Social contact patterns and implications for infectious disease transmission: a systematic review and meta-analysis of contact surveys
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 focussed 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, as well as 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).
Data availability
All individual-level data across all studies and analysis code are available at https://github.com/mrc-ide/contact_patterns (see Supplementary Text 4 for data dictionary).
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Article and author information
Author details
Funding
joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1)
- Andria Mousa
- Peter Winskill
- Patrick Walker
- Charles Whittaker
Australian National Health and Medical Research Council
- Fiona M Russell
WHO
- Fiona M Russell
Bill & Melinda Gates Foundation
- Fiona M Russell
Wellcome Trust
- Fiona M Russell
DFAT
- Fiona M Russell
EPSRC through the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning
- Mélodie Monod
University of Washington
- Jonathan D Sugimoto
US National Institutes of Health, NIAID
- Jonathan D Sugimoto
NIH (K24AI148459)
- Carlos G Grijalva
General Medical Sciences / National Institute of Health (U01-GM070749)
- Gail E Potter
UK foreign Commonwealth and Development Office
- Oliver John Watson
Emmes Company
- Gail E Potter
CUHK Direct grant for research (2019.020)
- Kin O Kwok
Health and Medical Research Fund (INF-CUHK-1,17160302,18170312)
- Kin O Kwok
General Research Fund (14112818)
- Kin O Kwok
Early Career Scheme (24104920)
- Kin O Kwok
Wellcome Trust (200861/Z/16/Z)
- Kin O Kwok
UK Medical Research Council. UK-funded award is part of the EDCTP2 programme supported by the EU (MR/P022081/1)
- Peter J Dodd
Australian Government Research Training Program Scholarship
- Eleanor FG Neal
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All original studies included were approved by an institutional ethics review committee. Ethics approval was not required for the present study.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Further reading
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- Epidemiology and Global Health
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In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.
Methods:
We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).
Results:
275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.
Conclusions:
In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.
Funding:
This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).
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- Epidemiology and Global Health
Background:
The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).
Methods:
Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.
Results:
Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).
Conclusions:
Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding:
Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.