Effectively Linking Molecular Informatics and Network Analytics to Reduce Malaria (ELIMINAR-Malaria) - Understanding human mobility is critical to achieving malaria elimination. This is particularly important in regions such as the Amazon, which has the goal of eliminating malaria by 2030, and where declining incidence rates are leading cases to become increasingly clustered into networks of villages that are connected due to human mobility and other environmental factors. These networks and villages are therefore at risk of malaria importation. However, efforts to understand how human mobility contributes to importation and diffusion are either incomplete or ill-suited to the Amazon context. For example, while mobile phone data have been used to track mobility in a number of settings, coverage is nonexistent in the rural Amazon, and is otherwise unable to capture the many different reasons people travel. Other approaches in the region have leveraged GPS tracking or individual-level, participatory mapping approaches, which cannot be easily scaled to collect data across an entire region to identify metacommunities or quantify mobility within and between them. Moreover, none of these approaches are able to track parasite populations. However, the molecular studies that do so are either cost-prohibitive or have insufficient coverage of the malaria genome to inform about importation in increasingly low transmission settings. Finally, all of these studies ignore the crucial role that community network ties have on human mobility and, by extension, malaria parasite structure and importation. Therefore, the objective of this proposal is to effectively link scalable and actionable network and molecular tools to understand the ways in which human mobility contributes to malaria population structure, importation, and diffusion in the northern Peruvian Amazon. Aim 1 of this proposal focuses on collecting primary community network data to: 1) quantify human mobility patterns, including drivers of mobility and seasonal variability, 2) identify networks of villages based on mobility patterns, and 3) quantify the effects of mobility on malaria transmission. Aim 2 will link these community network data with genomic surveillance of malaria parasites to: 1) identify how network structure contributes to malaria population structure, and 2) identify source and sink communities of malaria transmission, and 3) quantify the effect of different types of human mobility on malaria importation both between villages and between networks of villages. The overarching hypothesis is that network structure is a fundamental driver of malaria population structure, while human mobility within and between networks drives importation and diffusion. This proposal will make significant contributions to ongoing malaria surveillance and control in the region, and be conducted in collaboration with Peru’s Malaria Zero program, which his tasked with eliminating malaria in the region.
