Social Contact Patterns and Implications for Infectious Disease Transmission: A Systematic Review and Meta-Analysis of Contact Surveys

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, but low-income settings were 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).


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Previous outbreaks of Ebola (Mbala-Kingebeni et al., 2019), influenza (Khan et al., 2009), and the 26 ongoing COVID-19 pandemic have highlighted the importance of understanding the transmission 27 dynamics and spread of infectious diseases, which depend fundamentally on the underlying patterns 28 of social contact between individuals. Together, these patterns give rise to complex social networks 29 that influence disease dynamics (Eubank et al., 2004;Ferrari et al., 2006;Firth et al., 2020;Zhang et 30 al., 2020), including the capacity for emergent pathogens to become endemic (Ghani and Aral, 2005;31 Jacquez et al., 1988), the overdispersion of the offspring distribution underlying the reproduction 32 number (Delamater et al., 2019) and the threshold at which herd-immunity is reached (Fontanet and 33 Cauchemez, 2020;Mistry et al., 2021). They can similarly modulate the effectiveness of non-34 pharmaceutical interventions (NPIs), such as school closures and workplace restrictions, that are 35 typically deployed to control and contain the spread of infectious diseases . 36 37 Social contact surveys provide insight into the features of these networks, which is typically achieved 38 through incorporating survey results into mathematical models of infectious disease transmission 39 frequently used to guide decision making in response to outbreaks (Chang et al., 2021;Davies et al., 40 2020). Such inputs are necessary for models to have sufficient realism to evaluate relevant policy 41 questions. However, despite the known importance of contact patterns as determinants of the 42 infectious disease dynamics, our understanding of how they vary globally remains far from complete. 43 Reviews of contact patterns to date have focussed on High-Income countries (HICs) (Hoang et al., 44 2019). This is despite evidence that social contact patterns differ systematically across settings in ways 45 that have material consequences for the dynamics of infectious disease transmission and the 46 evolution of epidemic trajectories (Prem et al., 2017;Walker et al., 2020). Previous reviews has also 47 studies contained information on main demographic variables such as age and gender. Availability of 76 other variables analysed here for each study are listed in Supplementary Table 2. All studies reported 77 the number of contacts made in the past 24 hours of (or day preceding) the survey. The definitions of 78 contacts were broadly similar across studies (Supplementary Table 1). Specifically, contacts were 79 defined as skin-to-skin (physical) contact or a two-way conversation in the physical presence of 80 another person. All studies scored above 65% of the items on the AXIS risk of bias tool, suggesting 81 good or fair quality (Supplementary Table 3). Among all participants 47.5% were male, 30.1% were 82 aged under 15 years and 7.2% were aged over 65 years. The majority (83.4%) of participants were 83 asked to report the number of contacts they made on a weekday. A large proportion (34.1%) of 84 respondents lived in large households of 6 or more people but this was largely dependent on income 85 setting (LIC/LMIC=63.2%, UMIC=35.9%, HIC=4.9%). Among school-aged children (5 to 18 years), 88.1% 86 were students, and 59.1% of adults aged over 18 were employed. 87 88

Total number of contacts and contact location 89
The median number of contacts made per day across all the studies was 9 (IQR= 5-17), and was similar 90 across income strata , UMIC=8[5-16], HIC=9[5-17]; Table 1). There was a large 91 variation in contact rates across different studies, with the median number of daily contacts ranging 92 from 4 in a Zambian setting (Dodd et al., 2015) to 24 in an online Thai survey (Stein et al., 2014). When 93 stratifying by study methodology, median daily contacts was higher in diary-based surveys compared 94 to interview-/questionnaire-based surveys, which was true across all income strata (Table 1,  was substantial variation between studies and across income-strata in how the number of daily 99 contacts varied with age ( Figure 1A-C). Across UMICs and HICs, the number of daily contacts made by 100 participants decreased with age, with this decrease most notable in the oldest age-groups (adjCRR for 101 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  Figure 1D), but no effect of gender on total daily contacts for 106 other income strata (CRR[95%CrI]: UMIC=1.01[0.98-1.04], HIC=0.99[0.97-1.02]). There were also 107 differences in the number of daily contacts made according to the methodology used and whether 108 the survey was carried out on a weekday or over the weekend -in both instances, contrasting effects 109 of these factors on the number of daily contacts according to income strata were observed . 111

