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Nagpal S, Kumar R, Noronha RF, Kumar S, Gupta D, Amarchand R, Gosain M, Sharma H, Menon GI, Krishnan A. Seasonal variations in social contact patterns in a rural population in north India: Implications for pandemic control. PLoS One 2024; 19:e0296483. [PMID: 38386667 PMCID: PMC10883557 DOI: 10.1371/journal.pone.0296483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 12/11/2023] [Indexed: 02/24/2024] Open
Abstract
Social contact mixing patterns are critical to model the transmission of communicable diseases, and have been employed to model disease outbreaks including COVID-19. Nonetheless, there is a paucity of studies on contact mixing in low and middle-income countries such as India. Furthermore, mathematical models of disease outbreaks do not account for the temporal nature of social contacts. We conducted a longitudinal study of social contacts in rural north India across three seasons and analysed the temporal differences in contact patterns. A contact diary survey was performed across three seasons from October 2015-16, in which participants were queried on the number, duration, and characteristics of contacts that occurred on the previous day. A total of 8,421 responses from 3,052 respondents (49% females) recorded characteristics of 180,073 contacts. Respondents reported a significantly higher number and duration of contacts in the winter, followed by the summer and the monsoon season (Nemenyi post-hoc, p<0.001). Participants aged 0-9 years and 10-19 years of age reported the highest median number of contacts (16 (IQR 12-21), 17 (IQR 13-24) respectively) and were found to have the highest node centrality in the social network of the region (pageranks = 0.20, 0.17). A large proportion (>80%) of contacts that were reported in schools or on public transport involved physical contact. To the best of our knowledge, our study is the first from India to show that contact mixing patterns vary by the time of the year and provides useful implications for pandemic control. We compared the differences in the number, duration and location of contacts by age-group and gender, and studied the impact of the season, age-group, employment and day of the week on the number and duration of contacts using multivariate negative binomial regression. We created a social network to further understand the age and gender-specific contact patterns, and used the contact matrices in each season to parameterise a nine-compartment agent-based model for simulating a COVID-19 epidemic in each season. Our results can be used to parameterize more accurate mathematical models for prediction of epidemiological trends of infections in rural India.
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Affiliation(s)
| | - Rakesh Kumar
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States of America
| | | | | | - Mudita Gosain
- All India Institute of Medical Sciences, New Delhi, India
| | | | | | - Anand Krishnan
- All India Institute of Medical Sciences, New Delhi, India
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Gomes MGM, Ferreira MU, Corder RM, King JG, Souto-Maior C, Penha-Gonçalves C, Gonçalves G, Chikina M, Pegden W, Aguas R. Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold. J Theor Biol 2022; 540:111063. [PMID: 35189135 PMCID: PMC8855661 DOI: 10.1016/j.jtbi.2022.111063] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/21/2022]
Abstract
Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being crucial to protect vulnerable individuals from severe outcomes as the virus becomes endemic.
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Affiliation(s)
- M Gabriela M Gomes
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK; Centro de Matemática e Aplicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Marcelo U Ferreira
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil; Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, Nova University of Lisbon, Lisbon, Portugal
| | - Rodrigo M Corder
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jessica G King
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Caetano Souto-Maior
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Guilherme Gonçalves
- Unidade Multidisciplinar de Investigação Biomédica, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittburgh, PA, USA
| | - Wesley Pegden
- Department of Mathematical Sciences, Carnegie Mellon University, Pittburgh, PA, USA
| | - Ricardo Aguas
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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3
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Gomes MGM, Ferreira MU, Corder RM, King JG, Souto-Maior C, Penha-Gonçalves C, Gonçalves G, Chikina M, Pegden W, Aguas R. Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2020.04.27.20081893. [PMID: 32511451 PMCID: PMC7239079 DOI: 10.1101/2020.04.27.20081893] [Citation(s) in RCA: 128] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being critical to protect vulnerable individuals from severe outcomes as the virus becomes endemic.
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Affiliation(s)
- M Gabriela M Gomes
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
- Centro de Matemática e Aplicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Marcelo U Ferreira
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, Nova University of Lisbon, Lisbon, Portugal
| | - Rodrigo M Corder
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jessica G King
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Caetano Souto-Maior
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Guilherme Gonçalves
- Unidade Multidisciplinar de Investigação Biomédica, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittburgh, PA, USA
| | - Wesley Pegden
- Department of Mathematical Sciences, Carnegie Mellon University, , Pittburgh" , PA, USA
| | - Ricardo Aguas
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, Diallo A, Dodd PJ, Grijalva CG, Kiti MC, Krishnan A, Kumar R, Kumar S, Kwok KO, Lanata CF, le Polain de Waroux O, Leung K, Mahikul W, Melegaro A, Morrow CD, Mossong J, Neal EF, Nokes DJ, Pan-Ngum W, Potter GE, Russell FM, Saha S, Sugimoto JD, Wei WI, Wood RR, Wu J, Zhang J, Walker P, Whittaker C. Social contact patterns and implications for infectious disease transmission: a systematic review and meta-analysis of contact surveys. eLife 2021; 10:70294. [PMID: 34821551 PMCID: PMC8765757 DOI: 10.7554/elife.70294] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions. Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1). Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch. These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions. Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups. In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens. These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.
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Affiliation(s)
- Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver John Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mélodie Monod
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, United States
| | - Aldiouma Diallo
- VITROME, Institut de Recherche pour le Developpement, Dakar, Senegal
| | - Peter J Dodd
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Carlos G Grijalva
- Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, Nashville, United States
| | | | - Anand Krishnan
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kumar
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States
| | - Kin O Kwok
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | | | | | - Kathy Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wiriya Mahikul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Milano, Italy
| | - Carl D Morrow
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Eleanor Fg Neal
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - D James Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Gail E Potter
- National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville, United States
| | - Fiona M Russell
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - Siddhartha Saha
- US Centers for Disease Control and Prevention, New Delhi, India
| | - Jonathan D Sugimoto
- Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs, Seattle, United States
| | - Wan In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Robin R Wood
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Joseph Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Shanghai, China
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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