Mapping road network communities for guiding disease surveillance and control strategies

TL;DR

This paper develops methods to map road network communities in Africa, revealing how connectivity influences disease spread and aiding targeted surveillance and control strategies in low and middle income regions.

Contribution

It introduces novel techniques for identifying road connectivity communities and key link routes, integrating disease prevalence data to inform intervention planning.

Findings

Many highly-connected communities cross national borders.

Communities change when integrating malaria prevalence data.

The approach helps identify key areas for disease surveillance.

Abstract

Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance.