During the last decade, control efforts have been successful in decreasing the malaria burden in Peru. Nevertheless, the impact has not been uniform, mainly because of the variations in transmission patterns. A key element in control and elimination efforts is a good understanding of malaria transmission dynamics, which is a crucial determinant of the disease burden. This research project proposes a comprehensive statistical analysis and modelling of retrospective and prospective data that should lead to models able to predict future changes in transmission under different intervention strategies and therefore inform policy decisions. First, simple mathematical models will be developed using data from national health information systems and available data on key parameters of malaria transmission to predict the malaria burden in Peru, identifying the differences between the Northern Coast and the Amazon Region. Second, statistical models will be developed and validated to forecast malaria incidence based on meteorological and environmental conditions in the Northern Coast and the Amazon Region. Third, a multilevel analysis of a prospective nested casecontrol will be performed to identify individual, household and environmental factors associated with malaria infection. Finally, using collected data from previous aims, a complex mathematical model will be developed to predict the impact of interventions aiming at malaria control and elimination in Peru.
|Effective start/end date
|1/01/11 → 16/11/15
IWETO expertise domain
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