This investigation seeks to define a temporal and spatial pathway for reorienting malaria control into elimination by developing a landscape molecular epidemiology (LME) framework. Understanding the spatial aspects of malaria transmission is important because this information can be used to improve the efficiency of resource allocation and to identify areas at higher risk of reintroduction once elimination has been achieved locally. A priority in control programs is the identification of areas that sustain malaria transmission (hotspots) through the migration of infected individuals to other sites. This is particularly critical in countries with low transmission seeking to move from control to elimination. However, identifying malaria hotspots in such settings is often challenging both because of the increased prominence of stochastic effects in the disease dynamics and because of idiosyncratic features of the relatively small numbers of individuals that participate i transmission. This situation is made worse by the limited information on Plasmodium vivax, the most prevalent parasite outside Africa. Furthermore, whereas genetic information can be used to infer parasite dispersal between populations, the fact that malaria is endemic makes it difficul to interpret this data since the population structure of the parasite is influenced by short-term and long-term processes. To solve this problem, our LME approach will use a combination of epidemiological, genetic and environmental data to ascertain how malaria incidence is affected by the present-day connectivity of parasite populations. By developing a suite of epidemiological models that incorporate genetic information and by using landscape genetic methodologies, the proposed LME will be able to characterize the genetic structure of parasite populations on multiple spatial and temporal scales. This project will build on extensive preliminary studies in Colombia as part of a Malaria Research Center, CLAIM, currently supported by NIAID. It has three specific aims. First, we will characterize the influence of landscape features on the spatial and temporal structure of P. falciparum (Pf) and P. vivax (Pv) populations. Second, we will develop spatially-explicit individual-based models (IBMs) of Pf and Pv dynamics that relate malaria risk to spatial connectivity of parasite populations inferred from molecular data. Third, we will utilize our models to define a spatially-explicit pathway for malaria elimination in Colombia. This study will lead to several innovations. First, whereas methods integrating epidemiology and population genetics have been applied to many viruses and bacteria, malaria has lagged behind in these developments. Second, we will use remote sensing and multi-criteria decision analysis (MCDA) to quantify malaria risk across the landscape as a function of factors such as precipitation and land cover, and we will validate and refine these predictions using the results of our genetic analyses. Third, the information gathered in the present study will have implications for other areas of Asia and South America where P. vivax is prevalent. To the best of our knowledge, this is one of the few studies that can compare the two major malaria parasites within a common setting.

Public Health Relevance

Malaria remains a global health threat. The proposed landscape molecular epidemiology (LME) approach utilizes genetic and epidemiological data;its aim is to define a spatially-explicit pathway for reorienting control into elimination after malari programs have reduced disease burden and local elimination becomes possible. Specifically, we will identify malaria foci that are interconnected by the movements of infected individuals. This will allow their coordinated control.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
High Priority, Short Term Project Award (R56)
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Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
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Joy, Deirdre A
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Arizona State University-Tempe Campus
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United States
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