Malaria represents a global threat to health and security. Out of 99 countries with endemic malaria, 32 are in the process of eliminating malaria within their borders, and one major concern these countries face is the potential reintroduction of malaria post-elimination. The economic cost of elimination is typically dominated by management of imported malaria, and the occurrence of post-elimination outbreaks of malaria in countries such as Grenada, Trinidad and Tobago, and Jamaica have been attributed to reintroduction from other countries. Using large scale mobile phone and malaria prevalence datasets to inform human movement and transmission, we are parameterizing an agent-based malaria transmission model to quantify the spatial risk of a malaria epidemic in countries seeking to eliminate malaria, including Namibia and the Dominican Republic.
Understanding human movement between regions with different risk of malaria transmission is an important element of malarial control. Mobile phone datasets may provide a solution, however, mobile phone networks may not cover the entire region of interest. In collaboration with a team of interdisciplinary researchers, I am developing new methods to predict human movement based on landscape context:
This research has been published as:
Caughlin T.T., Ruktanonchai N., Acevedo M.A., Lopiano K., Prosper O., Eagle N., Tatem A.J. Geographic context predicts community membership in a mobile phone communication network. PLoS ONE 8(2): e56057. doi:10.1371/journal.pone.0056057.