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Increasing temperatures have raised concerns over the potential effect on disease spread. Temperature is a well known factor affecting mosquito population dynamics and the development rate of the malaria parasite within the mosquito, and consequently, malaria transmission. A sinusoidal wave is commonly used to incorporate temperature effects in malaria models, however, we introduce a seasonal malaria framework that links data on temperature-dependent mosquito and parasite demographic traits to average monthly regional temperature data, without forcing a sinusoidal t to the data. We introduce a spline methodology that maps temperature-dependent mosquito traits to time-varying model parameters. The resulting non-autonomous system of differential equations is used to study the impact of seasonality on malaria transmission dynamics and burden in a high and low malaria transmission region in Malawi. We present numerical simulations illustrating how temperature shifts alter the entomological inoculation rate and the number of malaria infections in these regions. 

Vaccination is among the most effective means of preventing and controlling infectious disease outbreaks. Mathematical models can be used to identify the optimal allocation of vaccine among various groups when host populations are heterogeneous. Population heterogeneity may affect individual decision-making and government policy. We show that mixing among sub-populations can profoundly influence the optimal vaccination allocation. Centralized and decentralized programs are examined, accounting for individual behavior and economic constraints. We also compare approaches to modeling transitions between epidemiological classes by epidemiological and economic modelers, and identify key differences.