Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar.

Kayla Kauffman,Courtney S Werner,Michelle Pender,James Moody,Pablo Tortosa,Jean Yves Rabezara,James P Herrera,Charles Nunn,Voahangy Soarimalala,Georgia Titcomb,Randall Kramer,Alma Solis,Peter J Mucha,Julie Teresa Shapiro

doi:10.1098/rsif.2021.0690

Abstract

Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.

Highlights

Infectious diseases are a major threat to human health, the global economy and international security [1]
Identifying heterogeneities in the contact patterns among host individuals is important for controlling infectious disease transmission, as this heterogeneity influences superspreading and outbreak size [2,3,4,5]
To account for days when participants did not wear the GPS, we calculated the daily 99% minimum convex polygon (MCP) and excluded days for which the entire 99% MCP fell within 1 ha, based on two assumptions: (i) everyone leaves their house regularly to access water for bathing, outdoor latrines and agricultural land outside the village (K Kauffman & CS Werner 2019, personal observation); (ii) studying the trajectories of individuals with less than 1 ha MCP, we found that they followed a ‘starburst’ pattern, indicating that the total area was likely to be the result of the scatter of inaccurate GPS points

Summary

Introduction

Infectious diseases are a major threat to human health, the global economy and international security [1]. Identifying heterogeneities in the contact patterns among host individuals is important for controlling infectious disease transmission, as this heterogeneity influences superspreading and outbreak size [2,3,4,5]. The construction and analysis of networks provide a powerful and increasingly used approach to investigate disease transmission pathways [2,3,4,5]. Despite the interest in applying network science to investigate disease transmission, few studies have considered the types of data to use in generating networks [7,8], which can include survey questions, spatiotemporal data or proximity loggers. The sampling methods should capture contact patterns that are relevant to the transmission mode of the infectious organism

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