1. Epidemiology and Global Health

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

  1. Andria Mousa  Is a corresponding author
  2. Peter Winskill
  3. Oliver John Watson
  4. Oliver Ratmann
  5. Mélodie Monod
  6. Marco Ajelli
  7. Aldiouma Diallo
  8. Peter J Dodd
  9. Carlos G Grijalva
  10. Moses Chapa Kiti
  11. Anand Krishnan
  12. Rakesh Kumar
  13. Supriya Kumar
  14. Kin O Kwok
  15. Claudio F Lanata
  16. Olivier Le Polain de Waroux
  17. Kathy Leung
  18. Wiriya Mahikul
  19. Alessia Melegaro
  20. Carl D Morrow
  21. Joël Mossong
  22. Eleanor FG Neal
  23. D James Nokes
  24. Wirichada Pan-ngum
  25. Gail E Potter
  26. Fiona M Russell
  27. Siddhartha Saha
  28. Jonathan D Sugimoto
  29. Wan In Wei
  30. Robin R Wood
  31. Joseph Wu
  32. Juanjuan Zhang
  33. Patrick Walker
  34. Charles Whittaker  Is a corresponding author
  1. MRC Centre for Global Infectious Disease Analysis, Imperial College London, United Kingdom
  2. Department of Mathematics, Imperial College London, United Kingdom
  3. Department of Epidemiology and Biostatistics, Indiana University School of Public Health, United States
  4. Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, United States
  5. VITROME, Institut de Recherche pour le Developpement, Senegal
  6. School of Health and Related Research, University of Sheffield, United Kingdom
  7. Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, United States
  8. KEMRI-Wellcome Trust Research Programme, Kenya
  9. Centre for Community Medicine, All India Institute of Medical Sciences, India
  10. Bill and Melinda Gates Foundation, United States
  11. JC School of Public Health and Primary Care, Chinese University of Hong Kong, China
  12. Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, China
  13. Shenzhen Research Institute of The Chinese University of Hong Kong, China
  14. Instituto de Investigación Nutricional, Peru
  15. Department of Medicine, Vanderbilt University, United States
  16. London School of Hygiene and Tropical Medicine, United Kingdom
  17. WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, China
  18. Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, China
  19. Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Thailand
  20. Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Italy
  21. Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, South Africa
  22. Centre for Infectious Disease Epidemiology and Research (CIDER), School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
  23. Health Directorate, Luxembourg
  24. Infection and Immunity, Murdoch Children's Research Institute, Australia
  25. Department of Paediatrics, University of Melbourne, Australia
  26. School of Life Sciences, University of Warwick, United Kingdom
  27. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Thailand
  28. National Institute for Allergies and Infectious Diseases, National Institutes of Health, United States
  29. The Emmes Company, United States
  30. Influenza Programme, US Centers for Disease Control and Prevention, India
  31. Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs, United States
  32. Department of Epidemiology, University of Washington, United States
  33. Fred Hutchinson Cancer Research Center, United States
  34. School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, China
https://doi.org/10.7554/eLife.70294
Share this article
Cite this article
  1. Andria Mousa
  2. Peter Winskill
  3. Oliver John Watson
  4. Oliver Ratmann
  5. Mélodie Monod
  6. Marco Ajelli
  7. Aldiouma Diallo
  8. Peter J Dodd
  9. Carlos G Grijalva
  10. Moses Chapa Kiti
  11. Anand Krishnan
  12. Rakesh Kumar
  13. Supriya Kumar
  14. Kin O Kwok
  15. Claudio F Lanata
  16. Olivier Le Polain de Waroux
  17. Kathy Leung
  18. Wiriya Mahikul
  19. Alessia Melegaro
  20. Carl D Morrow
  21. Joël Mossong
  22. Eleanor FG Neal
  23. D James Nokes
  24. Wirichada Pan-ngum
  25. Gail E Potter
  26. Fiona M Russell
  27. Siddhartha Saha
  28. Jonathan D Sugimoto
  29. Wan In Wei
  30. Robin R Wood
  31. Joseph Wu
  32. Juanjuan Zhang
  33. Patrick Walker
  34. Charles Whittaker
(2021)
Social contact patterns and implications for infectious disease transmission – a systematic review and meta-analysis of contact surveys
eLife 10:e70294.
https://doi.org/10.7554/eLife.70294
  2. Download BibTeX
  3. Download .RIS
12 figures, 4 tables and 8 additional files

Figures

Figure 1
Download asset Open asset
Total number of contacts.

