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.

Previous outbreaks of Ebola (Mbala-Kingebeni et al., 2019), influenza (Khan et al., 2009), and the ongoing COVID-19 pandemic have highlighted the importance of understanding the transmission dynamics and spread of infectious diseases, which depend fundamentally on the underlying patterns of social contact between individuals. Together, these patterns give rise to complex social networks that influence disease dynamics (Eubank et al., 2004; Ferrari et al., 2006; Firth et al., 2020; Zhang et al., 2020), including the capacity for emergent pathogens to become endemic (Ghani and Aral, 2005; Jacquez et al., 1988), the overdispersion of the offspring distribution underlying the reproduction number (Delamater et al., 2019 ) and the threshold at which herd immunity is reached (Fontanet and Cauchemez, 2020; Mistry et al., 2021). They can similarly modulate the effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and workplace restrictions, that are typically deployed to control and contain the spread of infectious diseases (Prem et al., 2020).

Social contact surveys provide insight into the features of these networks, which is typically achieved through incorporating survey results into mathematical models of infectious disease transmission frequently used to guide decision making in response to outbreaks (Chang et al., 2021; Davies et al., 2020). Such inputs are necessary for models to have sufficient realism to evaluate relevant policy questions. However, despite the known importance of contact patterns as determinants of the infectious disease dynamics, our understanding of how they vary globally remains far from complete. Reviews of contact patterns to date have focused on high-income countries (HICs) (Hoang et al., 2019). This is despite evidence that social contact patterns differ systematically across settings in ways that have material consequences for the dynamics of infectious disease transmission and the evolution of epidemic trajectories (Prem et al., 2017; Walker et al., 2020). Previous reviews have also primarily explored the total number of contacts made by individuals (Hoang et al., 2019) and/or how these contacts are distributed across different age/sex groups (Horton et al., 2020). Whilst these factors are a vital component underpinning disease spread, recent work has also underscored the importance of the characteristics of contacts (such as the location, duration, and extent of physical contact) in determining transmission risk (Thompson et al., 2021).

Here, we carry out a systematic review of contact surveys (conducted prior to the emergence of COVID-19) in lower-income, lower-middle and upper-middle-income countries (LICs, LMICs and UMICs, respectively). Alongside previously published data from HICs (Kwok et al., 2018; Kwok et al., 2014; Leung et al., 2017; Mossong et al., 2008), we collate individual participant data (IPD) on social contacts from published work spanning 27 surveys from 22 countries and over 28,000 individuals. We use a Bayesian framework to explore drivers and determinants of contact patterns across a wider range of settings and at a more granular scale than has previously been possible. Specifically, we assess the influence of key factors such as age, gender, and household structure on both the total number and characteristics (such as duration, location, and type) of contact made by an individual, and explore how the comparative importance of different factors varies across different settings. We additionally evaluate the extent and degree of assortativity in contact patterns between different groups, and how this varies across settings.

Understanding patterns of contact across populations is vital to predicting the dynamics and spread of infectious diseases, as well understanding the control interventions likely to have the greatest impact. Here, using a systematic review and individual-participant data meta-analysis of contact surveys, we summarise research exploring these patterns across a range of populations spanning 28,503 individuals and 22 countries. Our findings highlight substantial differences in contact patterns between income settings. These differences are driven by setting-specific sociodemographic factors such as age, gender, household structure, and patterns of employment, which all have material consequences for transmission and spread of respiratory pathogens.

