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González-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models. Infect Dis Model 2024; 9:1057-1080. [PMID: 38988830 PMCID: PMC11233876 DOI: 10.1016/j.idm.2024.05.005] [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: 03/04/2024] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 07/12/2024] Open
Abstract
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
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Affiliation(s)
- Gilberto González-Parra
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain
- Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA
| | - Md Shahriar Mahmud
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
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Jarvis CI, Coletti P, Backer JA, Munday JD, Faes C, Beutels P, Althaus CL, Low N, Wallinga J, Hens N, Edmunds WJ. Social contact patterns following the COVID-19 pandemic: a snapshot of post-pandemic behaviour from the CoMix study. Epidemics 2024; 48:100778. [PMID: 38964131 DOI: 10.1016/j.epidem.2024.100778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 07/06/2024] Open
Abstract
The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted, a final round of the CoMix social contact survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R0. Data collection occurred from 17 November to 7 December 2022. 7477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4 % of all participants reported wearing a facemask on the previous day. Self-reported vaccination rates in adults were similar for each country at around 86 %. Trimmed mean recorded contacts were highest in NL with 9.9 (95 % confidence interval [CI] 9.0-10.8) contacts per person per day and lowest in CH at 6.0 (95 % CI 5.4-6.6). Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95 % CI 1.4-1.9) and highest in NL at 3.4 recorded per person per day (95 % CI 43.0-4.0). The next-generation approach suggests that R0 for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80 % in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding.
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Affiliation(s)
- Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium.
| | - Jantien A Backer
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - James D Munday
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium
| | - Philippe Beutels
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk 2610, Belgium
| | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk 2610, Belgium
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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Reichmuth ML, Heron L, Beutels P, Hens N, Low N, Althaus CL. Social contacts in Switzerland during the COVID-19 pandemic: Insights from the CoMix study. Epidemics 2024; 47:100771. [PMID: 38821037 DOI: 10.1016/j.epidem.2024.100771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 06/02/2024] Open
Abstract
To mitigate the spread of SARS-CoV-2, the Swiss government enacted restrictions on social contacts from 2020 to 2022. In addition, individuals changed their social contact behavior to limit the risk of COVID-19. In this study, we aimed to investigate the changes in social contact patterns of the Swiss population. As part of the CoMix study, we conducted a survey consisting of 24 survey waves from January 2021 to May 2022. We collected data on social contacts and constructed contact matrices for the age groups 0-4, 5-14, 15-29, 30-64, and 65 years and older. We estimated the change in contact numbers during the COVID-19 pandemic to a synthetic pre-pandemic contact matrix. We also investigated the association of the largest eigenvalue of the social contact and transmission matrices with the stringency of pandemic measures, the effective reproduction number (Re), and vaccination uptake. During the pandemic period, 7084 responders reported an average number of 4.5 contacts (95% confidence interval, CI: 4.5-4.6) per day overall, which varied by age and survey wave. Children aged 5-14 years had the highest number of contacts with 8.5 (95% CI: 8.1-8.9) contacts on average per day and participants that were 65 years and older reported the fewest (3.4, 95% CI: 3.2-3.5) per day. Compared with the pre-pandemic baseline, we found that the 15-29 and 30-64 year olds had the largest reduction in contacts. We did not find statistically significant associations between the largest eigenvalue of the social contact and transmission matrices and the stringency of measures, Re, or vaccination uptake. The number of social contacts in Switzerland fell during the COVID-19 pandemic and remained below pre-pandemic levels after contact restrictions were lifted. The collected social contact data will be critical in informing modeling studies on the transmission of respiratory infections in Switzerland and to guide pandemic preparedness efforts.
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Affiliation(s)
- Martina L Reichmuth
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Leonie Heron
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, Antwerp, Belgium
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, Antwerp, Belgium; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland.
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Veneti L, Robberstad B, Steens A, Forland F, Winje BA, Vestrheim DF, Jarvis CI, Gimma A, Edmunds WJ, Van Zandvoort K, de Blasio BF. Social contact patterns during the early COVID-19 pandemic in Norway: insights from a panel study, April to September 2020. BMC Public Health 2024; 24:1438. [PMID: 38811933 PMCID: PMC11137890 DOI: 10.1186/s12889-024-18853-8] [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: 12/15/2023] [Accepted: 05/14/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND During the COVID-19 pandemic, many countries adopted social distance measures and lockdowns of varying strictness. Social contact patterns are essential in driving the spread of respiratory infections, and country-specific measurements are needed. This study aimed to gain insights into changes in social contacts and behaviour during the early pandemic phase in Norway. METHODS We conducted an online panel study among a nationally representative sample of Norwegian adults by age and gender. The panel study included six data collections waves between April and September 2020, and 2017 survey data from a random sample of the Norwegian population (including children < 18 years old) were used as baseline. The market research company Ipsos was responsible for carrying out the 2020 surveys. We calculated mean daily contacts, and estimated age-stratified contact matrices during the study period employing imputation of child-to-child contacts. We used the next-generation method to assess the relative reduction of R0 and compared the results to reproduction numbers estimated for Norway during the 2020 study period. RESULTS Over the six waves in 2020, 5 938 observations/responses were registered from 1 718 individuals who reported data on 22 074 contacts. The mean daily number of contacts among adults varied between 3.2 (95%CI 3.0-3.4) to 3.9 (95%CI 3.6-4.2) across the data collection waves, representing a 67-73% decline compared to pre-pandemic levels (baseline). Fewer contacts in the community setting largely drove the reduction; the drop was most prominent among younger adults. Despite gradual easing of social distance measures during the survey period, the estimated population contact matrices remained relatively stable and displayed more inter-age group mixing than at baseline. Contacts within households and the community outside schools and workplaces contributed most to social encounters. Using the next-generation method R0 was found to be roughly 25% of pre-pandemic levels during the study period, suggesting controlled transmission. CONCLUSION Social contacts declined significantly in the months following the March 2020 lockdown, aligning with implementation of stringent social distancing measures. These findings contribute valuable empirical information into the social behaviour in Norway during the early pandemic, which can be used to enhance policy-relevant models for addressing future crises when mitigation measures might be implemented.
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Affiliation(s)
- Lamprini Veneti
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Lovisenberggata 8, Oslo, 0456, Norway.
| | - Bjarne Robberstad
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Anneke Steens
- Department of Infection Control and Vaccine, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Forland
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Lovisenberggata 8, Oslo, 0456, Norway
| | - Brita A Winje
- Department of Infection Control and Vaccine, Norwegian Institute of Public Health, Oslo, Norway
| | - Didrik F Vestrheim
- Department of Infection Control and Vaccine, Norwegian Institute of Public Health, Oslo, Norway
| | - Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Amy Gimma
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Kevin Van Zandvoort
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Center for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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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.
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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
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Willem L, Abrams S, Franco N, Coletti P, Libin PJK, Wambua J, Couvreur S, André E, Wenseleers T, Mao Z, Torneri A, Faes C, Beutels P, Hens N. The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022). BMC Public Health 2024; 24:1171. [PMID: 38671366 PMCID: PMC11047051 DOI: 10.1186/s12889-024-18576-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] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. METHODS We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. RESULTS Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. CONCLUSIONS When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.
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Affiliation(s)
- Lander Willem
- Department of Family Medicine and Population Health, Antwerp, Belgium.
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
| | - Steven Abrams
- Department of Family Medicine and Population Health, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Pietro Coletti
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Pieter J K Libin
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - James Wambua
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Simon Couvreur
- Department of Epidemiology and public health, Sciensano, Brussel, Belgium
| | - Emmanuel André
- National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, University of Leuven, Leuven, Belgium
| | - Zhuxin Mao
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Andrea Torneri
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
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Angeli L, Caetano CP, Franco N, Abrams S, Coletti P, Van Nieuwenhuyse I, Pop S, Hens N. Who acquires infection from whom? A sensitivity analysis of transmission dynamics during the early phase of the COVID-19 pandemic in Belgium. J Theor Biol 2024; 581:111721. [PMID: 38218529 DOI: 10.1016/j.jtbi.2024.111721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/15/2024]
Abstract
Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial in studying infectious disease dynamics. Compartmental models represent a popular approach to address the problem, dividing the population of interest into a discrete and finite number of states depending on, for example, individuals' age and stage of infection. We study the corresponding linearised system whose operator, in the context of a discrete-time model, equates to a square matrix known as the next generation matrix. Performing formal perturbation analysis of the entries of the aforementioned matrix, we derive indices to quantify the age-specific variation of its dominant eigenvalue (i.e., the reproduction number) and explore the relevant epidemiological information we can derive from the eigenstructure of the matrix. The resulting method enables the assessment of the impact of age-related population heterogeneity on virus transmission. In particular, starting from an age-structured SEIR model, we demonstrate the use of this approach for COVID-19 dynamics in Belgium. We analyse the early stages of the SARS-CoV-2 spread, with particular attention to the pre-pandemic framework and the lockdown lifting phase initiated as of May 2020. Our results, influenced by our assumption on age-specific susceptibility and infectiousness, support the hypothesis that transmission was only influenced to a small extent by children in the age group [0,18) and adults over 60 years of age during the early phases of the pandemic and up to the end of July 2020.
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Affiliation(s)
- Leonardo Angeli
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium; Data Science Institute (DSI), Hasselt University, Hasselt, Belgium.
| | - Constantino Pereira Caetano
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Lisbon, Portugal; Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Nicolas Franco
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium; Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Steven Abrams
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium; Data Science Institute (DSI), Hasselt University, Hasselt, Belgium; Global Health Institute (GHI), Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Pietro Coletti
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium; Data Science Institute (DSI), Hasselt University, Hasselt, Belgium
| | - Inneke Van Nieuwenhuyse
- Data Science Institute (DSI), Hasselt University, Hasselt, Belgium; Computational Mathematics, Hasselt University, Hasselt, Belgium
| | - Sorin Pop
- Data Science Institute (DSI), Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium; Data Science Institute (DSI), Hasselt University, Hasselt, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio, University of Antwerp, Antwerp, Belgium
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Gonzalez-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303726. [PMID: 38496570 PMCID: PMC10942533 DOI: 10.1101/2024.03.04.24303726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain
- Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA
| | - Md Shahriar Mahmud
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
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De Gaetano A, Bajardi P, Gozzi N, Perra N, Perrotta D, Paolotti D. Behavioral Changes Associated With COVID-19 Vaccination: Cross-National Online Survey. J Med Internet Res 2023; 25:e47563. [PMID: 37906219 PMCID: PMC10646669 DOI: 10.2196/47563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/05/2023] [Accepted: 09/29/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND During the initial phases of the vaccination campaign worldwide, nonpharmaceutical interventions (NPIs) remained pivotal in the fight against the COVID-19 pandemic. In this context, it is important to understand how the arrival of vaccines affected the adoption of NPIs. Indeed, some individuals might have seen the start of mass vaccination campaigns as the end of the emergency and, as a result, relaxed their COVID-safe behaviors, facilitating the spread of the virus in a delicate epidemic phase such as the initial rollout. OBJECTIVE The aim of this study was to collect information about the possible relaxation of protective behaviors following key events of the vaccination campaign in four countries and to analyze possible associations of these behavioral tendencies with the sociodemographic characteristics of participants. METHODS We developed an online survey named "COVID-19 Prevention and Behavior Survey" that was conducted between November 26 and December 22, 2021. Participants were recruited using targeted ads on Facebook in four different countries: Brazil, Italy, South Africa, and the United Kingdom. We measured the onset of relaxation of protective measures in response to key events of the vaccination campaign, namely personal vaccination and vaccination of the most vulnerable population. Through calculation of odds ratios (ORs) and regression analysis, we assessed the strength of association between compliance with NPIs and sociodemographic characteristics of participants. RESULTS We received 2263 questionnaires from the four countries. Participants reported the most significant changes in social activities such as going to a restaurant or the cinema and visiting relatives and friends. This is in good agreement with validated psychological models of health-related behavioral change such as the Health Belief Model, according to which activities with higher costs and perceived barriers (eg, social activities) are more prone to early relaxation. Multivariate analysis using a generalized linear model showed that the two main determinants of the drop of social NPIs were (1) having previously tested positive for COVID-19 (after the second vaccine dose: OR 2.46, 95% CI 1.73-3.49) and (2) living with people at risk (after the second vaccine dose: OR 1.57, 95% CI 1.22-2.03). CONCLUSIONS This work shows that particular caution has to be taken during vaccination campaigns. Indeed, people might relax their safe behaviors regardless of the dynamics of the epidemic. For this reason, it is crucial to maintain high compliance with NPIs to avoid hindering the beneficial effects of the vaccine.
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Affiliation(s)
- Alessandro De Gaetano
- ISI Foundation, Turin, Italy
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France
| | - Paolo Bajardi
- ISI Foundation, Turin, Italy
- CENTAI Institute, Turin, Italy
| | - Nicolò Gozzi
- ISI Foundation, Turin, Italy
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
| | - Nicola Perra
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Daniela Perrotta
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
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Kremer C, Willem L, Boone J, Arrazola de Oñate W, Hammami N, Faes C, Hens N. Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021. PLoS One 2023; 18:e0292346. [PMID: 37862313 PMCID: PMC10588862 DOI: 10.1371/journal.pone.0292346] [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: 10/04/2022] [Accepted: 09/18/2023] [Indexed: 10/22/2023] Open
Abstract
The goal of tracing, testing, and quarantining contacts of infected individuals is to contain the spread of infectious diseases, a strategy widely used during the COVID-19 pandemic. However, limited research exists on the effectiveness of contact tracing, especially with regard to key performance indicators (KPIs), such as the proportion of cases arising from previously identified contacts. In our study, we analyzed contact tracing data from Belgium collected between September 2020 and December 2021 to assess the impact of contact tracing on SARS-CoV-2 transmission and understand its characteristics. Among confirmed cases involved in contact tracing in the Flemish and Brussels-Capital regions, 19.1% were previously identified as close contacts and were aware of prior exposure. These cases, referred to as 'known' to contact tracing operators, reported on average fewer close contacts compared to newly identified individuals (0.80 versus 1.05), resulting in fewer secondary cases (0.23 versus 0.28). Additionally, we calculated the secondary attack rate, representing infections per contact, which was on average lower for the 'known' cases (0.22 versus 0.25) between December 2020 and August 2021. These findings indicate the effectiveness of contact tracing in Belgium in reducing SARS-CoV-2 transmission. Although we were unable to quantify the exact number of prevented cases, our findings emphasize the importance of contact tracing as a public health measure. In addition, contact tracing data provide indications of potential shifts in transmission patterns among different age groups associated with emerging variants of concern and increasing vaccination rates.
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Affiliation(s)
- Cécile Kremer
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Jorden Boone
- KPMG Advisory, Public Sector Practice, Zaventem, Belgium
| | - Wouter Arrazola de Oñate
- Belgian Lung and Tuberculosis Association, Brussels, Belgium
- Flemish Association for Respiratory Health and Tuberculosis, Leuven, Belgium
| | - Naïma Hammami
- Department of Infectious Disease Prevention and Control, Department of Care, Flemish Region, Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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11
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Backer JA, van de Kassteele J, El Fakiri F, Hens N, Wallinga J. Contact patterns of older adults with and without frailty in the Netherlands during the COVID-19 pandemic. BMC Public Health 2023; 23:1829. [PMID: 37730628 PMCID: PMC10510272 DOI: 10.1186/s12889-023-16725-1] [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: 05/08/2023] [Accepted: 09/08/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, social distancing measures were imposed to protect the population from exposure, especially older adults and people with frailty, who have the highest risk for severe outcomes. These restrictions greatly reduced contacts in the general population, but little was known about behaviour changes among older adults and people with frailty themselves. Our aim was to quantify how COVID-19 measures affected the contact behaviour of older adults and how this differed between older adults with and without frailty. METHODS In 2021, a contact survey was carried out among people aged 70 years and older in the Netherlands. A random sample of persons per age group (70-74, 75-79, 80-84, 85-89, and 90 +) and gender was invited to participate, either during a period with stringent (April 2021) or moderate (October 2021) measures. Participants provided general information on themselves, including their frailty, and they reported characteristics of all persons with whom they had face-to-face contact on a given day over the course of a full week. RESULTS In total, 720 community-dwelling older adults were included (overall response rate of 15%), who reported 16,505 contacts. During the survey period with moderate measures, participants without frailty had significantly more contacts outside their household than participants with frailty. Especially for females, frailty was a more informative predictor of the number of contacts than age. During the survey period with stringent measures, participants with and without frailty had significantly lower numbers of contacts compared to the survey period with moderate measures. The reduction of the number of contacts was largest for the eldest participants without frailty. As they interact mostly with adults of a similar high age who are likely frail, this reduction of the number of contacts indirectly protects older adults with frailty from SARS-CoV-2 exposure. CONCLUSIONS The results of this study reveal that social distancing measures during the COVID-19 pandemic differentially affected the contact patterns of older adults with and without frailty. The reduction of contacts may have led to the direct protection of older adults in general but also to the indirect protection of older adults with frailty.
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Affiliation(s)
- Jantien A Backer
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
| | - Jan van de Kassteele
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Fatima El Fakiri
- Public Health Service of Amsterdam (GGD), Amsterdam, the Netherlands
| | - Niel Hens
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
- University of Antwerp, Vaccine and Infectious Disease Institute, Antwerp, Belgium
| | - Jacco Wallinga
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- Leiden University Medical Center, Leiden, the Netherlands
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12
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Devleesschauwer B, Willem L, Jurčević J, Smith P, Scohy A, Wyper GMA, Pires SM, Van Goethem N, Beutels P, Franco N, Abrams S, Van Cauteren D, Speybroeck N, Hens N, De Pauw R. The direct disease burden of COVID-19 in Belgium in 2020 and 2021. BMC Public Health 2023; 23:1707. [PMID: 37667264 PMCID: PMC10476343 DOI: 10.1186/s12889-023-16572-0] [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: 05/09/2023] [Accepted: 08/21/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Burden of disease estimates have become important population health metrics over the past decade to measure losses in health. In Belgium, the disease burden caused by COVID-19 has not yet been estimated, although COVID-19 has emerged as one of the most important diseases. Therefore, the current study aims to estimate the direct COVID-19 burden in Belgium, observed despite policy interventions, during 2020 and 2021, and compare it to the burden from other causes. METHODS Disability-adjusted life years (DALYs) are the sum of Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) due to disease. DALYs allow comparing the burden of disease between countries, diseases, and over time. We used the European Burden of Disease Network consensus disease model for COVID-19 to estimate DALYs related to COVID-19. Estimates of person-years for (a) acute non-fatal disease states were calculated from a compartmental model, using Belgian seroprevalence, social contact, hospital, and intensive care admission data, (b) deaths were sourced from the national COVID-19 mortality surveillance, and (c) chronic post-acute disease states were derived from a Belgian cohort study. RESULTS In 2020, the total number of COVID-19 related DALYs was estimated at 253,577 [252,541 - 254,739], which is higher than in 2021, when it was 139,281 [136,704 - 142,306]. The observed COVID-19 burden was largely borne by the elderly, and over 90% of the burden was attributable to premature mortality (i.e., YLLs). In younger people, morbidity (i.e., YLD) contributed relatively more to the DALYs, especially in 2021, when vaccination was rolled out. Morbidity was mainly attributable to long-lasting post-acute symptoms. CONCLUSION COVID-19 had a substantial impact on population health in Belgium, especially in 2020, when COVID-19 would have been the main cause of disease burden if all other causes had maintained their 2019 level.
