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Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol
Obianuju Genevieve Aguolu1, Moses Chapa Kiti2, Kristin Nelson2
1Division of Epidemiology, College of Public Heath, The Ohio State University, Columbus, Ohio.
Medrxiv : the Preprint Server for Health Sciences
|December 18, 2023
Summary
Researchers are collecting social interaction data in low-and-middle-income countries (LMICs) to improve infectious disease modeling. This study uses novel tools to capture contact patterns, crucial for understanding pathogen transmission in diverse populations.
Area of Science:
- Epidemiology
- Global 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 strategies.
- Existing data are insufficient to capture LMIC-specific demographic and contact pattern variations impacting disease dynamics.
Approach:
- Collecting qualitative and quantitative data across eight diverse sites in Guatemala, India, Pakistan, and Mozambique.
- Utilizing focus groups and cognitive interviews to refine data collection tools (surveys, contact diaries, wearable proximity sensors).
- Developing age-specific contact matrices and analyzing infant proximity to household members to characterize social mixing patterns.
Key Points:
- Qualitative data confirmed the feasibility and acceptability of contact diaries and wearable proximity sensors in LMICs.
- Quantitative data will provide a more accurate representation of human interactions relevant to pathogen transmission.
- Findings will yield crucial social mixing data for parameterizing mathematical models specific to LMIC populations.
Conclusions:
- This study addresses a critical data gap in understanding infectious disease transmission in LMICs.
- The developed tools and methodologies can enhance disease modeling accuracy for these regions.
- The findings will support the development of more effective public health interventions in LMICs.
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