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

Obianuju Genevieve Aguolu, Moses Chapa Kiti, Kristin Nelson, Carol Y. Liu, Maria Sundaram, Sergio Gramacho, Samuel Jenness, Alessia Melegaro, Charfudin Sacoor, Azucena Bardaji, Ivalda Macicame, Americo Jose, Nilzio Cavele, Felizarda Amosse, Migdalia Uamba, Edgar Jamisse, Corssino Tchavana, Herberth Giovanni Maldonado Briones, Claudia Jarquín, María AjsivinacLauren Pischel, Noureen Ahmed, Venkata Raghava Mohan, Rajan Srinivasan, Prasanna Samuel, Gifta John, Kye Ellington, Orvalho Augusto Joaquim, Alana Zelaya, Sara Kim, Holin Chen, Momin Kazi, Fauzia Malik, Inci Yildirim, Benjamin Lopman, Saad B. Omer

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere0301638
JournalPLoS ONE
Issue number6 June
Publication statusPublished - Jun 2024


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