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Affiliation(s)
- Brecht Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Lander Willem
- Department of Family Medicine and Public Health, University of Antwerp, Antwerp, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Jure Jurčević
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Pierre Smith
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Institute of Health and Society (IRSS), Université Catholique de Louvain, Brussels, Belgium
| | - Aline Scohy
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Grant M A Wyper
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | | | - Nina Van Goethem
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Nicolas Franco
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Steven Abrams
- Department of Family Medicine and Public Health, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
| | | | - Niko Speybroeck
- Institute of Health and Society, Université catholique de Louvain, Brussels, Belgium
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
| | - Robby De Pauw
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium.
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Wambua J, Loedy N, Jarvis CI, Wong KLM, Faes C, Grah R, Prasse B, Sandmann F, Niehus R, Johnson H, Edmunds W, Beutels P, Hens N, Coletti P. The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study. BMC Public Health 2023; 23:1350. [PMID: 37442987 PMCID: PMC10347859 DOI: 10.1186/s12889-023-16252-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/06/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND The SARS-CoV-2 transmission dynamics have been greatly modulated by human contact behaviour. To curb the spread of the virus, global efforts focused on implementing both Non-Pharmaceutical Interventions (NPIs) and pharmaceutical interventions such as vaccination. This study was conducted to explore the influence of COVID-19 vaccination status and risk perceptions related to SARS-CoV-2 on the number of social contacts of individuals in 16 European countries. METHODS We used data from longitudinal surveys conducted in the 16 European countries to measure social contact behaviour in the course of the pandemic. The data consisted of representative panels of participants in terms of gender, age and region of residence in each country. The surveys were conducted in several rounds between December 2020 and September 2021 and comprised of 29,292 participants providing a total of 111,103 completed surveys. We employed a multilevel generalized linear mixed effects model to explore the influence of risk perceptions and COVID-19 vaccination status on the number of social contacts of individuals. RESULTS The results indicated that perceived severity played a significant role in social contact behaviour during the pandemic after controlling for other variables (p-value < 0.001). More specifically, participants who had low or neutral levels of perceived severity reported 1.25 (95% Confidence intervals (CI) 1.13 - 1.37) and 1.10 (95% CI 1.00 - 1.21) times more contacts compared to those who perceived COVID-19 to be a serious illness, respectively. Additionally, vaccination status was also a significant predictor of contacts (p-value < 0.001), with vaccinated individuals reporting 1.31 (95% CI 1.23 - 1.39) times higher number of contacts than the non-vaccinated. Furthermore, individual-level factors played a more substantial role in influencing contact behaviour than country-level factors. CONCLUSION Our multi-country study yields significant insights on the importance of risk perceptions and vaccination in behavioral changes during a pandemic emergency. The apparent increase in social contact behaviour following vaccination would require urgent intervention in the event of emergence of an immune escaping variant.
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Affiliation(s)
- James Wambua
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Neilshan Loedy
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Kerry L. M. Wong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Helen Johnson
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
- Current Address: Health Emergency Preparedness and Response Authority (HERA), European Commission, 1049, Brussels, Belgium
| | - W.John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- The University of New South Wales, School of Public Health and Community Medicine, Sydney, NSW 2033 Australia
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
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14
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Loedy N, Coletti P, Wambua J, Hermans L, Willem L, Jarvis CI, Wong KLM, Edmunds W, Robert A, Leclerc QJ, Gimma A, Molenberghs G, Beutels P, Faes C, Hens N. Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic. BMC Public Health 2023; 23:1298. [PMID: 37415096 PMCID: PMC10326964 DOI: 10.1186/s12889-023-16193-7] [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: 12/21/2022] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences. METHODS A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. RESULTS Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula: see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. CONCLUSIONS CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.
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Affiliation(s)
- Neilshan Loedy
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - James Wambua
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lisa Hermans
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kerry L. M. Wong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Quentin J. Leclerc
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Epidemiology and Modelling of Bacterial Escape to Antimicrobials, Institut Pasteur, Paris, France
| | - Amy Gimma
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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15
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Alleman TW, Rollier M, Vergeynst J, Baetens JM. A Stochastic Mobility-Driven Spatially Explicit SEIQRD covid-19 Model with VOCs, Seasonality, and Vaccines. APPLIED MATHEMATICAL MODELLING 2023; 123:S0307-904X(23)00281-0. [PMID: 38620163 PMCID: PMC10306418 DOI: 10.1016/j.apm.2023.06.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 06/12/2023] [Accepted: 06/20/2023] [Indexed: 04/17/2024]
Abstract
In this work, we extend our previously developed compartmental SEIQRD model for sars-cov-2 in Belgium. We introduce sars-cov-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling sars-cov-2 transmission dynamics during the 2020-2021 covid-19 pandemic in Belgium. The model is geographically stratified into eleven spatial patches (provinces), and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We calibrate the model using the daily number of hospitalisations in each province and serological data. We find the model adequately describes these data, but the addition of interprovincial mobility was not necessary to obtain an accurate description of the 2020-2021 sars-cov-2 pandemic in Belgium. We further demonstrate how our model can be used to help policymakers decide on the optimal timing of the release of social restrictions.We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility.
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Affiliation(s)
- Tijs W Alleman
- BIOSPACE, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
- BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
| | - Michiel Rollier
- BIOSPACE, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
| | - Jenna Vergeynst
- BIOSPACE, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
- BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
| | - Jan M Baetens
- BIOSPACE, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent, 9000, Belgium
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Lajot A, Wambua J, Coletti P, Franco N, Brondeel R, Faes C, Hens N. How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends. BMC Infect Dis 2023; 23:410. [PMID: 37328811 DOI: 10.1186/s12879-023-08369-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.
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Affiliation(s)
- Adrien Lajot
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium.
| | - James Wambua
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Ruben Brondeel
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and infectious disease institute, University of Antwerp, Antwerp, Belgium
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17
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Dan S, Chen Y, Chen Y, Monod M, Jaeger VK, Bhatt S, Karch A, Ratmann O. Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model. PLoS Comput Biol 2023; 19:e1011191. [PMID: 37276210 DOI: 10.1371/journal.pcbi.1011191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/17/2023] [Indexed: 06/07/2023] Open
Abstract
Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.
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Affiliation(s)
- Shozen Dan
- Department of Mathematics, Imperial College London, London, England, United Kingdom
| | - Yu Chen
- Department of Mathematics, Imperial College London, London, England, United Kingdom
| | - Yining Chen
- Department of Mathematics, Imperial College London, London, England, United Kingdom
| | - Melodie Monod
- Department of Mathematics, Imperial College London, London, England, United Kingdom
| | - Veronika K Jaeger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Samir Bhatt
- School of Public Health, Imperial College London, London, England, United Kingdom
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, England, United Kingdom
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18
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Tizzani M, De Gaetano A, Jarvis CI, Gimma A, Wong K, Edmunds WJ, Beutels P, Hens N, Coletti P, Paolotti D. Impact of tiered measures on social contact and mixing patterns of in Italy during the second wave of COVID-19. BMC Public Health 2023; 23:906. [PMID: 37202734 PMCID: PMC10195658 DOI: 10.1186/s12889-023-15846-x] [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: 07/25/2022] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Most countries around the world enforced non-pharmaceutical interventions against COVID-19. Italy was one of the first countries to be affected by the pandemic, imposing a hard lockdown, in the first epidemic wave. During the second wave, the country implemented progressively restrictive tiers at the regional level according to weekly epidemiological risk assessments. This paper quantifies the impact of these restrictions on contacts and on the reproduction number. METHODS Representative (with respect to age, sex, and region of residence) longitudinal surveys of the Italian population were undertaken during the second epidemic wave. Epidemiologically relevant contact patterns were measured and compared with pre-pandemic levels and according to the level of interventions experienced by the participants. Contact matrices were used to quantify the reduction in the number of contacts by age group and contact setting. The reproduction number was estimated to evaluate the impact of restrictions on the spread of COVID-19. RESULTS The comparison with the pre-pandemic baseline shows a significant decrease in the number of contacts, independently from the age group or contact settings. This decrease in the number of contacts significantly depends on the strictness of the non-pharmaceutical interventions. For all levels of strictness considered, the reduction in social mixing results in a reproduction number smaller than one. In particular, the impact of the restriction on the number of contacts decreases with the severity of the interventions. CONCLUSIONS The progressive restriction tiers implemented in Italy reduced the reproduction number, with stricter interventions associated with higher reductions. Readily collected contact data can inform the implementation of mitigation measures at the national level in epidemic emergencies to come.
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Affiliation(s)
| | | | | | - Amy Gimma
- London School of Hygiene and Tropical Medicine, London, UK
| | - Kerry Wong
- London School of Hygiene and Tropical Medicine, London, UK
| | - W John Edmunds
- London School of Hygiene and Tropical Medicine, London, UK
| | - Philippe Beutels
- Centre for Health Economic Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Centre for Health Economic Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
| | - Pietro Coletti
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
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19
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Dorélien AM, Venkateswaran N, Deng J, Searle K, Enns E, Alarcon Espinoza G, Kulasingam S. Quantifying social contact patterns in Minnesota during stay-at-home social distancing order. BMC Infect Dis 2023; 23:324. [PMID: 37189060 PMCID: PMC10184106 DOI: 10.1186/s12879-022-07968-1] [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: 09/07/2022] [Accepted: 12/23/2022] [Indexed: 05/17/2023] Open
Abstract
SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.
