Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.

Background: Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods: To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion: Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021.

Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.

Social contact patterns among employees in U.S. long-term care facilities during the COVID-19 pandemic, December 2020 to June 2021.

OBJECTIVE: We measured contact patterns using social contact diaries for 157 U.S. long-term care facility employees from December 2020 - June 2021. These data are crucial for analyzing mathematical transmission models and for informing healthcare setting infection control policy. RESULTS: The median number of daily contacts was 10 (IQR 8-11). Household contacts were more likely partially masked than fully masked, more likely to involve physical contact, and longer in duration compared to facility contacts.

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021.

Non-pharmaceutical interventions minimize social contacts, hence the spread of SARS-CoV-2. We quantified two-day contact patterns among USA employees from 2020-2021 during the COVID-19 pandemic. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic diaries. Mean (standard deviation) contacts reported by 1,456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We modeled SARS-CoV-2 transmission at home, work, and community. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained very low in work settings. Contact data are important to parameterize models of infection transmission and control. Teaser: Changes in social contact patterns shape disease dynamics at workplaces in the USA.