Across the collated studies, the total number of contacts was highest for school-aged children. This is consistent with previous results from HICs (Béraud et al., 2015; Fu et al., 2012; Hoang et al., 2019; Ibuka et al., 2016; Lapidus et al., 2013) and shown here to be generally true for LICs/LMICs and UMICs also. Interestingly however, we observed differences in patterns of contact in adults across income strata. Whilst contact rates in HICs declined in older adults, this was not observed in LICs/LMICs, where contact rates did not differ in the oldest age group compared to younger ages. This is consistent with variation in household structure and size across settings, with nearly two-thirds of participants aged 65+ in included LIC/LMIC surveys living in large, likely intergenerational, households (6+ members), compared to only 2% in HICs. HICs were also characterised by more assortative mixing between age groups, with older adults in LICs/LMICs more likely to mix with individuals of younger ages, again consistent with the observed differences between household structures across the two settings. These results have important consequences for the viability and efficacy of protective policies centred around shielding of elderly individuals (i.e. those most at risk from COVID-19 or influenza). In these settings other strategies may be required to effectively shield vulnerable populations, as has been previously suggested (Dahab et al., 2020). Our results support the idea of households as a key site for transmission of respiratory pathogens (Thompson et al., 2021), with the majority of contacts made at home. Our analysis highlights that the number of contacts made at home is mainly driven by household size. However, the relative importance of households compared to other locations is likely to vary across settings. We observed significant differences across income settings in the distribution of contacts made at home, work, and school. The proportion of contacts made at home was highest for LIC/LMICs, where larger average household sizes were associated with more contacts, more physical contacts, and longer lasting contacts. By contrast, participants in HICs tended to report more contacts occurring at work and school. The lower number of contacts at work in LIC/LMIC may be explained by the types of employment (e.g. agriculture in rural surveys) and a selection bias (women at home/homemakers more likely to be surveyed in questionnaire-based surveys). Our analyses similarly highlighted significant variation in the duration and nature of contacts across settings. Contacts made by female participants in LICs/LMICs were more likely to be physical compared to men, whilst the opposite effect was observed for HICs and UMICs, potentially reflecting context-specific gender roles. In all settings, we observed a general decline of physical contacts with age, except in the very old (Mossong et al., 2008), potentially reflecting higher levels of dependency and the need for physical care.

Altogether, these results suggest differences between settings in the comparative importance of different locations (such as the household or the workplace) to transmission of SARS-CoV-2, a finding which would likely modulate the impact of different NPIs (such as workplace or school closures, stay at home orders, etc.). Moreover, it suggests that previous estimates of NPI effectiveness primarily derived from European data and settings (Brauner et al., 2021) may be of limited generalisability to non-European settings characterised by different structures and patterns of social contact. However, beyond highlighting heterogeneity in where and how transmission is likely to occur, it remains challenging to disentangle exactly how these differences in contact patterns would shape patterns of transmission. Whilst the collated data provide a cross-sectional snapshot into the networks of social contact underpinning transmission, they remain insufficient to completely resolve this network or its temporal dynamics. Our results therefore do not consider key features relevant to population-level spread and transmission (such as overall network structure or the extent of repeated contacts, which would be most likely to occur with household members) which previous work has demonstrated can have a significant impact on infectious disease dynamics, both in general terms (Bansal et al., 2010; Keeling and Eames, 2005) and with COVID-19 (Rader et al., 2020). It is in this context that recent results generating complete social networks (including both the frequency and identity of an individual’s contacts) from high-resolution GPS data represent promising developments in understanding social contact networks and how they shape transmission (Firth et al., 2020).

There are important caveats to these findings. Data constraints limited the numbers of factors we were able to explore – for example, despite evidence (Kiti et al., 2014) suggesting that contact patterns differ across rural and urban settings, only three studies (Kiti et al., 2014; le Polain de Waroux et al., 2018b; Neal et al., 2020) contained information from both rural and urban sites, allowing classification. Similarly, we were unable to examine the impact of socioeconomic factors such as household wealth, despite experiences with COVID-19 having highlighted strong socioeconomic disparities in both transmission and burden of disease (De Negri et al., 2021; Routledge et al., 2021; Ward et al., 2021; Winskill et al., 2020) and previous work suggesting that poorer individuals are less likely to be employed in occupations amenable to remote working (Loayza, 2020). A lack of suitably detailed information in the studies conducted precludes analysis of these factors but highlights the importance of incorporating economic questions into future contact surveys, such as household wealth and house square footage. Other factors also not controlled for here, but that may similarly shape contact patterns include school holidays or seasonal variations in population movement and composition that we are unable to capture given the cross-sectional nature of these studies.

