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

None Nkemnole E. B., None Oyewole J. O.
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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.
尼日利亚旱季拉沙热流行监测的隐马尔可夫模型分析
拉沙热是一种传染性病毒性疾病,在尼日利亚和其他西非国家流行。早期发现和应对该疾病的暴发对于防止其传播和减少疾病和死亡至关重要。找到一些解释拉沙热传播机制的数学模式,以及彻底了解影响该疾病的生物学因素,对于建立监测系统以防止该疾病进一步传播是必要的。在这项研究中,我们开发了一种隐马尔可夫模型(HMM)方法来监测尼日利亚拉沙热病毒感染的传播。HMM使用易感感染恢复(SIR)模型来制定过渡矩阵和过去疾病爆发的数据来计算观测值。结果表明,旱季为拉沙热发病高峰期,雨季发病人数最少;过渡矩阵显示,从易感状态过渡到感染状态的几率为98%,保持感染状态的几率为96%。稳定概率导致97.9%的概率过渡到感染状态,1.7%的概率过渡到易感状态。使用提议的HMM方法进行的实证分析不仅为公共卫生官员跟踪和应对拉沙热的爆发提供了有价值的工具,从而导致更有效的疾病控制策略,而且还为其他传染病建模建立了有效的结构,以帮助早期发现和应对流行病爆发。
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