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

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Low- and middle-income countries (LMICs) face a high burden of communicable diseases.
• Social interaction data are crucial for infectious disease modeling and prevention but are scarce in LMICs.
• Demographic and cultural variations significantly influence disease transmission dynamics.

Purpose of the Study:

• To address the lack of social interaction data in LMICs for infectious disease modeling.
• To develop and validate novel data collection tools for characterizing social mixing patterns.
• To generate age-specific contact matrices for LMIC populations.

Main Methods:

• Qualitative (focus groups, cognitive interviews) and quantitative data collection across eight sites in Guatemala, India, Pakistan, and Mozambique.
• Development and testing of enrollment surveys, contact diaries, exit surveys, and wearable proximity sensors.
• Creation of age-specific contact matrices (physical, nonphysical, combined) and calculation of infant proximity scores.

Main Results:

• Qualitative data confirmed the feasibility and acceptability of contact diaries and wearable proximity sensors in LMICs.
• Quantitative data will enable a more accurate representation of human interactions relevant to pathogen transmission.
• Identification of key drivers of social contacts through regression analysis.

Conclusions:

• The study successfully gathered insights into the perceptions and acceptability of novel data collection tools in LMICs.
• The generated social mixing data will enhance the accuracy of mathematical models for LMIC populations.
• Developed tools are adaptable for future research on infectious disease transmission in similar settings.