Social contact patterns and implications for infectious disease transmission – a systematic review and meta-analysis of contact surveys

Author:

Mousa Andria1ORCID,Winskill Peter1,Watson Oliver John2ORCID,Ratmann Oliver2ORCID,Monod Mélodie2,Ajelli Marco34,Diallo Aldiouma5,Dodd Peter J6,Grijalva Carlos G7,Kiti Moses Chapa8,Krishnan Anand9,Kumar Rakesh9,Kumar Supriya10,Kwok Kin O111213,Lanata Claudio F1415,de Waroux Olivier Le Polain16,Leung Kathy1718ORCID,Mahikul Wiriya19,Melegaro Alessia20ORCID,Morrow Carl D2122,Mossong Joël23,Neal Eleanor FG2425,Nokes D James826,Pan-ngum Wirichada27,Potter Gail E2829,Russell Fiona M2425,Saha Siddhartha30,Sugimoto Jonathan D313233,Wei Wan In11,Wood Robin R21,Wu Joseph1718,Zhang Juanjuan34,Walker Patrick1,Whittaker Charles1ORCID

Affiliation:

1. MRC Centre for Global Infectious Disease Analysis, Imperial College London

2. Department of Mathematics, Imperial College London

3. Department of Epidemiology and Biostatistics, Indiana University School of Public Health

4. Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University

5. VITROME, Institut de Recherche pour le Developpement

6. School of Health and Related Research, University of Sheffield

7. Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center

8. KEMRI-Wellcome Trust Research Programme

9. Centre for Community Medicine, All India Institute of Medical Sciences

10. Bill and Melinda Gates Foundation

11. JC School of Public Health and Primary Care, Chinese University of Hong Kong

12. Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong

13. Shenzhen Research Institute of The Chinese University of Hong Kong

14. Instituto de Investigación Nutricional

15. Department of Medicine, Vanderbilt University

16. London School of Hygiene and Tropical Medicine

17. WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong

18. Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park

19. Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy

20. Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University

21. Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town

22. Centre for Infectious Disease Epidemiology and Research (CIDER), School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town

23. Health Directorate

24. Infection and Immunity, Murdoch Children's Research Institute

25. Department of Paediatrics, University of Melbourne

26. School of Life Sciences, University of Warwick

27. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University

28. National Institute for Allergies and Infectious Diseases, National Institutes of Health

29. The Emmes Company

30. Influenza Programme, US Centers for Disease Control and Prevention

31. Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs

32. Department of Epidemiology, University of Washington

33. Fred Hutchinson Cancer Research Center

34. School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education

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).

Funder

Joint Centre funding from the UK Medical Research Council and DFID

Chinese University of Hong Kong

Health and Medical Research Fund

General Research Fund of Shanghai Normal University

Early Career Scheme

Wellcome Trust

Medical Research Council

Australian Government Research Training Program Scholarship

National Health and Medical Research Council

World Health Organization

Department of Foreign Affairs and Trade

Engineering and Physical Sciences Research Council

University of Washington

National Institute of Allergy and Infectious Diseases

National Institutes of Health

Emmes Company

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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