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Social contact patterns and implications for infectious disease transmission - a systematic review and meta-analysis of contact surveys.
Mousa, Andria; Winskill, Peter; Watson, Oliver John; Ratmann, Oliver; Monod, Mélodie; Ajelli, Marco; Diallo, Aldiouma; Dodd, Peter J; Grijalva, Carlos G; Kiti, Moses Chapa; Krishnan, Anand; Kumar, Rakesh; Kumar, Supriya; Kwok, Kin O; Lanata, Claudio F; de Waroux, Olivier Le Polain; Leung, Kathy; Mahikul, Wiriya; Melegaro, Alessia; Morrow, Carl D; Mossong, Joël; Neal, Eleanor Fg; Nokes, D James; Pan-Ngum, Wirichada; Potter, Gail E; Russell, Fiona M; Saha, Siddhartha; Sugimoto, Jonathan D; Wei, Wan In; Wood, Robin R; Wu, Joseph; Zhang, Juanjuan; Walker, Patrick; Whittaker, Charles.
  • Mousa A; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.
  • Winskill P; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.
  • Watson OJ; Department of Mathematics, Imperial College London, London, United Kingdom.
  • Ratmann O; Department of Mathematics, Imperial College London, London, United Kingdom.
  • Monod M; Department of Mathematics, Imperial College London, London, United Kingdom.
  • Ajelli M; Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, United States.
  • Diallo A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, United States.
  • Dodd PJ; VITROME, Institut de Recherche pour le Developpement, Dakar, Senegal.
  • Grijalva CG; School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
  • Kiti MC; Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, Nashville, United States.
  • Krishnan A; KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
  • Kumar R; Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India.
  • Kumar S; Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India.
  • Kwok KO; Bill and Melinda Gates Foundation, Seattle, United States.
  • Lanata CF; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
  • de Waroux OLP; Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, China.
  • Leung K; Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China.
  • Mahikul W; Instituto de Investigación Nutricional, Lima, Peru.
  • Melegaro A; Department of Medicine, Vanderbilt University, Nashville, United States.
  • Morrow CD; London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Mossong J; WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Neal EF; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China.
  • Nokes DJ; Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand.
  • Pan-Ngum W; Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Milano, Italy.
  • Potter GE; Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa.
  • Russell FM; Centre for Infectious Disease Epidemiology and Research (CIDER), School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
  • Saha S; Health Directorate, Luxembourg, Luxembourg.
  • Sugimoto JD; Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia.
  • Wei WI; Department of Paediatrics, University of Melbourne, Victoria, Australia.
  • Wood RR; KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
  • Wu J; School of Life Sciences, University of Warwick, Coventry, United Kingdom.
  • Zhang J; Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
  • Walker P; National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville, United States.
  • Whittaker C; The Emmes Company, Rockville, United States.
Elife ; 102021 11 25.
Article in English | MEDLINE | ID: covidwho-1534521
Preprint
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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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Transmission, Infectious / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Year: 2021 Document Type: Article Affiliation country: ELife.70294

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Transmission, Infectious / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Year: 2021 Document Type: Article Affiliation country: ELife.70294