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Affiliation(s)
| | | | - Jiuchen Deng
- University of Minnesota, Minneapolis, MN, 55455, USA
| | - Kelly Searle
- University of Minnesota, Minneapolis, MN, 55455, USA
| | - Eva Enns
- University of Minnesota, Minneapolis, MN, 55455, USA
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20
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Wong KLM, Gimma A, Coletti P, Faes C, Beutels P, Hens N, Jaeger VK, Karch A, Johnson H, Edmunds WJ, Jarvis CI. Social contact patterns during the COVID-19 pandemic in 21 European countries - evidence from a two-year study. BMC Infect Dis 2023; 23:268. [PMID: 37101123 PMCID: PMC10132446 DOI: 10.1186/s12879-023-08214-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 03/31/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Most countries have enacted some restrictions to reduce social contacts to slow down disease transmission during the COVID-19 pandemic. For nearly two years, individuals likely also adopted new behaviours to avoid pathogen exposure based on personal circumstances. We aimed to understand the way in which different factors affect social contacts - a critical step to improving future pandemic responses. METHODS The analysis was based on repeated cross-sectional contact survey data collected in a standardized international study from 21 European countries between March 2020 and March 2022. We calculated the mean daily contacts reported using a clustered bootstrap by country and by settings (at home, at work, or in other settings). Where data were available, contact rates during the study period were compared with rates recorded prior to the pandemic. We fitted censored individual-level generalized additive mixed models to examine the effects of various factors on the number of social contacts. RESULTS The survey recorded 463,336 observations from 96,456 participants. In all countries where comparison data were available, contact rates over the previous two years were substantially lower than those seen prior to the pandemic (approximately from over 10 to < 5), predominantly due to fewer contacts outside the home. Government restrictions imposed immediate effect on contacts, and these effects lingered after the restrictions were lifted. Across countries, the relationships between national policy, individual perceptions, or personal circumstances determining contacts varied. CONCLUSIONS Our study, coordinated at the regional level, provides important insights into the understanding of the factors associated with social contacts to support future infectious disease outbreak responses.
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Affiliation(s)
- Kerry L M Wong
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Amy Gimma
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Veronika K Jaeger
- Institute of Epidemiology and Social Medicine, University of Muenster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Andre Karch
- Institute of Epidemiology and Social Medicine, University of Muenster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Helen Johnson
- European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
| | - WJohn Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Christopher I Jarvis
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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21
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Lajot A, Cornelissen L, Van Cauteren D, Meurisse M, Brondeel R, Dupont-Gillain C. Comparing the incidence of SARS-CoV-2 across age groups considering sampling biases - use of testing data of autumn 2021 in Belgium. Arch Public Health 2023; 81:66. [PMID: 37088854 PMCID: PMC10122721 DOI: 10.1186/s13690-023-01072-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/23/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To design efficient mitigation measures against COVID-19, understanding the transmission dynamics between different age groups was crucial. The role of children in the pandemic has been intensely debated and involves both scientific and ethical questions. To design efficient age-targeted non-pharmaceutical interventions (NPI), a good view of the incidence of the different age groups was needed. However, using Belgian testing data to infer real incidence (RI) from observed incidence (OI) or positivity ratio (PR) was not trivial. METHODS Based on Belgian testing data collected during the Delta wave of Autumn 2021, we compared the use of different estimators of RI and analyzed their effect on comparisons between age groups. RESULTS We found that the RI estimator's choice strongly influences the comparison between age groups. CONCLUSION The widespread implementation of testing campaigns using representative population samples could help to avoid pitfalls related to the current testing strategy in Belgium and worldwide. This approach would also allow a better comparison of the data from different countries while reducing biases arising from the specificities of each surveillance system.
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Affiliation(s)
- Adrien Lajot
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium.
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
| | - Laura Cornelissen
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | | | - Marjan Meurisse
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Ruben Brondeel
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Christine Dupont-Gillain
- Institute of Condensed Matter and Nanosciences, Faculty of Bioscience Engineering, UCLouvain, Louvain-la-Neuve, Belgium.
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22
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Backer JA, Bogaardt L, Beutels P, Coletti P, Edmunds WJ, Gimma A, van Hagen CCE, Hens N, Jarvis CI, Vos ERA, Wambua J, Wong D, van Zandvoort K, Wallinga J. Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands. Sci Rep 2023; 13:5166. [PMID: 36997550 PMCID: PMC10060924 DOI: 10.1038/s41598-023-32031-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
The COVID-19 pandemic was in 2020 and 2021 for a large part mitigated by reducing contacts in the general population. To monitor how these contacts changed over the course of the pandemic in the Netherlands, a longitudinal survey was conducted where participants reported on their at-risk contacts every two weeks, as part of the European CoMix survey. The survey included 1659 participants from April to August 2020 and 2514 participants from December 2020 to September 2021. We categorized the number of unique contacted persons excluding household members, reported per participant per day into six activity levels, defined as 0, 1, 2, 3-4, 5-9 and 10 or more reported contacts. After correcting for age, vaccination status, risk status for severe outcome of infection, and frequency of participation, activity levels increased over time, coinciding with relaxation of COVID-19 control measures.
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Affiliation(s)
- Jantien A Backer
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
| | - Laurens Bogaardt
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Pietro Coletti
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
| | - W John Edmunds
- London School of Hygiene and Tropical Medicine, London, UK
| | - Amy Gimma
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Niel Hens
- University of Antwerp, Antwerp, Belgium
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
| | | | - Eric R A Vos
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - James Wambua
- UHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium
| | - Denise Wong
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Jacco Wallinga
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
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23
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Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities. Spat Spatiotemporal Epidemiol 2023:100568. [PMCID: PMC9904848 DOI: 10.1016/j.sste.2023.100568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.
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24
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Golding N, Price DJ, Ryan G, McVernon J, McCaw JM, Shearer FM. A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence. eLife 2023; 12:78089. [PMID: 36661303 PMCID: PMC9995112 DOI: 10.7554/elife.78089] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Against a backdrop of widespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major outbreaks, the effective reproduction number can be estimated from a time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanistic modelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 from periods of high to low - or zero - case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low - or zero - case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.
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Affiliation(s)
- Nick Golding
- Telethon Kids InstituteNedlandsAustralia
- Curtin UniversityPerthAustralia
| | - David J Price
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of MelbourneVictoriaAustralia
- Melbourne School of Population and Global Health, The University of MelbourneVictoriaAustralia
| | - Gerard Ryan
- Telethon Kids InstituteNedlandsAustralia
- Melbourne School of Population and Global Health, The University of MelbourneVictoriaAustralia
- School of Ecosystem and Forest Sciences, The University of MelbourneVictoriaAustralia
| | - Jodie McVernon
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of MelbourneVictoriaAustralia
- Melbourne School of Population and Global Health, The University of MelbourneVictoriaAustralia
- Murdoch Childrens Research Institute, The Royal Children’s HospitalVictoriaAustralia
| | - James M McCaw
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of MelbourneVictoriaAustralia
- Melbourne School of Population and Global Health, The University of MelbourneVictoriaAustralia
- School of Mathematics and Statistics, The University of MelbourneVictoriaAustralia
| | - Freya M Shearer
- Telethon Kids InstituteNedlandsAustralia
- Melbourne School of Population and Global Health, The University of MelbourneVictoriaAustralia
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25
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Liang Y, Peng C, You Q, Litvinova M, Ajelli M, Zhang J, Yu H. Estimating Changes in Contact Patterns in China Over the First Year of the COVID-19 Pandemic: Implications for SARS-CoV-2 Spread — Four Cities, China, 2020. China CDC Wkly 2023; 5:113-119. [PMID: 37006711 PMCID: PMC10061775 DOI: 10.46234/ccdcw2023.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Previous studies have demonstrated significant changes in social contacts during the first-wave coronavirus disease 2019 (COVID-19) in Chinese mainland. The purpose of this study was to quantify the time-varying contact patterns by age in Chinese mainland in 2020 and evaluate their impact on the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods Diary-based contact surveys were performed for four periods: baseline (prior to 2020), outbreak (February 2020), post-lockdown (March-May 2020), and post-epidemic (September-November 2020). We built a Susceptible-Infected-Recovered (SIR) model to evaluate the effect of reducing contacts on transmission. Results During the post-epidemic period, daily contacts resumed to 26.7%, 14.8%, 46.8%, and 44.2% of the pre-COVID levels in Wuhan, Shanghai, Shenzhen, and Changsha, respectively. This suggests a moderate risk of resurgence in Changsha, Shenzhen, and Wuhan, and a low risk in Shanghai. School closure alone was not enough to interrupt transmission of SARS-CoV-2 Omicron BA.5, but with the addition of a 75% reduction of contacts at the workplace, it could lead to a 16.8% reduction of the attack rate. To control an outbreak, concerted strategies that target schools, workplaces, and community contacts are needed. Discussion Monitoring contact patterns by age is key to quantifying the risk of COVID-19 outbreaks and evaluating the impact of intervention strategies.
