  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.
