Mathematical Modelling of Malaria Transmission Dynamics in Kenya: The Role of Seasonality, Drug Resistance, and Human Movement

Background: Although there has been a remarkable improvement in controlling malaria, the clinical problem is still of public health importance in Kenya and especially in areas where there is climatic variation, which affects the transmission pattern. Seasonal rainfall has been explored as a central factor in the breeding of mosquitoes and the ensuing outbreaks of malaria, but most models have not included these ecological forces together with drug resistance and human mobility. Objective: The purpose of the study is to formulate and examine a seasonally driven malaria transmission model to represent the interaction of the dynamics of mosquito infection, antimalarial drug resistance, and human mobility in the Kenyan setting. Methods: We developed a compartmental model with drug-susceptible and drug-resistant parasite strains, categorised by stages of human infections, and mosquitoes. The model proposes seasonal forcing in a sinusoidal representation, which is staggered with the Kenyan rainfall pattern, which drives the mosquito recruitment. Inter-regional human migration is thought to take the form of a toggling migration parameter. The deSolve package in R was then used to simulate the model within a 2-year horizon. Monthly averages were then used to determine the peaks of the infection rates, and these were equated to the long (March to May) and short (October to December) rainy seasons in Kenya. Results: It was found that the results of simulations identified clear infection spikes closely corresponding to two periods of rainfall in Kenya (bimodal). The post-rainy periods when mosquitoes infected with the malaria parasites reach their peak (Q), as well as when humans are infected, were consistent, making a difference to resistant infections, which do not drop as fast as susceptible infections. The circulation of human movements enhanced the continuation and propagation of resistant infections. This indicated the environmental drivers of seasonal forcing that explained the time and magnitude of outbreaks. Conclusion: Noting the inclusion of seasonality, drug resistance, and movement in the models of malaria transmission increases their reality and predictability to a great extent. The syncing of the most significant infection-containing seasons with rainy seasons necessitates climate-tactful surveillance and intervention time. In Kenya, where the mobility of the population is high based on trade, labour migration, and between urban and rural regions, the modelling of such mobility is very important in gaining an understanding of the management of the epidemic. The model can be of great benefit in optimising vector control, deploying drugs and allocating resources regionally to malaria-endemic countries such as Kenya.