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Affiliation(s)
- Yuxia Liang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
| | - Cheng Peng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
| | - Qian You
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
| | - Maria Litvinova
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
- Juanjuan Zhang,
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai Municipality, China
- Hongjie Yu,
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26
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Bridgen JR, Jewell C, Read JM. Social mixing patterns in the UK following the relaxation of COVID-19 pandemic restrictions, July-August 2020: a cross-sectional online survey. BMJ Open 2022; 12:e059231. [PMID: 36523221 PMCID: PMC9748508 DOI: 10.1136/bmjopen-2021-059231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To quantify and characterise non-household contact and to identify the effect of shielding and isolating on contact patterns. DESIGN Cross-sectional study. SETTING AND PARTICIPANTS Anyone living in the UK was eligible to take part in the study. We recorded 5143 responses to the online questionnaire between 28 July 2020 and 14 August 2020. OUTCOME MEASURES Our primary outcome was the daily non-household contact rate of participants. Secondary outcomes were propensity to leave home over a 7 day period, whether contacts had occurred indoors or outdoors locations visited, the furthest distance travelled from home, ability to socially distance and membership of support bubble. RESULTS The mean rate of non-household contacts per person was 2.9 d-1. Participants attending a workplace (adjusted incidence rate ratio (aIRR) 3.33, 95% CI 3.02 to 3.66), self-employed (aIRR 1.63, 95% CI 1.43 to 1.87) or working in healthcare (aIRR 5.10, 95% CI 4.29 to 6.10) reported significantly higher non-household contact rates than those working from home. Participants self-isolating as a precaution or following Test and Trace instructions had a lower non-household contact rate than those not self-isolating (aIRR 0.58, 95% CI 0.43 to 0.79). We found limited evidence that those shielding had reduced non-household contacts compared with non-shielders. CONCLUSION The daily rate of non-household interactions remained lower than prepandemic levels measured by other studies, suggesting continued adherence to social distancing guidelines. Individuals attending a workplace in-person or employed as healthcare professionals were less likely to maintain social distance and had a higher non-household contact rate, possibly increasing their infection risk. Shielding and self-isolating individuals required greater support to enable them to follow the government guidelines and reduce non-household contact and therefore their risk of infection.
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Affiliation(s)
- Jessica Re Bridgen
- Lancaster Medical School, Lancaster University Faculty of Health and Medicine, Lancaster, UK
| | - Chris Jewell
- Lancaster Medical School, Lancaster University Faculty of Health and Medicine, Lancaster, UK
| | - Jonathan M Read
- Lancaster Medical School, Lancaster University Faculty of Health and Medicine, Lancaster, UK
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27
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Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States. PLoS Comput Biol 2022; 18:e1010742. [PMID: 36459512 DOI: 10.1371/journal.pcbi.1010742] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 12/14/2022] [Accepted: 11/16/2022] [Indexed: 12/04/2022] Open
Abstract
Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.
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28
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Liu CY, Smith S, Chamberlain AT, Gandhi NR, Khan F, Williams S, Shah S. Use of surveillance data to elucidate household clustering of SARS-CoV-2 in Fulton County, Georgia a major metropolitan area. Ann Epidemiol 2022; 76:121-127. [PMID: 36210009 PMCID: PMC9536872 DOI: 10.1016/j.annepidem.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Households are important for SARS-CoV-2 transmission due to high intensity exposure in enclosed spaces over prolonged durations. We quantified and characterized household clustering of COVID-19 cases in Fulton County, Georgia. METHODS We used surveillance data to identify all confirmed COVID-19 cases in Fulton County. Household clustered cases were defined as cases with matching residential address. We described the proportion of COVID-19 cases that were clustered, stratified by age over time and explore trends in age of first diagnosed case within households and subsequent household cases. RESULTS Between June 1, 2020 and October 31, 2021, 31,449(37%) of 106,233 cases were clustered in households. Children were the most likely to be in household clusters than any other age group. Initially, children were rarely (∼ 10%) the first cases diagnosed in the household but increased to almost 1 of 3 in later periods. DISCUSSION One-third of COVID-19 cases in Fulton County were part of a household cluster. Increasingly children were the first diagnosed case, coinciding with temporal trends in vaccine roll-out among the elderly and the return to in-person schooling in Fall 2021. Limitations include restrictions to cases with a valid address and unit number and that the first diagnosed case may not be the infection source for the household.
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Affiliation(s)
- Carol Y Liu
- Emory University Rollins School of Public Health, Atlanta, GA.
| | | | | | - Neel R Gandhi
- Emory University Rollins School of Public Health, Atlanta, GA; Emory School of Medicine, Atlanta, GA
| | - Fazle Khan
- Fulton County Board of Health, Atlanta, GA
| | | | - Sarita Shah
- Emory University Rollins School of Public Health, Atlanta, GA; Emory School of Medicine, Atlanta, GA
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29
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Reichmuth ML, Hodcroft EB, Riou J, Neher RA, Hens N, Althaus CL. Impact of cross-border-associated cases on the SARS-CoV-2 epidemic in Switzerland during summer 2020 and 2021. Epidemics 2022; 41:100654. [PMID: 36444785 PMCID: PMC9671612 DOI: 10.1016/j.epidem.2022.100654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/01/2022] [Accepted: 11/12/2022] [Indexed: 11/18/2022] Open
Abstract
During the summers of 2020 and 2021, the number of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Switzerland remained at relatively low levels, but grew steadily over time. It remains unclear to what extent epidemic growth during these periods was a result of the relaxation of local control measures or increased traveling and subsequent importation of cases. A better understanding of the role of cross-border-associated cases (imports) on the local epidemic dynamics will help to inform future surveillance strategies. We analyzed routine surveillance data of confirmed cases of SARS-CoV-2 in Switzerland from 1 June to 30 September 2020 and 2021. We used a stochastic branching process model that accounts for superspreading of SARS-CoV-2 to simulate epidemic trajectories in absence and in presence of imports during summer 2020 and 2021. The Swiss Federal Office of Public Health reported 22,919 and 145,840 confirmed cases of SARS-CoV-2 from 1 June to 30 September 2020 and 2021, respectively. Among cases with known place of exposure, 27% (3,276 of 12,088) and 25% (1,110 of 4,368) reported an exposure abroad in 2020 and 2021, respectively. Without considering the impact of imported cases, the steady growth of confirmed cases during summer periods would be consistent with a value of Re that is significantly above the critical threshold of 1. In contrast, we estimated Re at 0.84 (95% credible interval, CrI: 0.78-0.90) in 2020 and 0.82 (95% CrI: 0.74-0.90) in 2021 when imported cases were taken into account, indicating that the local Re was below the critical threshold of 1 during summer. In Switzerland, cross-border-associated SARS-CoV-2 cases had a considerable impact on the local transmission dynamics and can explain the steady growth of the epidemic during the summers of 2020 and 2021.
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Affiliation(s)
- Martina L. Reichmuth
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Correspondence to: Institute of Social and Preventive Medicine, University of Bern, Mittelstrasse 43, CH-3012 Bern, Switzerland
| | - Emma B. Hodcroft
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Federal Office of Public Health, Liebefeld, Switzerland
| | - Richard A. Neher
- Swiss Institute of Bioinformatics, Lausanne, Switzerland,Biozentrum, University of Basel, Basel, Switzerland
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium,Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christian L. Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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30
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Banholzer N, Lison A, Özcelik D, Stadler T, Feuerriegel S, Vach W. The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review. Eur J Epidemiol 2022; 37:1003-1024. [PMID: 36152133 PMCID: PMC9510554 DOI: 10.1007/s10654-022-00908-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.
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Affiliation(s)
- Nicolas Banholzer
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
| | - Dennis Özcelik
- Chemistry | Biology | Pharmacy Information Center, ETH Zurich, Zurich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- LMU Munich School of Management, LMU Munich, Munich, Germany
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland
- Department of Environmental Sciences, University of Basel, Basel, Switzerland
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31
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Adu PA, Binka M, Mahmood B, Jeong D, Buller-Taylor T, Damascene MJ, Iyaniwura S, Ringa N, Velásquez García HA, Wong S, Yu A, Bartlett S, Wilton J, Irvine MA, Otterstatter M, Janjua NZ. Cohort profile: the British Columbia COVID-19 Population Mixing Patterns Survey (BC-Mix). BMJ Open 2022; 12:e056615. [PMID: 36002217 PMCID: PMC9412046 DOI: 10.1136/bmjopen-2021-056615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Several non-pharmaceutical interventions, such as physical distancing, handwashing, self-isolation, and school and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of COVID-19 on 26 January 2020, to minimise in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns Survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents. PARTICIPANTS The ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow-up survey is sent to participants 2-4 weeks after completing the baseline survey. Survey responses are weighted to BC's population by age, sex, geography and ethnicity to obtain generalisable estimates. Additional indices such as the Material and Social Deprivation Index, residential instability, economic dependency, and others are generated using census and location data. FINDINGS TO DATE As of 26 July 2021, over 61 000 baseline survey responses were received of which 41 375 were eligible for analysis. Of the eligible participants, about 60% consented to follow-up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 83.5% of respondents were female, 58.7% were 55 years or older, 87.5% identified as white and 45.9% had at least a university degree. After weighting, approximately 50% were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree. FUTURE PLANS Multiple papers describing contact patterns, physical distancing measures, regular handwashing and facemask wearing, modelling looking at impact of physical distancing measures and vaccine acceptance, hesitancy and uptake are either in progress or have been published.