Sample median total number of contacts shown by gender (right) and 5-year age groups up to ages 80+ shown for (A) lower-income countries (LICs)/lower-middle countries (LMICs), (B) upper-middle-income countries (UMICs), and (C) high-income countries (HICs). Grey lines denote individual studies, and the solid black line is the median across all studies of within that income group. Studies with a diary-based methodology are represented by a solid grey line and those with a questionnaire or interview design are shown as a dashed line. For UMICs, one study outlier with extremely high number of contacts is excluded (online Thai survey with a ‘snowball’ design by Stein et al., 2014). Contact rate ratios and associated 95% credible intervals from a negative binomial model with random study effects are shown in (D) (LICs/LMICs), (E) (UMICs), and (F) (HICs). All models were adjusted for age and gender and were ran separately for each key variable (weekday/weekend, household size, survey methodology, student/employment status).

Figure 2
Download asset Open asset
Contact location and household size.

(A) Sample median number of contacts by household size in review data, stratified by income strata. Shaded area denotes the interquartile range. (B) Sample mean % of contacts made at each location (home, school, work, other) by income group. (C) Total daily contacts (sample mean number) made at each location by 5-year age group. (D) Sample median number of contacts made at home by 5-year age groups and income strata. Shaded area denotes the interquartile range. (E) Average household size and GDP; red circles represent median household size in single studies from the review. GDP information was obtained from the World Bank Group and global household size data from the Department of Economic and Social Affairs, Population Division, United Nations.

Figure 3
Download asset Open asset
Physical contacts.

Mean proportion of contacts that are physical shown by gender (right) and 5-year age groups up to ages 80+ shown for (A) lower-income countries (LICs)/lower-middle countries (LMICs), (B) upper-middle-income countries (UMICs), and (C) high-income countries (HICs). Grey lines denote individual studies, and the solid black line is the mean across all studies of within that income group. Studies with a diary-based methodology are represented by a solid grey line and those with a questionnaire or interview design are shown as a dashed line. Odds ratios and associated 95% credible intervals from a logistic regression model with random study effects are shown in D (LICs/LMICs), E (UMICs), and F (HICs). All models were adjusted for age and gender and were ran separately for each key variable (weekday/weekend, household size, survey methodology, student/employment status).

Figure 4
Download asset Open asset
Contact duration.

Mean proportion of contacts that last at least an hour shown by gender (right) and 5-year age groups up to ages 80+ shown for (A) lower-income countries (LICs)/lower-middle countries (LMICs), (B) upper-middle-income countries (UMICs), and (C) high-income countries (HICs). Grey lines denote individual studies and the solid black line is the mean across all studies of within that income group. Studies with a diary-based methodology are represented by a solid grey line and those with a questionnaire or interview design are shown as a dashed line. Odds ratios and associated 95% credible intervals from a logistic regression model with random study effects are shown in (D) (LICs/LMICs), (E) (UMICs), and (F) (HICs). All models were adjusted for age and gender and were ran separately for each key variable (weekday/weekend, household size, survey methodology, student/employment status).

Appendix 1—figure 1
Download asset Open asset
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of the screening process and selection of eligible.
Appendix 2—figure 1
Download asset Open asset
Total number of contacts boxplots and violin plots by participant/study characteristics.
Appendix 2—figure 2
Download asset Open asset
The relationship between household size and median daily contacts made at home divided by a participant’s household size, stratified by income strata.
Appendix 2—figure 3
Download asset Open asset
Comparison of estimated regression coefficients for predicting total contacts with and without the inclusion of additional contacts.
Appendix 2—figure 4
Download asset Open asset
Location of contacts as a percentage of total daily contacts by (A) participant’s age, (B) participant’s gender, (C) day of the week, (D) household size, (E) employment status (in participants aged 18 or over), and (F) student status in participants aged 5 to <20 years.
Appendix 2—figure 5
Download asset Open asset
Total number of daily contacts in each location by (A) participant’s age, (B) participant’s gender, (C) day of the week, (D) household size, (E) employment status (in participants aged 18 or over), and (F) student status in participants aged 5 to < 20 years.
Appendix 2—figure 6
Download asset Open asset
Contact location and (A) type of contacts and (B) duration of contact, by income group.
Appendix 2—figure 7
Download asset Open asset
Comparison of estimated regression coefficients in the main analysis and sensitivity analysis weighing each study equally within an income group.

Tables

Table 1
Summary table of total daily contacts.

The total number of observations, as well as the mean, median, and interquartile range (p25 and p75) of total daily contacts shown by participant and study characteristics.

NMeanp25Medianp75
  
Overall28,50314.55917
  
GenderMale13,21815.35918
  Female14,59813.75916
  
Age<158,56114.661019
  15–6517,84114.95917
  >652,04710.43612
  
Income statusLIC/LMIC9,90615.451017
  UMIC8,33014.45816
  HIC10,26713.75917
  
Survey MethodologyDiary12,22613.961018
  Interview/survey16,22715.04816
  
Day typeWeekend4,30814.75916
  Weekday21,57914.15917
  
EmploymentYes8,87915.45917
(in those aged > 18)No6,1589.84712
  
StudentYes4,43818.481424
(in those aged 5–18)No60010.45814
  
Household size11,47910.43612
  23,22011.84714
  34,13012.04714
  45,24013.45817
  53,10912.54814
  6+8,87317.771120
  