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We also examined the influence of factors that might influence both the total number and location 113 (home, work, school and other) of the contacts individuals make. Across all income-strata, students 114 (defined as those currently in education, attending school and aged between 5 and 18 years) made 115 more daily contacts than non-students aged between 5 and 18 ( Figure 1D-F). Total daily contacts 120 increased with household size (Figure 2A, Supplementary Figure 2) across all income-strata; 121 individuals living in large households (6+ members) had 1.47 (95%CrI:1.32-1.64) (LIC/LMICs), 2.58 122 (95%CrI:2.37-2.80) (UMICs) and 1.51 (95%CrI:1.40-1.63) (HICs) times more daily contacts than those 123 living alone, after accounting for age and gender ( Figure 1E-F). Sensitivity analyses excluding additional 124 contacts (as defined in Methods), showed little difference in effect sizes, and were strongly correlated 125 with the effect sizes shown in Figure 1D-F (Supplementary Figure 3). 126 127 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ;https://doi.org/10.1101https://doi.org/10. /2021 Motivated by this suggestion of strong, location-related (school, work and household) effects on total 128 daily contact rates, we further explored the locations in which contacts were made. Contact location 129 was known for 314,235 contacts, 42.7% of which occurred at home (13.1% at work, 12.5% at school 130 and 31.7% in other locations). Across income-strata, there was significant variation in the proportion 131 of contacts made at home -being highest in LICs/LMICs (68.3%) and lowest in HICs (37.0%) ( Figure  132 2B). Age differences were also observed in the number of contacts made at home, particularly for 133 LICs/LMICs ( Figure 2C-2D). Relatedly, a higher proportion of contacts occurred at work and school 134 (14.6 % and 11.3%) in HICs compared to LICs/LMICs (3.9% and 5.2%, respectively; Supplementary 135 Figure 4). Strong, gender specific patterns of contact location were also observed. Across all income 136 strata males made a higher proportion of their contacts at work compared to females, although this 137 difference was largest for LICs/LMICs (Supplementary Figure 4). Further, we found significant variation 138 between income strata in median household size (7 in LICs/LMICs, 5 in UMICs and 3 in HICs). This trend 139 of decreasing household size with increasing country income was consistent with global data (  there was a higher proportion observed for adults aged 80 or over ( Figure 3A-C). Contacts made by 153 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint However, it was only in HICs that there was a significant effect of being a student (adjOR=1.18, 95%CrI: 180 1.09-1.27; Figure 4D-F) on the proportion of contacts lasting ≥1 hour. For all income strata, the 181 proportion of contacts >1h increased with increasing household size ( Figure 4D-F). 182 183

Assortativity by age and gender 184
Twelve studies collected information on the gender of the contact and eight studies contained 185 information on age allowing assignment of contacts to one of the three age-groups described in 186 Table 2, Supplementary Text 2). We found evidence to suggest that contacts 187 were assortative by gender for all income strata, as participants were more likely to mix with their 188 own gender (Supplementary Text 2). Mixing was also assortative by age, with participants more likely 189 to contact individuals who belonged to the same age group this degree of age-assortativity was lowest 190 for LICs/LMICs, where only 29% of contacts made by adults were with individuals of the same age 191 group. By contrast, in HICs we observed a higher degree of assortative mixing, with most contacts 192 (51.4%) made by older adults occurring with individuals belonging to the same age group. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; Across the collated studies, the total number of contacts was highest for school-aged children. This is 204 consistent with previous results from HICs (Béraud et al., 2015;Fu et al., 2012;Hoang et al., 2019;205 Ibuka et al., 2016;Lapidus et al., 2013) and shown here to be generally true for LICs/LMICs and UMICs 206 also. Interestingly however, we observed differences in patterns of contact in adults across income 207 strata. Whilst contact rates in HICs declined in older adults, this was not observed in LICs/LMICs, where 208 contact rates did not differ in the oldest age-group compared to younger ages. This is consistent with 209 variation in household structure and size across settings, with nearly two thirds of participants aged 210 65+ in included LIC/LMIC surveys living in large, likely intergenerational, households (6+ members), 211 compared to only 2% in HICs. HICs were also characterised by more assortative mixing between age-212 groups, with older adults in LICs/LMICs more likely to mix with individuals of younger ages, again 213 consistent with the observed differences between household structures across the two settings. These 214 results have important consequences for the viability and efficacy of protective policies centred 215 around shielding of elderly individuals (i.e. those most at risk from COVID-19 or influenza) in these 216