Another important limitation to these results is that we are only able to consider a limited set of contact characteristics (the location and duration of the contact and whether it was physical). Previous work has highlighted the importance of these factors in determining the risk of respiratory pathogen transmission (Chang et al., 2021; Dunne et al., 2018; le Polain de Waroux et al., 2018a; Neal et al., 2020; Thompson et al., 2021), but only a limited number of studies reported whether a contact was ‘close’ or ‘casual’ (Kwok et al., 2018; Kwok et al., 2014; le Polain de Waroux et al., 2018b) and whether the contact was made indoors or outdoors (Wood et al., 2012); both factors likely to influence transmission risk (Bulfone et al., 2021; Chu et al., 2020). More generally, the relevance and comparative importance of different contacts to transmission likely varies according to the specific pathogen and its predominant transmission modality (e.g. aerosol, droplet, fomite, etc.). It is therefore important to note that these results do not provide a direct indication of explicit transmission risk, but rather an indicator of factors likely to be relevant to transmission.

Relatedly, it is also important to note that the studies collated here were conducted over a wide time-period (2005–2018). In conjunction with the cross-sectional nature of the included studies, this precludes us from being able to examine for potential time-related trends in contact patterns. Additionally, the collated surveys were all carried out prior to the onset of the SARS-CoV-2 pandemic. Previous work has documented significant alterations to patterns of social contact in response to individual-level behaviour changes or government implemented NPIs aimed at controlling SARS-CoV-2 spread, and that these changes are dynamic and time-varying (Gimma et al., 2021; McCreesh et al., 2021). A detailed understanding of the impact of changing contact patterns on disease spread necessarily requires both an understanding of baseline contact patterns (as detailed in the studies collated here) and what changes have occurred as a result of control measures – however this latter data remains sparse and is available for only a limited number of settings (Jarvis et al., 2021; Jarvis et al., 2020; Liu et al., 2021). Description of contact location was also coarse and precluded more granular analyses of specific settings, such as markets, which have previously been shown to be important locations for transmission in rural areas (Grijalva et al., 2015).

Heterogeneity between studies was larger for LICs/LMICs and UMICs, which we partly accounted for, through fitting random study effects. These study differences may be attributed to the way individual contact surveys were conducted, making comparisons of contact patterns among surveys more difficult (e.g. prospective/retrospective diary surveys, online/paper questionnaires, face-to-face/phone interviews, and different contact definitions). For instance, there is evidence suggesting that prospective reporting, which is less affected by recall bias, can often lead to a higher number of contacts being reported (Mikolajczyk and Kretzschmar, 2008) and a lower probability of casual or short-lasting contacts being missed. The relatively high contact rates observed in HICs may be explained by the fact that all but two HIC surveys used diary methods. Our study highlights that a unified definition of ‘contact’ and standard practice in data collection could help increase the quality of collected data, leading to more robust and reliable conclusions about contact patterns. Whilst we aggregate results by income strata due to the limited availability of data (particularly in LICs and middle-income countries), it is important to note that the outcomes considered here are likely to be shaped by several different factors other than country-level income. Whilst some of these factors will be correlated with a country’s income status (e.g. household size Walker et al., 2020), many others will be unique to a particular setting or geographical area or correlate only weakly with country-level data. Examples include patterns of employment, the role of women, and other contextual factors. These analyses are therefore intended primarily to provide indications of prevailing patterns, rather than a definitive description of contact patterns in a specific context and highlight the significant need for further studies to be carried out in a diversity of different locations.

Despite these limitations however, our results highlight significant differences in the structure and nature of contact patterns across settings. These differences suggest that the comparative importance of different locations and age groups to transmission will likely vary across settings and have critical consequences for the efficacy and suitability of strategies aimed at controlling the spread of respiratory pathogens such as SARS-CoV-2. Most importantly, our study highlights the limited amount of work that has been undertaken to date to better understand and quantify patterns of contact across a range of settings, particularly in LICs and middle-income countries, which is vital in informing control strategies reducing the spread of such pathogens.

All individual-level data across all studies and analysis code are available at https://github.com/mrc-ide/contact_patterns (copy archived at swh:1:rev:0b732099d66b2788ae6da5cf0e8185b25de70868) (see Supplementary file 5 for data dictionary).

