Malaria Early Warning Application for Individual Risk Assessment

As one of the oldest known diseases to inflict humanity (since the Agricultural Revolution about 12,000 years ago), malaria has proven to be a significant global challenge. Many intervention strategies have been undertaken in the last few decades such as widespread insecticide-treated bed nets (ITN), long-lasting insecticidal nets (LLIN) and indoor residual spraying (IRS). Yet even with great success, malaria continues to be a ravaging disease requiring inventive solutions. In this study, we develop a malaria early warning system, which utilizes an adapted Ross-MacDonald model to assess individual risk and disease epidemiology. Strategies for achieving a disease-free equilibrium state are also shown by performing local asymptotic stability analysis. It is important to note that the stages of the mosquito life-cycle are highly influenced by weather conditions, both in the aquatic and adult stages, as well as by the use of insecticides (either through ITN/LLIN use or via IRS). Therefore, we consider regional data parameters, such as weather conditions, parasite rate and resistance, to estimate deviated risk from the baseline, with the final product being a progressive web application (i.e. a web and mobile app). Such a product has widespread application primarily in holoendemic areas in Africa to inform both native and tourist populations of their relative risk of contracting malaria.