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<p>Lassa fever is an acute viral haemorrhagic disease caused by the Lassa virus. Transmission to humans primarily occurs through direct contact with Mastomys rats or via the ingestion of food or usage of household items contaminated with the urine or faeces of infected rats. The MaxEnt algorithm was used to estimate the distribution of Lassa fever based on data on the incidence of the disease, ecogeographic features, and human factors. Principal component analysis (PCA) was used to mitigate multicollinearity among the environmental variables. The model’s accuracy was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The prevalence of Lassa fever is anticipated to be substantially affected by human factors (population density, roads, built-settlement, poverty), climatic variables (Prec11, Tmean01, Bio7, Bio12), and altitude. The model distribution map revealed that Owo, Ose, Akure North, Akure South, Akoko South-West, Akoko South-East, Akoko North-East, Ifedore, Idanre, Ondo, and Akoko North-West are very suitable regions. Our suitability map identifies hotspots, aiding public health officials in resource distribution to mitigate the current Lassa fever epidemic in Ondo State, Nigeria.</p>

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Modelling the effects of climate and human factor on Lassa fever distribution in Ondo State Nigeria

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月11日

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  • Temitope Emmanuel Arotolu,
  • Josephine Olayinka-Olagunju,
  • Adekunle A. Dosumu

摘要

Lassa fever is an acute viral haemorrhagic disease caused by the Lassa virus. Transmission to humans primarily occurs through direct contact with Mastomys rats or via the ingestion of food or usage of household items contaminated with the urine or faeces of infected rats. The MaxEnt algorithm was used to estimate the distribution of Lassa fever based on data on the incidence of the disease, ecogeographic features, and human factors. Principal component analysis (PCA) was used to mitigate multicollinearity among the environmental variables. The model’s accuracy was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The prevalence of Lassa fever is anticipated to be substantially affected by human factors (population density, roads, built-settlement, poverty), climatic variables (Prec11, Tmean01, Bio7, Bio12), and altitude. The model distribution map revealed that Owo, Ose, Akure North, Akure South, Akoko South-West, Akoko South-East, Akoko North-East, Ifedore, Idanre, Ondo, and Akoko North-West are very suitable regions. Our suitability map identifies hotspots, aiding public health officials in resource distribution to mitigate the current Lassa fever epidemic in Ondo State, Nigeria.