Abstract
Despite progress, malaria remains a public health problem. P. vivax, the main malaria species in the Americas and much of Asia, poses major challenges for control and elimination because of its complex transmission and persistence despite interventions. In the Amazon, transmission is highly heterogeneous across space, time, and individuals, with variation between and within communities, across seasons, and among people with different exposure levels. The high proportion of asymptomatic infections missed by passive surveillance, together with mobility-driven connectivity, further complicates control. Compartmental mathematical models provide a framework to describe transmission. This PhD project aims to extend, calibrate, and validate a deterministic compartmental model of P. vivax transmission for low-endemic Amazonian settings. I will first analyse epidemiological, entomological, and mobility data from rural communities in the Peruvian Amazon to estimate key indicators. I will then adapt an existing metapopulation model to incorporate hypnozoite dynamics, infection status, seasonality, risk heterogeneity, and connectivity between communities. The model will be calibrated and validated with surveillance data, and later used to compare LLINs, IRS, and MDA with targeted strategies for high-risk groups. This project will provide a modelling tool tailored to the Amazonian context and support malaria control and elimination planning in Peru and other endemic settings.
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