Despite dramatic decreases in malaria deaths over the past decade, recent reports show these declines are stalling with an increase in clinical cases last year. New strategies are needed to restart progress toward malaria elimination, with a demand for strategies that detect imported malaria and measure the impact of newly imported infections on onward transmission. Pathogen sequencing has emerged as a paradigm- shifting approach to understand infectious disease transmission. Resulting genomic epidemiology strategies are routine for non-sexually recombining pathogens like Ebola, Zika, Influenza, and methicillin-resistant Staphylococcus aureus (MRSA), but are not readily applicable to sexually-recombining pathogens like malaria. Population genetic tools that account for inheritance and recombination open the door for using genomic sequencing strategies that leverage parasite relatedness to inform malaria transmission networks. The main question we want to answer is whether genetic data can provide precise information about infection origin and connectivity (i.e., degree of genetic relatedness) between infections. The goal of this proposal is to develop a genetic toolkit that can (1) determine if a malaria infection is local or imported, and (2) reveal linkages (e.g., shared ancestry) between malaria infections. A ?proof of concept? study will test this genetic approach in northern Senegal, where the few remaining cases are thought to result from imported malaria. Our central hypothesis is that local and imported malaria infections are genetically distinct, and that infections found close to each other share genetic ancestry consistent with transmission.
The specific aims of the current application are: 1. To determine whether a malaria infection is local or imported using genetic relatedness metrics. When malaria prevalence is very low and few cases remain, the choice of an intervention strategy depends critically on identifying whether infections are imported or the result of local transmission. Genetic methods based on parasite relatedness will be developed to differentiate local from imported infections. 2. To define infection linkages using genetic relatedness to infer possible transmission networks. Recent genetic relatedness between infections can be used to create probabilistic network plots. These networks will be benchmarked against current epidemiological modeling approaches to validate and assess the value of genetic approaches for transmission chain identification.
Genomic epidemiology applied to human pathogens like Ebola, Zika, Influenza, and methicillin- resistant Staphylococcus aureus (MRSA) provides a paradigm-shifting approach that reveals transmission patterns and guides public health decision-makers. In this proposal, we develop genomic methods that quantify malaria parasite relatedness and apply them to northern Senegal as a ?proof of concept? to differentiate imported from locally transmitted infections and reveal patterns of transmission. This knowledge will help malaria program officials to select the most appropriate interventions based upon transmission patterns to achieve malaria elimination under resource constrained conditions.