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Affiliation(s)
- Prince A Adu
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Mawuena Binka
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Bushra Mahmood
- Department of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Dahn Jeong
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Makuza Jean Damascene
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarafa Iyaniwura
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Mathematics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Notice Ringa
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Héctor A Velásquez García
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Stanley Wong
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Amanda Yu
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Sofia Bartlett
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - James Wilton
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Mike A Irvine
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Otterstatter
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Naveed Zafar Janjua
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation & Outcome Sciences, St. Paul's Hospital, Vancouver, British Columbia, Canada
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32
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Kuylen EJ, Torneri A, Willem L, Libin PJK, Abrams S, Coletti P, Franco N, Verelst F, Beutels P, Liesenborgs J, Hens N. Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2. PLoS Comput Biol 2022; 18:e1009980. [PMID: 35994497 PMCID: PMC9436127 DOI: 10.1371/journal.pcbi.1009980] [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: 03/01/2022] [Revised: 09/01/2022] [Accepted: 06/29/2022] [Indexed: 11/18/2022] Open
Abstract
Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes—with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity. To investigate the effect of different sources of superspreading on disease dynamics, we implemented superspreading driven by heterogeneity in infectiousness and heterogeneity in contact behavior into an individual-based model for the transmission of SARS-CoV-2 in the Belgian population. We compared the impact of both forms of superspreading in a scenario without interventions as well as in a scenario in which a period of strict social distancing (i.e. a lockdown) is followed by a period of partial release. We found that both forms of superspreading have very different effects. On the one hand, increasing the level of infectiousness-related heterogeneity led to less outbreaks being observed following the introduction of one infected individual in the population. Furthermore, final outbreak sizes decreased, and outbreaks became slower, with lower and later peaks, and a lower herd immunity threshold. Finally, the risk for resurgence of an outbreak following a period of lockdown also decreased. On the other hand, when contact-related heterogeneity was high, this also led to smaller final sizes, but caused outbreaks to be more explosive regarding other aspects (such as higher peaks that occurred earlier). The herd immunity threshold also increased, as did the risk of resurgence of outbreaks.
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Affiliation(s)
- Elise J. Kuylen
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- * E-mail:
| | - Andrea Torneri
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Lander Willem
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Pieter J. K. Libin
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - Steven Abrams
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Global Health Institute, University of Antwerp, Antwerp, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems, Department of Mathematics, University of Namur, Namur, Belgium
| | - Frederik Verelst
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW, Australia
| | - Jori Liesenborgs
- Expertise Centre for Digital Media, Hasselt University - transnational University Limburg, Hasselt, Belgium
| | - Niel Hens
- Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
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33
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Nielsen BF, Li Y, Sneppen K, Simonsen L, Viboud C, Levin SA, Grenfell BT. Immune Heterogeneity and Epistasis Explain Punctuated Evolution of SARS-CoV-2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.27.22278129. [PMID: 35982659 PMCID: PMC9387145 DOI: 10.1101/2022.07.27.22278129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
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Affiliation(s)
- Bjarke Frost Nielsen
- PandemiX Center, Roskilde University
- Niels Bohr Institute, University of Copenhagen
| | - Yimei Li
- Department of Ecology & Evolutionary Biology, Princeton University
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health
| | - Simon A. Levin
- Department of Ecology & Evolutionary Biology, Princeton University
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Nelson KN, Siegler AJ, Sullivan PS, Bradley H, Hall E, Luisi N, Hipp-Ramsey P, Sanchez T, Shioda K, Lopman BA. Nationally representative social contact patterns among U.S. adults, August 2020-April 2021. Epidemics 2022; 40:100605. [PMID: 35810698 PMCID: PMC9242729 DOI: 10.1016/j.epidem.2022.100605] [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: 09/17/2021] [Revised: 06/14/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic, few have reported differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.
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Affiliation(s)
- Kristin N Nelson
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA.
| | - Aaron J Siegler
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA
| | - Patrick S Sullivan
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA
| | - Heather Bradley
- Department of Population Health Sciences, Georgia State University School of Public Health, USA
| | - Eric Hall
- School of Public Health, Oregon Health & Science University, USA
| | - Nicole Luisi
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA
| | - Palmer Hipp-Ramsey
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA
| | - Travis Sanchez
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA
| | - Kayoko Shioda
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, USA
| | - Benjamin A Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University, USA; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, USA
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35
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Miranda MNS, Pingarilho M, Pimentel V, Torneri A, Seabra SG, Libin PJK, Abecasis AB. A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2. Front Microbiol 2022; 13:889643. [PMID: 35722303 PMCID: PMC9201468 DOI: 10.3389/fmicb.2022.889643] [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: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.
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Affiliation(s)
- Mafalda N S Miranda
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Marta Pingarilho
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Victor Pimentel
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Andrea Torneri
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sofia G Seabra
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Pieter J K Libin
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium.,Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.,Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven, University of Leuven, Leuven, Belgium
| | - Ana B Abecasis
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
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36
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Smith LE, Potts HWW, Amlȏt R, Fear NT, Michie S, Rubin GJ. Patterns of social mixing in England changed in line with restrictions during the COVID-19 pandemic (September 2020 to April 2022). Sci Rep 2022; 12:10436. [PMID: 35729196 PMCID: PMC9212204 DOI: 10.1038/s41598-022-14431-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 06/07/2022] [Indexed: 11/20/2022] Open
Abstract
Social mixing contributes to the transmission of SARS-CoV-2. We developed a composite measure for risky social mixing, investigating changes during the pandemic and factors associated with risky mixing. Forty-five waves of online cross-sectional surveys were used (n = 78,917 responses; 14 September 2020 to 13 April 2022). We investigated socio-demographic, contextual and psychological factors associated with engaging in highest risk social mixing in England at seven timepoints. Patterns of social mixing varied over time, broadly in line with changes in restrictions. Engaging in highest risk social mixing was associated with being younger, less worried about COVID-19, perceiving a lower risk of COVID-19, perceiving COVID-19 to be a less severe illness, thinking the risks of COVID-19 were being exaggerated, not agreeing that one’s personal behaviour had an impact on how COVID-19 spreads, and not agreeing that information from the UK Government about COVID-19 can be trusted. Our composite measure for risky social mixing varied in line with restrictions in place at the time of data collection, providing some validation of the measure. While messages targeting psychological factors may reduce higher risk social mixing, achieving a large change in risky social mixing in a short space of time may necessitate a reimposition of restrictions.
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Affiliation(s)
- Louise E Smith
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. .,NIHR Health Protection Research Unit in Emergency Preparedness and Response, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK. .,Department of Psychological Medicine, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK.
| | - Henry W W Potts
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
| | - Richard Amlȏt
- NIHR Health Protection Research Unit in Emergency Preparedness and Response, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK.,Behavioural Science and Insights Unit, UK Health Security Agency, Porton Down, Wiltshire, Salisbury, SP4 0JG, UK
| | - Nicola T Fear
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,King's Centre for Military Health Research and Academic Department of Military Mental Health, King's College London, London, UK.,Department of Psychological Medicine, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - G James Rubin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Health Protection Research Unit in Emergency Preparedness and Response, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK.,Department of Psychological Medicine, Weston Education Centre, King's College London, Cutcombe Road, London, SE5 9RJ, UK
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37
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Godbout A, Drolet M, Mondor M, Simard M, Sauvageau C, De Serres G, Brisson M. Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic. BMC Med 2022; 20:199. [PMID: 35606803 PMCID: PMC9126104 DOI: 10.1186/s12916-022-02398-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 05/09/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND As we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada. METHODS We used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 m or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance. RESULTS A total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9-7.3) before the pandemic to 3.2 (95%CI 2.5-3.9) during the first wave among individuals with comorbidities and from 8.1 (95%CI 7.3-9.0) to 2.7 (95%CI 2.2-3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5-3.2) vs 3.9 (95%CI 3.5-4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4-4.7) and without comorbidities (4.5, 95%CI 4.1-4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities. CONCLUSIONS It will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).
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Affiliation(s)
- Aurélie Godbout
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.,Laval University, Québec, Canada
| | - Mélanie Drolet
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada
| | - Myrto Mondor
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada
| | - Marc Simard
- Institut National de Santé Publique du Québec, Québec, Canada
| | - Chantal Sauvageau
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.,Laval University, Québec, Canada.,Institut National de Santé Publique du Québec, Québec, Canada
| | - Gaston De Serres
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.,Laval University, Québec, Canada.,Institut National de Santé Publique du Québec, Québec, Canada
| | - Marc Brisson
- Centre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada. .,Laval University, Québec, Canada. .,MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
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Drolet M, Godbout A, Mondor M, Béraud G, Drolet-Roy L, Lemieux-Mellouki P, Bureau A, Demers É, Boily MC, Sauvageau C, De Serres G, Hens N, Beutels P, Dervaux B, Brisson M. Time trends in social contacts before and during the COVID-19 pandemic: the CONNECT study. BMC Public Health 2022; 22:1032. [PMID: 35606703 PMCID: PMC9125550 DOI: 10.1186/s12889-022-13402-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/04/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Since the beginning of the COVID-19 pandemic, many countries, including Canada, have adopted unprecedented physical distancing measures such as closure of schools and non-essential businesses, and restrictions on gatherings and household visits. We described time trends in social contacts for the pre-pandemic and pandemic periods in Quebec, Canada. METHODS CONNECT is a population-based study of social contacts conducted shortly before (2018/2019) and during the COVID-19 pandemic (April 2020 - February 2021), using the same methodology for both periods. We recruited participants by random digit dialing and collected data by self-administered web-based questionnaires. Questionnaires documented socio-demographic characteristics and social contacts for two assigned days. A contact was defined as a two-way conversation at a distance ≤ 2 m or as a physical contact, irrespective of masking. We used weighted generalized linear models with a Poisson distribution and robust variance (taking possible overdispersion into account) to compare the mean number of social contacts over time and by socio-demographic characteristics. RESULTS A total of 1291 and 5516 Quebecers completed the study before and during the pandemic, respectively. Contacts significantly decreased from a mean of 8 contacts/day prior to the pandemic to 3 contacts/day during the spring 2020 lockdown. Contacts remained lower than the pre-COVID period thereafter (lowest = 3 contacts/day during the Christmas 2020/2021 holidays, highest = 5 in September 2020). Contacts at work, during leisure activities/in other locations, and at home with visitors showed the greatest decreases since the beginning of the pandemic. All sociodemographic subgroups showed significant decreases of contacts since the beginning of the pandemic. The mixing matrices illustrated the impact of public health measures (e.g. school closure, gathering restrictions) with fewer contacts between children/teenagers and fewer contacts outside of the three main diagonals of contacts between same-age partners/siblings and between children and their parents. CONCLUSION Physical distancing measures in Quebec significantly decreased social contacts, which most likely mitigated the spread of COVID-19.
