Using Mathematical Models in Controlling the Spread of Malaria

Abstract

Malaria is an infectious disease, transmitted between humans through mosquito bites, that kills about two million people a year. We derive and analyze a mathematical model to better understand the transmission and spread of this disease. Our main goal is to use this model to compare intervention strategies for malaria control for two representative areas of high and low transmission. We model malaria using ordinary differential equations. We analyze the existence and stability of disease-free and endemic (malaria persisting in the population) equilibria. Key to our analysis is the definition of a reproductive number, R₀ (the number of new infections caused by one individual in an otherwise fully susceptible population through the duration of the infectious period). We prove the loss of stability of the disease-free equilibrium as R0 increases through R₀ = 1. Using global bifurcation theory developed by Rabinowitz, we show the bifurcation of endemic equilibria at R₀ = 1. This bifurcation can be either supercritical (leading to stable endemic equilibria for R₀ > 1) or subcritical (leading to stable endemic equilibria for R₀ < 1 in the presence of hysteresis). We compile two reasonable sets of values for the parameters in the model: for areas of high and low transmission. We compute sensitivity indices of R₀ and the endemic equilibrium to the parameters around the baseline values. R₀ is most sensitive to the mosquito biting rate in both high and low transmission areas. The fraction of infectious humans at the endemic equilibrium is most sensitive to the mosquito biting rate in low transmission areas, and to the human recovery rate in high transmission areas. This sensitivity analysis allows us to compare the effectiveness of different control strategies. According to our model, the most effective methods for malaria control are the use of insecticide-treated bed nets and the prompt diagnosis and treatment of infected individuals.