Population-environment dynamics influencing malaria risk in the Peruvian Amazon
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This application is being submitted in response to Notice NOT-HD-01-004, "Expansion of the NICHD Mentored Research Scientist Development Award (K01): Population Research." The objectives of this MRSDA are to expand Dr. William Pan''s training and knowledge in the field of population, environment and health dynamics toward the goal of becoming an expert and independent investigator in this growing field. Dr. Pan will build upon his experience and expertise in household and use, biostatistics, multilevel modeling and spatial analysis by obtaining training in remote sensing (RS), ecology (vector ecology and biogeography), and malaria epidemiology. This new set of skills and knowledge will be obtained through a well-defined career development plan consisting of coursework, directed readings, and mentored research. Coursework and readings will be conducted in all areas, with a particular focus on RS and ecology. An expert group of mentors and collaborators have committed themselves to helping Dr. Pan achieve his goal and provide guidance for his proposed Mentored Research. The long-term objectives for his study are: (1) Identify household, community, and infrastructure factors associated with land use and land cover (LULC); and (2) Determine the extent to which LULC, and determinants thereof, are associated with malaria vector presence and human malaria risk towards the eventual control and eradication of P. vivax. The project tests the central hypothesis that community settlement areas have more diverse LULC, less land cleared, and better environmental management than areas managed by nearby labor camps; and therefore, malaria vector density will be greater in areas managed by labor camps than by household settlers. To achieve the objectives and test hypotheses, the study proposes to combine a population-environment and vector ecology study in the northern Peruvian Amazon along the Mazan and Napo Rivers. Data to be combined include survey information from households and key informants in communities and labor camps, locational data of relevant infrastructure and transportation networks, a time series of RS images, and a longitudinal sample of bodies of water to collect malaria vector larvae. Spatially-explicit models using traditional and Bayesian multilevel frameworks will be specified to test hypotheses. The findings from this study will have a wide impact on malaria prevention and control programs throughout Latin America.
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