A critical need for countries attempting to eliminate malaria is the ability to distinguish infections locally transmitted by a mosquito bite from those imported into an area by the movement of people. Combating local transmission requires local, focused interventions, while combating imported transmission requires control at a distant focus of infection or at international boundaries. In addition, quantifying local transmission is critical in assessing progress toward elimination, efficacy of interventions, and eventual confirmation of elimination. A number of countries aiming to eliminate malaria, including Swaziland and Namibia, have instituted passive and active surveillance systems to better understand malaria transmission. While such surveillance systems may identify the location where both symptomatic and asymptomatic infections are detected, they are limited in determining the origin of infections by relying on reported travel histories and cannot quantify the number of malaria infections resulting from each source infection. We propose to add molecular genotyping of P. falciparum infections to these surveillance systems to reconstruct malaria transmission trees, thereby determining the origin of infections and quantifying local transmission potential. 

 

While a number of methods have been used to genotype malaria parasites for various purposes, none have yet successfully tracked the origin and spread of malaria infections at a microepidemiological level. We have developed a novel genotyping method specifically to track the origin and spread of P. falciparum, overcoming earlier limitations. This method is designed to work robustly on dried blood spot (DBS) samples easily collected under field conditions, is relatively inexpensive, and provides high resolution genetic signatures at multiple loci through the P. falciparum genome, allowing for tracing of lineage through multiple transmission events. We will apply these methods to samples and data obtained from passive and active malaria surveillance in Swaziland and Namibia, two African countries poised to eliminate malaria. Taking advantage of high density sampling provided by our surveillance infrastructure and robust molecular genotyping methods, we will go beyond population level measures of genetic diversity to monitor transmission at the level of individual infections. 

 

Goal: to implement a sustainable, field-friendly system for assessing and monitoring the microepidemiology of malaria transmission throughout Swaziland and in a district of northern Namibia. Objectives: 1) classify individual malaria infections as locally transmitted (within the range of a mosquito) versus imported (requiring people movement); and 2) measure and map local malaria transmission potential, defined by the reproductive number under control (Rc, the number of secondary infections resulting from each infection). 

Design: We will collect DBS samples and relevant epidemiologic data from existing passive and active surveillance systems in Swaziland and Namibia. We will perform multilocus genotyping on DBSs from every identified infection to determine genetic relationships between malaria parasites. These data will be used to develop and then fit a formal population genetic statistical model for calculating malaria transmission trees. Transmission parameters required to address our objectives will be obtained from this analysis. Results will be compared to those obtained from traditional epidemiologic investigation.