A Stochastic Model for Meningococcal Disease

Abstract: This paper presents a stochastic model for Meningococcal disease. Mathematical models for diseases are an important tool in population dynamics, and their results carry real-world implications as they guide policies and vaccination strategies. While the systems being modelled are generally stochastic in nature, deterministic models are often used, possibly to the detriment of accuracy. In this project, a stochastic model is developed for Meningococcal disease in the African Meningitis belt, a region plagued by recurring epidemics, a high incidence rate of disease and significant seasonal variations. The model uses the Feller-Kendall algorithm, and is able to accurately emulate some of the specificities present in the region. Simulation results suggest that the seasonality cannot be exclusively explained by behavioural variations, but does not dismiss its influence entirely. Furthermore, there are signs that the system in a non-epidemic state can be modelled using a deterministic framework.