Lassa Fever (LF), caused by Mastomys natalensis, a rodent specie, is an acute zoonotic viral hemorrhagic illness. It is endemic in most countries of West Africa, and may spread to other parts of the world, if not checked, according to the Center for Diseases Control and Prevention. In this paper, we present a spatiotemporal analysis of previous outbreaks of Lassa Fever in Nigeria, and hybrid mathematical and agent-based models to understand the dynamics of the transmission and environmental factors affecting the outbreaks. Using existing data on recorded cases and available intervention details, we model the effectiveness of these control measures on curtailing the outbreaks. According to our findings, efforts aimed at isolating newly infected individuals, coupled with treatment, appear to yield the highest efficiency amongst all studied control measures. This is closely followed by efforts aimed at controlling infected and susceptive rodent populations. In low-resource countries, it is recommended that the few available resources be channeled in this order of priority in the management of Lassa Fever cases: isolation, treatment and rodent control.

Optimum location of vaccine distribution and Emergency Operation Centers (EOCs) is imperative to ensuring prompt and efficient vaccination of eligible population in any location of interest. The proximity of these vaccination centers is likely to positively affect the decision of the target population to present themselves for vaccination. In this paper, a computational model for optimizing the number and determining the location of depots or vaccine distribution centers, and amounts of vaccines to be stocked at each center, to satisfy the needs of the local population is proposed. A modified K-means++ is used to optimize the number of required centers and the approximate locations to ensure the usage of the least possible cost. The algorithm allows planners to enter two initial specific locations as depots, thereby avoiding the usual random selection of initial points. Using geospatial and population data, the resulting clusters are divided into two, on each iteration. Heap sort is used to select the next centroid. Optimization of these locations is iteratively done, until there are no more changes. An optimized number of vaccine distribution centers for any region of interest can be obtained. It ensures that least possible cost is used. Our algorithm avoids the usual random outcomes associated with K-means and provides a more efficient clustering output, with an improved time complexity. The application of the proposed algorithm to a real-world test instance indicates its effectiveness.