An Analysis of the Hidden Markov Model for Surveilling the Transmission of Lassa Fever Epidemic Disease in Nigeria during Dry Season

Abstract

Lassa fever is an infectious viral disease that is endemic in Nigeria and other West African countries. Early detection and response to outbreaks of the disease are critical to prevent its spread and reduce illnesses and death. Finding some mathematical patterns that explain the mechanisms of Lassa fever transmission, as well as a thorough understanding of the biological contributing to affecting the disease, are necessary in putting in place a surveillance system with a view to preventing further spread of the disease. In this study, we developed a Hidden Markov Model (HMM) approach to surveil the transmission of Lassa fever virus infections in Nigeria. The HMM was developed using the susceptible Infection recovered (SIR) model to formulate the transition matrix and data from past outbreaks of the disease to compute the observations. Our results showed that the dry season as the peak period for Lassa fever and recorded the lowest numbers during the rainy season. The transition matrix showed a 98% chance of transitioning to the infected state from being susceptible and a 96% chance of remaining infected. The stable probability resulted in a 97.9% probability of transitioning to the infected state and a 1.7% chance of transitioning to the susceptible state. The Empirical analysis using the proposed HMM approach does not only provide a valuable tool for public health officials to track and respond to outbreaks of Lassa fever, leading to more effective disease control strategies but also, establishes an efficient structure for other infectious diseases modeling to aid in early detection and response to epidemic outbreaks.