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Author details

1. Andria Mousa

   MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   a.mousa17@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9406-7481

2. Peter Winskill

   MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Investigation, Methodology, Writing – review and editing

   Competing interests

   received funding from WHO and the Asian Development Bank via their institution to investigate COVID19 Vaccine modelling. The author has no other competing interests to declare

3. Oliver John Watson

   Department of Mathematics, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2374-0741

4. Oliver Ratmann

   Department of Mathematics, Imperial College London, London, United Kingdom

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8667-4118

5. Mélodie Monod

   Department of Mathematics, Imperial College London, London, United Kingdom

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Marco Ajelli

   1. Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, United States
   2. Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, United States

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   has received research funding from Seqirus outside the submitted work

7. Aldiouma Diallo

   VITROME, Institut de Recherche pour le Developpement, Dakar, Senegal

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Peter J Dodd

   School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

9. Carlos G Grijalva

   Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, Nashville, United States

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   has received funding from NIH, CDC, AHRQ, FDA, Campbell Alliance/Syneos Health and Sanofi, outside the current work. CGG has also received consulting fees from Pfizer, Merck and Sanofi. The author has no other competing interests to declare

10. Moses Chapa Kiti

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

11. Anand Krishnan

    Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

12. Rakesh Kumar

    Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

13. Supriya Kumar

    Bill and Melinda Gates Foundation, Seattle, United States

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

14. Kin O Kwok

    1. JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
    2. Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, China
    3. Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

15. Claudio F Lanata

    1. Instituto de Investigación Nutricional, Lima, Peru
    2. Department of Medicine, Vanderbilt University, Nashville, United States

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Olivier Le Polain de Waroux

    London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

17. Kathy Leung

    1. 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
    2. Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4777-388X

18. Wiriya Mahikul

    Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

19. Alessia Melegaro

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

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2221-8898

20. Carl D Morrow

    1. Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
    2. 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

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

21. Joël Mossong

    Health Directorate, Luxembourg, Luxembourg

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

22. Eleanor FG Neal

    1. Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
    2. Department of Paediatrics, University of Melbourne, Victoria, Australia

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

23. D James Nokes

    1. KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    2. School of Life Sciences, University of Warwick, Coventry, United Kingdom

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

24. Wirichada Pan-ngum

    Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

25. Gail E Potter

    1. National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville, United States
    2. The Emmes Company, Rockville, United States

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    was employed by the Emmes Company while analyzing the Niakhar Senegal social contact network data included in this study. The Emmes Company was contracted to perform data cleaning and data analysis of the Niakhar, Senegal clinical trial data (but not the social contact network data) for this study before GEP joined the Emmes Company (in October 2015). After GEP joined the Emmes Company, the sole support from Emmes for this manuscript was in the form of salary support for GEP. GEP also received travel support from Thomas Francis Jr. Travel Fellowship. The author has no other competing interests to declare

26. Fiona M Russell

    1. Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
    2. Department of Paediatrics, University of Melbourne, Victoria, Australia

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

27. Siddhartha Saha

    Influenza Programme, US Centers for Disease Control and Prevention, New Delhi, India

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

28. Jonathan D Sugimoto

    1. Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs, Seattle, United States
    2. Department of Epidemiology, University of Washington, Washington, United States
    3. Fred Hutchinson Cancer Research Center, Seattle, United States

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    received travel support for this work from Thomas Francis, Jr. Scholarship Fund. JDS has also received consultancy fees from the International Vaccine Institute, Seoul, Republic of Korea, for work related to studies of typhoid fever burden and vaccine impact. JDS also participates on a Data Safety Monitoring Board for optimising infant immunisation schedules in Uganda & Nepal, University of Oxford, UK (ongoing)

29. Wan In Wei

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

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

30. Robin R Wood

    Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

31. Joseph Wu

    1. 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
    2. Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

32. Juanjuan Zhang

    School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China

    Contribution

    Data curation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

33. Patrick Walker

    MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Investigation, Methodology, Writing – review and editing

    Competing interests

    received consultancy fees from Pfizer for a lecture on statistical inference using infectious disease models and payment or honoraria from IS Global, Barcelona (for a lecture on statistical inference using infectious disease models) and Lancet Global Health (for reviewing an article). The author has no other competing interests to declare

34. Charles Whittaker

    MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing - original draft, Writing – review and editing

    For correspondence

    charles.whittaker16@imperial.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5003-2575

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