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Affiliation(s)
- Mélanie Drolet
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
| | - Aurélie Godbout
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
- Laval University, Québec, Québec, Canada
| | - Myrto Mondor
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
| | - Guillaume Béraud
- Department of Infectious Diseases, Centre Hospitalier Universitaire de Poitiers, 86021, Poitiers, France
| | - Léa Drolet-Roy
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
| | - Philippe Lemieux-Mellouki
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
- Laval University, Québec, Québec, Canada
| | - Alexandre Bureau
- Laval University, Québec, Québec, Canada
- CERVO Brain Research Center, Centre Intégré Universitaire de Santé Et de Services Sociaux de La Capitale-Nationale, Québec, QC, Canada
| | - Éric Demers
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
| | - Marie-Claude Boily
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Chantal Sauvageau
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
- Laval University, Québec, Québec, Canada
- Institut National de Santé Publique du Québec, Québec, Québec, Canada
| | - Gaston De Serres
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada
- Laval University, Québec, Québec, Canada
- Institut National de Santé Publique du Québec, Québec, Québec, Canada
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health, University of New South Wales, Sydney, Australia
| | - Benoit Dervaux
- Institut Pasteur U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Univ Lille, Inserm, CHU Lille, 59000, Lille, France
| | - Marc Brisson
- Centre de Recherche du CHU de Québec - Université Laval, Québec, Québec, Canada.
- Laval University, Québec, Québec, Canada.
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
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Ringa N, Iyaniwura SA, David S, Irvine MA, Adu P, Spencer M, Janjua NZ, Otterstatter MC. Social Contacts and Transmission of COVID-19 in British Columbia, Canada. Front Public Health 2022; 10:867425. [PMID: 35592086 PMCID: PMC9110764 DOI: 10.3389/fpubh.2022.867425] [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: 02/01/2022] [Accepted: 03/25/2022] [Indexed: 01/08/2023] Open
Abstract
Background Close-contact rates are thought to be a driving force behind the transmission of many infectious respiratory diseases. Yet, contact rates and their relation to transmission and the impact of control measures, are seldom quantified. We quantify the response of contact rates, reported cases and transmission of COVID-19, to public health contact-restriction orders, and examine the associations among these three variables in the province of British Columbia, Canada. Methods We derived time series data for contact rates, daily cases and transmission of COVID-19 from a social contacts survey, reported case counts and by fitting a transmission model to reported cases, respectively. We used segmented regression to investigate impacts of public health orders; Pearson correlation to determine associations between contact rates and transmission; and vector autoregressive modeling to quantify lagged associations between contacts rates, daily cases, and transmission. Results Declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in cases showed a reporting delay of about 2 weeks. Contact rates were a significant driver of COVID-19 and explained roughly 19 and 20% of the variation in new cases and transmission, respectively. Interestingly, increases in COVID-19 transmission and cases were followed by reduced contact rates: overall, daily cases explained about 10% of the variation in subsequent contact rates. Conclusion We showed that close-contact rates were a significant time-series driver of transmission and ultimately of reported cases of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest possible behavioral feedback, by which increased reported cases lead to reduced subsequent contact rates. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.
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Affiliation(s)
- Notice Ringa
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Sarafa A. Iyaniwura
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- Department of Mathematics, Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada
| | - Samara David
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Mike A. Irvine
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Prince Adu
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michelle Spencer
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Naveed Z. Janjua
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michael C. Otterstatter
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
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40
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Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data. PLoS Comput Biol 2022; 18:e1009965. [PMID: 35353810 PMCID: PMC9000131 DOI: 10.1371/journal.pcbi.1009965] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 04/11/2022] [Accepted: 02/24/2022] [Indexed: 11/19/2022] Open
Abstract
Several important aspects related to SARS-CoV-2 transmission are not well known due to a lack of appropriate data. However, mathematical and computational tools can be used to extract part of this information from the available data, like some hidden age-related characteristics. In this paper, we present a method to investigate age-specific differences in transmission parameters related to susceptibility to and infectiousness upon contracting SARS-CoV-2 infection. More specifically, we use panel-based social contact data from diary-based surveys conducted in Belgium combined with the next generation principle to infer the relative incidence and we compare this to real-life incidence data. Comparing these two allows for the estimation of age-specific transmission parameters. Our analysis implies the susceptibility in children to be around half of the susceptibility in adults, and even lower for very young children (preschooler). However, the probability of adults and the elderly to contract the infection is decreasing throughout the vaccination campaign, thereby modifying the picture over time.
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Nelson KN, Siegler AJ, Sullivan PS, Bradley H, Hall E, Luisi N, Hipp-Ramsey P, Sanchez T, Shioda K, Lopman BA. Nationally Representative Social Contact Patterns among U.S. adults, August 2020-April 2021. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.09.22.21263904. [PMID: 35378746 PMCID: PMC8978954 DOI: 10.1101/2021.09.22.21263904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic., few have reported on differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.
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Affiliation(s)
- Kristin N Nelson
- Department of Epidemiology, Rollins School of Public Health, Emory University
| | - Aaron J Siegler
- Department of Epidemiology, Rollins School of Public Health, Emory University
| | - Patrick S Sullivan
- Department of Epidemiology, Rollins School of Public Health, Emory University
| | - Heather Bradley
- Department of Population Health Sciences, Georgia State University School of Public Health
| | - Eric Hall
- School of Public Health, Oregon Health & Science University
| | - Nicole Luisi
- Department of Epidemiology, Rollins School of Public Health, Emory University
| | | | - Travis Sanchez
- Department of Epidemiology, Rollins School of Public Health, Emory University
| | - Kayoko Shioda
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University
| | - Benjamin A Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University
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The influence of risk perceptions on close contact frequency during the SARS-CoV-2 pandemic. Sci Rep 2022; 12:5192. [PMID: 35338202 PMCID: PMC8951651 DOI: 10.1038/s41598-022-09037-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/16/2022] [Indexed: 12/19/2022] Open
Abstract
Human behaviour is known to be crucial in the propagation of infectious diseases through respiratory or close-contact routes like the current SARS-CoV-2 virus. Intervention measures implemented to curb the spread of the virus mainly aim at limiting the number of close contacts, until vaccine roll-out is complete. Our main objective was to assess the relationships between SARS-CoV-2 perceptions and social contact behaviour in Belgium. Understanding these relationships is crucial to maximize interventions’ effectiveness, e.g. by tailoring public health communication campaigns. In this study, we surveyed a representative sample of adults in Belgium in two longitudinal surveys (survey 1 in April 2020 to August 2020, and survey 2 in November 2020 to April 2021). Generalized linear mixed effects models were used to analyse the two surveys. Participants with low and neutral perceptions on perceived severity made a significantly higher number of social contacts as compared to participants with high levels of perceived severity after controlling for other variables. Our results highlight the key role of perceived severity on social contact behaviour during a pandemic. Nevertheless, additional research is required to investigate the impact of public health communication on severity of COVID-19 in terms of changes in social contact behaviour.
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Wikle NB, Tran TNA, Gentilesco B, Leighow SM, Albert E, Strong ER, Brinda K, Inam H, Yang F, Hossain S, Chan P, Hanage WP, Messick M, Pritchard JR, Hanks EM, Boni MF. SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics. SCIENCE ADVANCES 2022; 8:eabf9868. [PMID: 35080987 PMCID: PMC8791616 DOI: 10.1126/sciadv.abf9868] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/03/2021] [Indexed: 05/03/2023]
Abstract
State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states-Rhode Island, Massachusetts, and Pennsylvania-that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.
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Affiliation(s)
- Nathan B. Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | | | - Scott M. Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park, PA, USA
| | - Emily R. Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Karel Brinda
- Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Sajid Hossain
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Philip Chan
- Department of Medicine, Brown University, Providence, RI, USA
| | - William P. Hanage
- Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Maria Messick
- Rhode Island Office of the Governor and Rhode Island Department of Health, Providence, RI, USA
| | - Justin R. Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Ephraim M. Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Maciej F. Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Luyten J, Schokkaert E. Belgium's response to the COVID-19 pandemic. HEALTH ECONOMICS, POLICY, AND LAW 2022; 17:37-47. [PMID: 34219632 PMCID: PMC8280466 DOI: 10.1017/s1744133121000232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 05/27/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022]
Abstract
Belgium is often seen as an outlier in the international experience with the coronavirus disease 2019. We summarize the unfolding of the pandemic in Belgium from February to December 2020, discuss the countermeasures that were implemented and provide some explanations why the numbers indicate a stronger pandemic in Belgium than in its neighbouring countries. To some extent, the seemingly poor performance of Belgium is a measurement artefact. Yet, there were indeed particular factors in Belgium that unnecessarily increased the toll of the pandemic. In the first wave insufficient priority was given to protect care homes. The second wave was larger than necessary due to a failure to timely implement restrictive measures. The latter can, at least partly, be explained by a unique political situation: a temporary, minority government in the middle of a major crisis.
