Modeling Seasonal Malaria Transmission

A Methodology Connecting Regional Temperatures to Mosquito and Parasite Developmental Traits

  • Olivia Prosper University of Tennessee
  • Katharine Gurski Howard University
  • Miranda I. Teboh-Ewungkem Lehigh University
  • Angela Peace Texas Tech University
  • Zhilan Feng Purdue University
  • Margaret Reynolds West Point
  • Carrie Manore Los Alamos National Laboratory
Keywords: malaria, seasonal, temperature-dependent, non-autonomous, cubic splines, data


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 fit 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.

How to Cite
Prosper, Olivia, Katharine Gurski, Teboh-EwungkemMiranda, Angela Peace, Zhilan Feng, Margaret Reynolds, and Carrie Manore. 2023. “Modeling Seasonal Malaria Transmission”. Letters in Biomathematics 10 (1), 3–27.