A deterministic mathematical model for quantifiable prediction of antimalarials limiting the prevalence of multidrug-resistant malaria

Abstract

The malaria’s multidrug-resistant strain in Nigeria is prevalent and it poses a significant challenge for disease elimination. The testing for resistance is available but underutilized. Therefore, we develop a mathematical model incorporating the testing as a control strategy. This allows us to make quantifiable predictions about the effects of testing utilization on the malaria prevalence. By fitting the model to data on malaria and using field data reported in the literature, important parameters associated with the disease dynamics are estimated and calculated. First, we analyze the disease-free state of the malaria model and calculate the baseline control reproduction number. Sensitivity analysis is used to investigate the influence of the parameters in curtailing the disease. Numerical simulations are used to explore the behavior of the model solutions involving testing for resistance of the strain and wild strain malaria. We found that the implementation of testing would (a) prevent the increase of malaria prevalence from 30% to 35%, (b) significantly slow down the replacement of the wild strain by the resistant strain, and (c) avert about 6% of treatment failures. We also found that increasing mosquito death rate or reducing mosquito biting rate, mosquito birth rate, transmission to or from mosquitoes would contribute most significantly to the reduction of malaria prevalence in the community. In conclusion, the treatment failure is a significant component of the community malaria epidemic. Testing for multidrug resistance yields a significant reduction in cases with many implications regarding the containment of malaria in Nigeria.