settings. 217
Our results support the idea of households as a key site for transmission of respiratory 218 pathogens (Thompson et al., 2021), with the majority of contacts made at home. However, its relative 219 importance compared to other locations is likely to vary across settings. Our results highlighted 220 significant differences across income settings in the distribution of contacts made at home, work and 221 school. The proportion of contacts made at home was highest for LIC/LMICs, where larger average 222 household sizes were associated with more contacts, more physical contacts, and longer lasting 223 contacts. By contrast, participants in HICs tended to report more contacts occurring at work and 224 school. The lower number of contacts at work in LIC/LMIC may be explained by the types of 225 employment (e.g agriculture in rural surveys) and a selection bias (women at home/homemakers 226 more likely to be surveyed in questionnaire-based surveys). Such differences would have 227 consequences for which locations contribute most to transmission and in turn modulate the efficacy 228 of different NPIs, such as workplace closures. Our analyses similarly highlighted significant variation in 229 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; the duration and nature of contacts across settings. Contacts made by female participants in 230 LICs/LMICs were more likely to be physical compared to men, whilst the opposite effect was observed 231 for HICs and UMICs, potentially reflecting context-specific gender roles. In all settings, we observed a 232 general decline of physical contacts with age, except in the very old (Mossong et al., 2008) Similarly, we were unable to examine the impact of socioeconomic factors such as household wealth, 239 despite experiences with COVID-19 having highlighted strong socio-economic disparities in both 240 transmission and burden of disease (De Negri et al., 2021;Routledge et al., 2021;Ward et al., 2021;241 Winskill et al., 2020) and previous work suggesting that poorer individuals are less likely to be 242 employed in occupations amenable to remote working (Loayza, 2020). A lack of suitably detailed 243 information in the studies conducted precludes analysis of these factors but highlights the importance 244 of incorporating economic questions into future contact surveys, such as household wealth and house 245 square footage. Other factors also not controlled for here, but that may similarly shape contact 246 patterns include school holidays or seasonal variations in population movement and composition that 247 we are unable to capture given the cross-sectional nature of these studies. 248 Another important limitation to the results presented here is that we are only able to consider a 249 limited set of contact characteristics (the location and duration of the contact and whether it was 250 physical). Previous work has highlighted the importance of these factors in determining the risk of 251 respiratory pathogen transmission (Chang et al., 2021;Dunne et al., 2018;Olivier le Polain de Waroux 252 et al., 2018;Neal et al., 2020;Thompson et al., 2021), but only a limited number of studies reported 253 whether a contact was "close" or "casual" (Kwok et al., 2018(Kwok et al., , 2014O. le Polain de Waroux et al., 2018) 254 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint and whether the contact was made indoors or outdoors (Wood et al., 2012); both factors likely to 255 influence transmission risk (Bulfone et al., 2021;Chu et al., 2020). More generally, the relevance and 256 comparative importance of different contacts to transmission likely varies according to the specific 257 pathogen and its predominant transmission modality (e.g. aerosol, droplet, fomite etc). It is therefore 258 important to note that these results do not provide a direct indication of explicit transmission risk, but 259 rather an indicator of factors likely to be relevant to transmission. Relatedly, it is also important to 260 note that the studies collated here were all conducted prior to the onset of the SARS-CoV-2 pandemic. 261 Previous work has documented significant alterations to patterns of social contact in response to 262 individual-level behaviour changes or government implemented NPIs aimed at controlling SARS-CoV-263 2 spread, but detailed analysis of changing contact patterns is contingent on both an understanding 264 of baseline contact patterns as detailed in the studies collated here as well as longitudinal sampling of 265 how contacts patterns change over time, which is available for only a limited number of settings (Jarvis 266 et al., 2021(Jarvis 266 et al., , 2020Liu et al., 2021). Description of contact location was also coarse and precluded more 267 granular analyses of specific settings, such as markets, which have previously been shown to be 268 important locations for transmission in rural areas (Grijalva et al., 2015). 