Modeling Seasonal Malaria Transmission

A Methodology Connecting Regional Temperatures to Mosquito and Parasite Developmental Traits

Authors

  • Olivia Prosper Department of Mathematics, University of Tennessee Knoxville Author
  • Katharine Gurski Department of Mathematics, Howard University Author
  • Miranda I. Teboh-Ewungkem Department of Mathematics, Lehigh University Author
  • Angela Peace Department of Mathematics and Statistics, Texas Tech University Author
  • Zhilan Feng Department of Mathematics, Purdue University Author

DOI:

https://doi.org/10.30707/LiB10.1.1682014077.793816

Keywords:

Malaria, Seasonal, Temperature-dependent, Non-autonomous, Cubic splines, Data

Abstract

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.

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Published

2023-01-24

Issue

Section

Research

How to Cite

Modeling Seasonal Malaria Transmission: A Methodology Connecting Regional Temperatures to Mosquito and Parasite Developmental Traits. (2023). Letters in Biomathematics, 10(1), 3-27. https://doi.org/10.30707/LiB10.1.1682014077.793816

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