Abstract

Malaria continues to be a significant health problem across India, with Plasmodium falciparum being the most deadly species of parasites that carry the disease. This research develops a data-driven mathematical model to predict malaria transmission dynamics in Maharashtra, focusing on three districts with varying transmission levels: Nashik, Brihan Mumbai, and Gadchiroli. A modified SIR-SI model, which incorporates both human and mosquito populations, was used to analyze the spread of Plasmodium falciparum malaria. Using real-world data from the Open Government Data Platform India, differential equations, and parameter estimation, the models were optimized for best fit. Key factors such as seasonality, immunity loss, control measures, and mosquito breeding dynamics were integrated to increase the models' accuracy. Results indicate regional differences in transmission rates, influenced by climate, population density, healthcare access, and vector control strategies. Sensitivity analysis highlights the role of human-to-mosquito transmission rates and control interventions in reducing Plasmodium falciparum malaria prevalence. These findings provide insights into targeted policy recommendations for malaria prevention and control in Maharashtra, with broader implications for other endemic regions.

Advisor

Huang, Qimin

Department

Mathematics

Disciplines

Medicine and Health Sciences

Keywords

Malaria transmission, Plasmodium falciparum, Mathematical modeling, Parameter estimation, Sensitivity analysis, Vector-borne diseases, Public health interventions, Maharashtra, India, Seasonality in malaria

Publication Date

2025

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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