269 Heterogeneity between studies was larger for LICs/LMICs and UMICs, which we partly accounted for, 270 through fitting random study effects. These study differences may be attributed to the way individual 271 contact surveys were conducted, making comparisons of contact patterns among surveys more 272 difficult (e.g. prospective/retrospective diary surveys, online/paper questionnaires, face-to-273 face/phone interviews, and different contact definitions). For instance, there is evidence suggesting 274 that prospective reporting, which is less affected by recall bias, can often lead to a higher number of 275 contacts being reported (Mikolajczyk and Kretzschmar, 2008) and a lower probability of casual or 276 short-lasting contacts being missed. The relatively high contact rates observed in HICs may be 277 explained by the fact that all but two HIC surveys used diary methods. Our study highlights that a 278 unified definition of "contact" and standard practice in data collection could help increase the quality 279 of collected data, leading to more robust and reliable conclusions about contact patterns. Whilst we 280 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint aggregate results by income strata due to the limited availability of data (particularly in lower-and 281 middle-income countries), it is important to note that the outcomes considered here are likely to be 282 shaped by several different factors other than country-level income. Whilst some of these factors will 283 be correlated with a country's income status (e.g. household size ), many others 284 however will be unique to a particular setting or geographical area or correlate only weakly with 285 country-level data. Examples include patterns of employment, the role of women, and other 286 contextual factors. These analyses are therefore intended primarily to provide indications of prevailing 287 patterns, rather than a definitive description of contact patterns in a specific context and highlight the 288 significant need for further studies to by carried out in a diversity of different locations. 289 Despite these limitations however, our results highlight significant differences in the structure and 290 nature of contact patterns across settings. These differences suggest that the comparative importance 291 of different locations and age-groups to transmission will likely vary across settings and have critical 292 consequences for the efficacy and suitability of strategies aimed at controlling the spread of 293 respiratory pathogens such as SARS-CoV-2. Most importantly, our study highlights the limited amount 294 of work that has been undertaken to date to better understand and quantify patterns of contact across 295 a range of settings, particularly in lower-and middle-income countries, which is vital in informing 296 control strategies reducing the spread of such pathogens.  Table 4). Collated records underwent title and abstract screening for relevance, before full-text 303 screening using pre-determined criteria. Studies were included if they reported on any type of face-304 to-face or close contact with humans and were carried out in LICs, LMICs or UMICs only. No restrictions 305 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint on collection method (e.g. prospective diary-based surveys or retrospective surveys based on a face-306 to-face/phone interview or questionnaire) were applied. Studies were excluded if they did not report 307 contacts relevant to air-borne diseases (e.g. sexual contacts), were conducted in HICs, were contact 308 tracing studies of infected cases, or were conference abstracts. All studies were screened 309 independently by two reviewers (AM and CW). Differences were resolved through consensus and 310 discussion. The study protocol can be accessed through PROSPERO (registration number: 311 CRD42020191197 were contacted to request data. Extracted data included the participant's age, gender, employment, 317 student status, household size and total number of contacts, as well as the day of the week for which 318 contacts were reported. Some studies reported information at the level of individual contacts and 319 included the age, gender, location and duration of the contact, as well whether it involved physical 320 contact. Individual-level data from HICs, not systematically identified, were used for comparison, and 321 included three studies from Hong Kong(Kwok et al., 2018, 2014Leung et al., 2017) and the 8 European 322 countries from the POLYMOD study (Mossong et al., 2008). Data were collated, cleaned and 323 standardised using Stata version 14. Country-specific average household size were obtained from the 324  Table 5). Risk of bias was assessed using the AXIS critical appraisal tool used 330 to evaluate quality of cross-sectional studies (Downes et al., 2016), modified to this study's objectives 331 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint (Supplementary Table 3). Each item was attributed a zero or a one, and a quality score was assigned 332 to each study, ranging from 0% ("poor" quality) to 100% ("good" quality). The individual-level data 333 across all studies and analysis code are available at https://github.com/mrc-ide/contact_patterns (see 334 Supplementary Text 3 for data dictionary). 