Researcher ORCID Identifier

https://orcid.org/0009-0006-7462-4256

Graduation Year

2023

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Mathematics

Second Department

Physics

Reader 1

Christina Edholm

Reader 2

Adam Landsberg

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Terms of Use for work posted in Scholarship@Claremont.

Rights Information

2023 Kaia M Smith

Abstract

Malaria is a vector-borne disease that continues to be one of the deadliest in the world, despite persistent efforts to eradicate it. It has been well-documented that there is a relationship between malaria transmission and climatic factors such as temperature. We build off of the work of previous mathematical models which have examined the effects of temperature on malaria transmission, formulating a model with temperature-dependent parameters. For both this model and its autonomous counterpart with fixed parameter values, we calculate the basic reproductive number R0. We find that increases in temperature increase R0, and that the autonomous model underestimates R0 significantly. Finally, we consider parameter identifiability. We find that the constant model parameters are not identifiable using two years of simulated daily case data. Considering the model's identifiability with a smaller number of parameters, we find that one year of daily case data is likely to be insufficient even when only a small number of parameters are estimated. However, using weekly data rather than daily data does not appear to make a large difference in terms of practical identifiability.

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