcare facility was used for comparison. Results: Overall, 469 students aged 2-19 years were followed up for 220 days. A total of 1726 samples were collected from 325 symptomatic students; median age of 7 years (IQR 5-11). At least one virus target was detected in 384 (22%) of the samples with a frequency of 288 (16.7%) for rhinovirus, 47 (2.7%) parainfluenza virus, 35 (2.0%) coronavirus, 15 (0.9%) adenovirus, 11 (0.6%) respiratory syncytial virus (RSV) and 5 (0.3%) influenza virus. The proportion of virus positive samples was higher among lower grades compared to upper grades (25.9% vs 17.5% respectively; χ
2 = 17.2,
P -value <0.001). Individual virus target frequencies did not differ by age, sex, grade, school term or class size. Rhinovirus was predominant in both the school and outpatient setting. Conclusion: Multiple respiratory viruses circulated in this rural school population. Rhinovirus was dominant in both the school and outpatient setting and RSV was of notably low frequency in the school. The role of school children in transmitting viruses to the household setting is still unclear and further studies linking molecular data to contact patterns between the school children and their households are required.
Collapse
Affiliation(s)
- Irene Wangwa Adema
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Everlyn Kamau
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Joyce Uchi Nyiro
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Grieven P Otieno
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Clement Lewa
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Patrick K Munywoki
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - D James Nokes
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya.,School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, Coventry, CV4 7AL, UK
| |
Collapse
|
48
|
Wagner M, Kombe IK, Kiti MC, Aziza R, Barasa E, Nokes DJ. Using contact data to model the impact of contact tracing and physical distancing to control the SARS-CoV-2 outbreak in Kenya. Wellcome Open Res 2020. [DOI: 10.12688/wellcomeopenres.16264.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background: Across the African continent, other than South Africa, COVID-19 cases have remained relatively low. Nevertheless, in Kenya, despite early implementation of containment measures and restrictions, cases have consistently been increasing. Contact tracing forms one of the key strategies in Kenya, but may become infeasible as the caseload grows. Here we explore different contact tracing strategies by distinguishing between household and non-household contacts and how these may be combined with other non-pharmaceutical interventions. Methods: We extend a previously developed branching process model for contact tracing to include realistic contact data from Kenya. Using the contact data, we generate a synthetic population of individuals and their contacts categorised by age and household membership. We simulate the initial spread of SARS-CoV-2 through this population and look at the effectiveness of a number of non-pharmaceutical interventions with a particular focus on different contact tracing strategies and the potential effort involved in these. Results: General physical distancing and avoiding large group gatherings combined with contact tracing, where all contacts are isolated immediately, can be effective in slowing down the outbreak, but were, under our base assumptions, not enough to control it without implementing extreme stay at home policies. Under optimistic assumptions with a highly overdispersed R0 and a short delay from symptom onset to isolation, control was possible with less stringent physical distancing and by isolating household contacts only. Conclusions: Without strong physical distancing measures, controlling the spread of SARS-CoV-2 is difficult. With limited resources, physical distancing combined with the isolation of households of detected cases can form a moderately effective strategy, and control is possible under optimistic assumptions. More data are needed to understand transmission in Kenya, in particular by studying the settings that lead to larger transmission events, which may allow for more targeted responses, and collection of representative age-related contact data.
Collapse
|
49
|
Abstract
BACKGROUND Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published. METHODS We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible. RESULTS In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies. CONCLUSIONS We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.
Collapse
|
50
|
Luka MM, Kamau E, Adema I, Munywoki PK, Otieno GP, Gicheru E, Gichuki A, Kibinge N, Agoti CN, Nokes DJ. Molecular Epidemiology of Human Rhinovirus From 1-Year Surveillance Within a School Setting in Rural Coastal Kenya. Open Forum Infect Dis 2020; 7:ofaa385. [PMID: 33094115 PMCID: PMC7568438 DOI: 10.1093/ofid/ofaa385] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 08/21/2020] [Indexed: 01/12/2023] Open
Abstract
Background Human rhinovirus (HRV) is the most common cause of the common cold but may also lead to more severe respiratory illness in vulnerable populations. The epidemiology and genetic diversity of HRV within a school setting have not been previously described. The objective of this study was to characterize HRV molecular epidemiology in a primary school in a rural location of Kenya. Methods Between May 2017 and April 2018, over 3 school terms, we collected 1859 nasopharyngeal swabs (NPS) from pupils and teachers with symptoms of acute respiratory infection in a public primary school in Kilifi County, coastal Kenya. The samples were tested for HRV using real-time reverse transcription polymerase chain reaction. HRV-positive samples were sequenced in the VP4/VP2 coding region for species and genotype classification. Results A total of 307 NPS (16.4%) from 164 individuals were HRV positive, and 253 (82.4%) were successfully sequenced. The proportion of HRV in the lower primary classes was higher (19.8%) than upper primary classes (12.2%; P < .001). HRV-A was the most common species (134/253; 53.0%), followed by HRV-C (73/253; 28.9%) and HRV-B (46/253; 18.2%). Phylogenetic analysis identified 47 HRV genotypes. The most common genotypes were A2 and B70. Numerous (up to 22 in 1 school term) genotypes circulated simultaneously, there was no individual re-infection with the same genotype, and no genotype was detected in all 3 school terms. Conclusions HRV was frequently detected among school-going children with mild acute respiratory illness symptoms, particularly in the younger age groups (<5-year-olds). Multiple HRV introductions were observed that were characterized by considerable genotype diversity.
Collapse
Affiliation(s)
- Martha M Luka
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.,Department of Public Health, Pwani University, Kilifi, Kenya
| | - Everlyn Kamau
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Irene Adema
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Patrick K Munywoki
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Grieven P Otieno
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Elijah Gicheru
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Alex Gichuki
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Nelson Kibinge
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
| | - Charles N Agoti
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.,Department of Public Health, Pwani University, Kilifi, Kenya
| | - D James Nokes
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya.,School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| |
Collapse
|