335 336 Statistical analysis 337 The mean, median and interquartile range of total daily unique contacts were calculated for subgroups 338 including country income status, individual study, survey methodology (diary-based or 339 questionnaire/interview-based), survey day (weekday/weekend), and respondent characteristics such 340 as age, sex, employment/student status and household size. Detailed description of data assumptions 341 for each study can be found in Supplementary Text 3. 342

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A negative binomial regression model was used to explore the association between the total number 344 of daily contacts and the participant's age, sex, employment/student status and household size, as 345 well as methodology and survey day. Incidence rate ratios from these regressions are referred to as 346 "Contact Rate Ratios" (CRRs). A sensitivity analysis was carried out that excluded additional contacts 347 (such as additional work contacts, group contacts, and number missed out, which were recorded 348 separately and in less detail by participants compared to their other contacts (Ajelli and Litvinova, 349 2017;Kumar et al., 2018;Leung et al., 2017;Zhang et al., 2020)). Logistic regressions were used to 350 explore determinants of contact duration (<1hr/1hr+) and type (physical/non-physical), using the 351 same explanatory variables as in the total contacts analyses. The proportion of contacts made at each 352 location (home, school, work and other) was explored descriptively and contacts made with the same 353 individual in separate locations/instances were considered as separate contacts. 354 355 All analyses were done in a Bayesian framework using the probabilistic programming language Stan, 356 using uninformative priors in all analyses and implemented in R via the package brms (Bürkner, 2018, 357 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ;https://doi.org/10.1101https://doi.org/10. /2021 2017). All analyses were stratified by three income strata (LICs and LMICs were combined to preserve 358 statistical power) and included random-study effects, apart from models adjusting for methodology 359 which did not vary by study. The effect of each factor was explored in an age-and gender-adjusted 360 model. All models exploring the effect of student status or employment status were restricted to 361 children aged between 5 and 18 years and adults over 18, respectively. In the remaining models 362 including all ages, age was adjusted as a categorical variable (<15, 15 to 65 and over 65 years). CRRs, 363 Odds Ratios (ORs) and their associated 95% Credible Intervals are presented for all regression models. 364 Here, we report estimates adjusted for age and gender (referred to as adjCRR or adjOR). Studies which 365 collated contact-level data were used to assess assortativity of mixing by age and gender for different 366 country-income strata by calculating the proportions of contacts made by participants that are male 367 or female and those that belong to three broad age groups (children, adults, and older adults; 368 is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; this study. The Emmes Company was contracted to perform data cleaning and data analysis of the 383 Niakhar, Senegal clinical trial data (but not the social contact network data) for this study before 384 is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint NIAID. C.G.G. declares funding from NIH (K24AI148459). G.E.P. was supported previously by General 408 Medical Sciences / National Institute of Health U01-GM070749. G.E.P was employed by the Emmes 409 Company while analyzing the Niakhar Senegal social contact network data included in this study. The 410 Emmes Company was contracted to perform data cleaning and data analysis of the Niakhar, Senegal 411 clinical trial data (but not the social contact network data) for this study before G.E.P. joined the 412 Emmes Company (in October 2015). After G.E.P. joined the Emmes Company, the sole support from 413 Emmes for this manuscript was in the form of salary support for G.E.P. 414 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. doi:10.1038/s41577-020-00451-5 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint Figure 1 -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) LICs/LMICs, B) UMICs and C) 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).
Figure 2-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. A B C D E . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 16, 2021. ; https://doi.org/10.1101/2021.06.10.21258720 doi: medRxiv preprint