Paper Information

The result showed that if there had been 80% sustained level LLINs usage as proposed by NMEP since 2015, there would have been about 5.2 million malaria cases averted, which corresponds to 97.98% reduction. However, moving forward, if 80% usage can be achieved and sustained, about 3 million malaria cases would be averted by May 2029, signifying an impressive reduction of 78.93% in incidence.

We conclude that in order to significantly reduce malaria incidence in Kebbi State to its bearest minimum, health policymakers and decision makers in Nigeria should prioritise scaling up LLINs distribution and usage in the state.

Malaria, in Nigeria, is a disease of public health concern that has caused both morbidity and mortality, with the highest prevalence in Kebbi State. Long-lasting insecticidal nets (LLINs) have been instrumental in controlling the burden of the disease. This study aims to assess the effect of LLINs on malaria transmission dynamics in Kebbi State, Nigeria.

Routine data for the confirmed uncomplicated malaria cases in Kebbi State, Nigeria, from January 2015 to May 2024 were used to understand the transmission dynamics within the population. A deterministic compartmental model was developed to capture the malaria transmission dynamics in Kebbi State. Qualitative analysis was carried out, establishing the positivity and boundedness of solutions to ensure the biological feasibility of the model. The disease-free equilibrium was analyzed for stability, revealing under which condition the disease is eradicated. Effective reproduction number, Re , was derived, governing the disease persistent in the presence of intervention. The endemic equilibrium was further examined to indicate situations where the disease persist in the population. The model was fitted to Kebbi state monthly malaria cases using the least squares estimation method implemented in R. Numerical simulations were performed using the R software. Prediction scenarios of malaria cases considering different usage levels of LLINs (38.2%, 50% and 80%) are visualized through the simulation of the malaria model.