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Affiliation(s)
- Jeroen Luyten
- Leuven Institute for Healthcare Policy, University of Leuven, Leuven, Belgium
| | - Erik Schokkaert
- Department of Economics, University of Leuven, Naamsestraat 69, B-3000Leuven, Belgium
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Paoluzzi M, Gnan N, Grassi F, Salvetti M, Vanacore N, Crisanti A. A single-agent extension of the SIR model describes the impact of mobility restrictions on the COVID-19 epidemic. Sci Rep 2021; 11:24467. [PMID: 34963680 PMCID: PMC8714823 DOI: 10.1038/s41598-021-03721-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 12/07/2021] [Indexed: 12/04/2022] Open
Abstract
Mobility restrictions are successfully used to contain the diffusion of epidemics. In this work we explore their effect on the epidemic growth by investigating an extension of the Susceptible-Infected-Removed (SIR) model in which individual mobility is taken into account. In the model individual agents move on a chessboard with a Lévy walk and, within each square, epidemic spreading follows the standard SIR model. These simple rules allow to reproduce the sub-exponential growth of the epidemic evolution observed during the Covid-19 epidemic waves in several countries and which cannot be captured by the standard SIR model. We show that we can tune the slowing-down of the epidemic spreading by changing the dynamics of the agents from Lévy to Brownian and we investigate how the interplay among different containment strategies mitigate the epidemic spreading. Finally we demonstrate that we can reproduce the epidemic evolution of the first and second COVID-19 waves in Italy using only 3 parameters, i.e , the infection rate, the removing rate, and the mobility in the country. We provide an estimate of the peak reduction due to imposed mobility restrictions, i. e., the so-called flattening the curve effect. Although based on few ingredients, the model captures the kinetic of the epidemic waves, returning mobility values that are consistent with a lock-down intervention during the first wave and milder limitations, associated to a weaker peak reduction, during the second wave.
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Affiliation(s)
- Matteo Paoluzzi
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, C. Martí Franquès 1, 08028, Barcelona, Spain.
| | - Nicoletta Gnan
- CNR-ISC, Institute for Complex Systems UOS "Sapienza", Piazzale A. Moro 2, 00185, Rome, Italy
- Department of Physics, Sapienza University of Rome, Rome, Italy
| | - Francesca Grassi
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
- IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Nicola Vanacore
- National Center for Disease Prevention and Health Promotion, Istituto Superiore di Sanità, Rome, Italy
| | - Andrea Crisanti
- CNR-ISC, Institute for Complex Systems UOS "Sapienza", Piazzale A. Moro 2, 00185, Rome, Italy
- Department of Physics, Sapienza University of Rome, Rome, Italy
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Chinnery PF, Pearce JJ, Kinsey AM, Jenkinson JM, Wells G, Watt FM. How COVID-19 has changed medical research funding. Interface Focus 2021; 11:20210025. [PMID: 34956595 PMCID: PMC8504879 DOI: 10.1098/rsfs.2021.0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 12/15/2022] Open
Abstract
Here, we consider how the lessons we learned in 2020 from funding COVID-19 research could have a long-term impact on the way that we fund medical research. We look back at how UK government funding for COVID-19 medical research evolved, beginning with the early calls for proposals in February that pump-primed funding for vaccines and therapeutics, and culminating in the launch of the government's National Core Studies programme in October. We discuss how the research community mobilized to submit and review grants more rapidly than ever before, against a background of laboratory and office closures. We also highlight the challenges of running clinical trials as the number of hospitalized patients fluctuated with different waves of the disease.
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Affiliation(s)
| | - Jonathan J Pearce
- Medical Research Council, 58 Victoria Embankment, London E4Y 0DS, UK
| | - Anna M Kinsey
- Medical Research Council, 58 Victoria Embankment, London E4Y 0DS, UK
| | | | - Glenn Wells
- Medical Research Council, 58 Victoria Embankment, London E4Y 0DS, UK
| | - Fiona M Watt
- Medical Research Council, 58 Victoria Embankment, London E4Y 0DS, UK
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Brankston G, Merkley E, Fisman DN, Tuite AR, Poljak Z, Loewen PJ, Greer AL. Quantifying contact patterns in response to COVID-19 public health measures in Canada. BMC Public Health 2021; 21:2040. [PMID: 34749676 PMCID: PMC8574152 DOI: 10.1186/s12889-021-12080-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 10/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A variety of public health measures have been implemented during the COVID-19 pandemic in Canada to reduce contact between individuals. The objective of this study was to provide empirical contact pattern data to evaluate the impact of public health measures, the degree to which social contacts rebounded to normal levels, as well as direct public health efforts toward age- and location-specific settings. METHODS Four population-based cross-sectional surveys were administered to members of a paid panel representative of Canadian adults by age, gender, official language, and region of residence during May (Survey 1), July (Survey 2), September (Survey 3), and December (Survey 4) 2020. A total of 4981 (Survey 1), 2493 (Survey 2), 2495 (Survey 3), and 2491 (Survey 4) respondents provided information about the age and setting for each direct contact made in a 24-h period. Contact matrices were constructed and contacts for those under the age of 18 years imputed. The next generation matrix approach was used to estimate the reproduction number (Rt) for each survey. Respondents with children under 18 years estimated the number of contacts their children made in school and extracurricular settings. RESULTS Estimated Rt values were 0.49 (95% CI: 0.29-0.69) for May, 0.48 (95% CI: 0.29-0.68) for July, 1.06 (95% CI: 0.63-1.52) for September, and 0.81 (0.47-1.17) for December. The highest proportion of reported contacts occurred within the home (51.3% in May), in 'other' locations (49.2% in July) and at work (66.3 and 65.4% in September and December). Respondents with children reported an average of 22.7 (95% CI: 21.1-24.3) (September) and 19.0 (95% CI 17.7-20.4) (December) contacts at school per day per child in attendance. CONCLUSION The skewed distribution of reported contacts toward workplace settings in September and December combined with the number of reported school-related contacts suggest that these settings represent important opportunities for transmission emphasizing the need to support and ensure infection control procedures in both workplaces and schools.
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Affiliation(s)
| | - Eric Merkley
- Munk School of Global Affairs & Public Policy, University of Toronto, Toronto, Canada
| | - David N Fisman
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Ashleigh R Tuite
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Zvonimir Poljak
- Department of Population Medicine, University of Guelph, Guelph, Canada
| | - Peter J Loewen
- Munk School of Global Affairs & Public Policy, University of Toronto, Toronto, Canada
| | - Amy L Greer
- Department of Population Medicine, University of Guelph, Guelph, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
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48
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Wikle N, Tran TNA, Gentilesco B, Leighow SM, Albert J, Strong ER, Břinda K, Inam H, Yang F, Hossain S, Chan P, Hanage WP, Messick M, Pritchard JR, Hanks EM, Boni MF. SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.11.17.20232918. [PMID: 34426816 PMCID: PMC8382133 DOI: 10.1101/2020.11.17.20232918] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states - Rhode Island, Massachusetts, and Pennsylvania - that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% ‒ 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% ‒ 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% ‒ 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group.
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Affiliation(s)
- Nathan Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | | | - Scott M Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - Joseph Albert
- Department of Physics, Pennsylvania State University, University Park, PA
| | - Emily R Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Karel Břinda
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | - Sajid Hossain
- Yale School of Medicine, Yale University, New Haven, CT
| | - Philip Chan
- Department of Medicine, Brown University, Providence, RI
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Maria Messick
- Rhode Island Office of the Governor and Rhode Island Department of Health, Providence, RI
| | - Justin R Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - Ephraim M Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Maciej F Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
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49
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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.
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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
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50
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Del Fava E, Cimentada J, Perrotta D, Grow A, Rampazzo F, Gil-Clavel S, Zagheni E. Differential impact of physical distancing strategies on social contacts relevant for the spread of SARS-CoV-2: evidence from a cross-national online survey, March-April 2020. BMJ Open 2021; 11:e050651. [PMID: 34675016 PMCID: PMC8532142 DOI: 10.1136/bmjopen-2021-050651] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES We investigate changes in social contact patterns following the gradual introduction of non-pharmaceutical interventions and their implications for infection transmission in the early phase of the pandemic. DESIGN, SETTING AND PARTICIPANTS We conducted an online survey based on targeted Facebook advertising campaigns across eight countries (Belgium, France, Germany, Italy, the Netherlands, Spain, UK and USA), achieving a sample of 51 233 questionnaires in the period 13 March-12 April 2020. Poststratification weights based on census information were produced to correct for selection bias. OUTCOME MEASURES Participants provided data on social contact numbers, adoption of protective behaviours and perceived level of threat. These data were combined to derive a weekly index of infection transmission, the net reproduction number [Formula: see text] . RESULTS Evidence from the USA and UK showed that the number of daily contacts mainly decreased after governments issued the first physical distancing guidelines. In mid-April, daily social contact numbers had decreased between 61% in Germany and 87% in Italy with respect to pre-COVID-19 levels, mostly due to a contraction in contacts outside the home. Such reductions, which were uniform across age groups, were compatible with an [Formula: see text] equal or smaller than one in all countries, except Germany. This indicates lower levels of infection transmission, especially in a period of gradual increase in the adoption rate of the face mask outside the home. CONCLUSIONS We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week and other demographic factors, which could be leveraged by the scientific community for developing more realistic epidemic models of COVID-19.
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Affiliation(s)
- Emanuele Del Fava
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
| | - Jorge Cimentada
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
| | - Daniela Perrotta
- 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
| | - Francesco Rampazzo
- Saïd Business School, Leverhulme Centre for Demographic Science, and Nuffield College, University of Oxford, Oxford, UK
| | - Sofia Gil-Clavel
- 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
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