Malaria across borders: Measuring imported infections and contributions to local transmission in Uganda and Zimbabwe

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PROJECT SUMMARY/ABSTRACT Malaria cases and deaths primarily caused by Plasmodium falciparum have declined significantly in sub- Saharan Africa as a result of the broad deployment of vector control and effective clinical management. In low to moderate-transmission settings slated for elimination, imported cases become an increasingly important epidemiological consideration. In these settings, imported cases may a) represent a high but poorly defined proportion of the overall malaria burden, b) result in secondary transmission that can impede local elimination efforts, and c) may require additional or alternative interventions than standard control measures. Imported cases, when currently evaluated at all, are operationally defined as infections acquired outside of a defined geographic area and identified based on travel history. However, lack of capture of asymptomatic infections together with variable quality of travel history collection limit the utility of this standard approach to identifying imported infections and quantifying their role in transmission. Further, there are no routinely collected data that would allow evaluation of the impact of imported cases on local transmission. In this proposal, we will collect detailed travel histories, perform active surveillance for asymptomatic infections, and generate parasite genomic data to more accurately define the role of imported infections in two representative border regions of sub-Saharan Africa (Tororo District, Uganda and Mutasa District, Zimbabwe) that leverage substantial surveillance infrastructure from the NIH-funded International Centers of Excellence for Malaria Research (ICEMR) network and will employ active (via a longitudinal study) and passive (via health facility surveillance) designs to capture asymptomatic and symptomatic infections. We propose the following Specific Aims.1) To quantify and characterize imported malaria infections. We will use a probabilistic approach to classify infections as imported or local via detailed travel and other behavioral survey data and determine the travel patterns and risk factors associated with importation. 2) To determine the impact of importation on local transmission and identify appropriate targeted interventions. We will use parasite genomics and epidemiological data to define local transmission and the impact of imported infections, taking advantage of dense sampling of symptomatic and asymptomatic infections in focused geographies. We will use a robust set of statistical modeling approaches, including Bayesian estimation of transmission networks incorporating all genomic and epidemiologic data. We will use these data to model the predicted impact of various combinations of targeted interventions. The expected outcome of the proposed research is evidence on appropriate surveillance methods for imported malaria infections and on the contribution of these infections to sustaining transmission. By identifying ways to better target interventions, these results will impact national and international malaria control efforts as they strive for elimination and require evidence on how to mitigate the risk of imported malaria infections.