ASTMH 2014, Andrew Tatem: "Mapping seasonal interactions between population movements and malaria transmission for strategic malaria elimination planning"
In collaboration with ASTMH, ImageAV & presenters, MESA brings you this webcast.
Title: Mapping seasonal interactions between population movements and malaria transmission for strategic malaria elimination planning
This talk forms part of the Scientific Session on 'Malaria Elimination' at ASTMH. In most countries that are planning for malaria elimination a strong seasonality in malaria transmission can be observed. This often drives the timing of intervention and surveillance efforts, and the seasonal patterns can potentially be exploited to optimally target resources for achieving elimination. Factors that have received lesser attention in designing elimination strategies are population movements, their seasonal patterns and their demographic composition. As a country or area transitions towards malaria elimination, imported cases make up an increasingly larger proportion of those seen, and the importance of accounting for population movements rises. Throughout the world, the volumes and major routes of population movements tend to follow seasonal patterns, with certain times of year showing significantly greater amounts of movement than others, and this varying by demographic groupings. These movements can impact substantially on the dispersal of parasites in a region, depending on how the timings and routes of seasonal movements interact with the seasonality of malaria transmission. Here, we demonstrate how the simple combination of national malaria surveillance system data, case investigation information and population mobility metrics derived from mobile phone records can inform on these seasonal interactions, using Namibia as an example. Using anonymized cellphone call detail records to determine the mobility patterns of nearly 2 million residents, we show that the timing, duration of stay and magnitude of movements vary substantially across time and space, with significant movement peaks in December and January aligning with peak malaria transmission. Further, case investigation data from more than 100 malaria patients in the region with highest transmission (Zambezi) enables validation of these mobility patterns and provides valuable additional demographic and epidemiological insights into risk groups and contact patterns. The approaches presented can be updated rapidly and used to identify which regions would benefit from coordinating efforts at certain times of year and how spatially progressive elimination plans can be designed to account for the interacting seasonality in transmission and mobility.