StudyBelgiumMossong75011.85915
 ChinaRead1,82118.671322
 ChinaZhang96518.841030
 FijiNeal2,0196.4468
 FinlandMossong1,00611.15915
 GermanyMossong1,3417.94610
 Hong KongKwok et al., 201476218.35918
 Hong KongKwok et al., 20181,06611.93713
 Hong KongLeung1,14914.43715
 IndiaKumar2,94327.0121726
 ItalyMossong84919.8101727
 KenyaKiti56817.7101523
 LuxembourgMossong1,05117.581424
 The NetherlandsMossong26913.961119
 PeruGrijalva58815.381220
 PolandMossong1,01216.371322.5
 RussiaAjelli50218.061119
 South AfricaDodd1,2765.2457
 South AfricaWood57115.691420
 SenegalPotter1,41719.7101525
 ThailandMahikul36922.6132031
 ThailandStein21958.5152455
 UgandaLe Polain de Waroux5687.0579
 United KingdomMossong1,01211.761016
 VietnamHorby8657.7579
 ZambiaDodd2,3004.8346
 ZimbabweMelegaro1,24510.76914
  
Appendix 2—table 1
Correlation (Pearson’s rho) between coefficients estimated in the main analysis and those from the sensitivity analysis weighing each study equally within an income group.
 Income groupPearson’s rho correlation coefficient
Total daily contactsLIC/LMIC0.927
UMIC0.962
HIC0.996
  
Duration of contactsLIC/LMIC0.987
UMIC0.984
HIC0.998
  
Physical contactsLIC/LMIC0.977
UMIC0.974
HIC0.998
Appendix 2—table 2
Assortativity by age and sex, weighing by study sample size (method A).
 Age category  Gender
LIC/LMIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.470.410.12Participant genderMale0.590.41
20.220.640.14Female0.410.59
30.200.510.29  
   
   
UMIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.340.510.15Participant genderMale0.520.48
20.200.620.17Female0.460.54
30.140.410.45  
   
   
HIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.550.310.14Participant genderMale0.510.49
20.240.530.23Female0.420.58
30.150.330.51  
   
Appendix 2—table 3
Assortativity by age and sex, weighing each study equally (method B).
   
LIC/LMIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.480.410.11Participant genderMale0.550.45
20.270.600.13Female0.420.58
30.210.510.28  
   
   
UMIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.380.460.16Participant genderMale0.540.46
20.210.610.18Female0.440.56
30.180.500.32  
   
   
HIC  
 Contact age  Contact gender
 123  MaleFemale
Participant age10.540.310.14Participant genderMale0.510.49
20.270.510.22Female0.420.58
30.210.310.48  
   

Additional files

Transparent reporting form
https://cdn.elifesciences.org/articles/70294/elife-70294-transrepform1-v2.pdf
Supplementary file 1

Search string.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp1-v2.docx
Supplementary file 2

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-individual participant data (IPD) checklist of items to include when reporting a systematic review and meta-analysis of IPD.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp2-v2.docx
Supplementary file 3

Risk of bias table (AXIS critical appraisal tool).

https://cdn.elifesciences.org/articles/70294/elife-70294-supp3-v2.xlsx
Supplementary file 4

Study additional Information and data assumptions.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp4-v2.docx
Supplementary file 5

Data dictionary for participant-level data.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp5-v2.docx
Supplementary file 6

Extraction table of study characteristics.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp6-v2.docx
Supplementary file 7

Data availability by study.

https://cdn.elifesciences.org/articles/70294/elife-70294-supp7-v2.docx

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andria Mousa
  2. Peter Winskill
  3. Oliver John Watson
  4. Oliver Ratmann
  5. Mélodie Monod
  6. Marco Ajelli
  7. Aldiouma Diallo
  8. Peter J Dodd
  9. Carlos G Grijalva
  10. Moses Chapa Kiti
  11. Anand Krishnan
  12. Rakesh Kumar
  13. Supriya Kumar
  14. Kin O Kwok
  15. Claudio F Lanata
  16. Olivier Le Polain de Waroux
  17. Kathy Leung
  18. Wiriya Mahikul
  19. Alessia Melegaro
  20. Carl D Morrow
  21. Joël Mossong
  22. Eleanor FG Neal
  23. D James Nokes
  24. Wirichada Pan-ngum
  25. Gail E Potter
  26. Fiona M Russell
  27. Siddhartha Saha
  28. Jonathan D Sugimoto
  29. Wan In Wei
  30. Robin R Wood
  31. Joseph Wu
  32. Juanjuan Zhang
  33. Patrick Walker
  34. Charles Whittaker
(2021)
Social contact patterns and implications for infectious disease transmission – a systematic review and meta-analysis of contact surveys
eLife 10:e70294.
https://doi.org/10.7554/eLife.70294