African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

中文翻译：

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基于随机森林算法和气象数据的全球非洲猪瘟疫情预测。

非洲猪瘟（ASF）是猪的一种强力传染病。由于目前没有有效的疫苗和治疗方法，一旦爆发，它将对养猪业构成巨大威胁。在本文中，我们使用了ASF爆发数据和WorldClim数据库气象数据，并选择CfsSubset Evaluator-Best First特征选择方法与随机森林算法相结合，构建了非洲猪瘟爆发预测模型。随后，我们还建立了除建模以外的数据测试集，该模型在独立测试集上的准确度精度值范围为76.02％-84.64％，表明建模效果更好，预测精度高于以前的估计。此外，对用于建模的12个特征进行了logistic回归分析，并绘制了ROC曲线。结果表明，bio14特征（最干旱月份的降水）对ASF爆发的影响最大，并且推测该流行病的爆发与降水显着相关。最后，我们使用这种定性预测模型为ASF疫情建立了一个全球在线预测系统，希望这项研究将对决策者有所帮助，他们随后可以采取相关的预防和控制措施，以防止未来的进一步扩散该病的流行。并且推测该流行病的爆发与降水显着相关。最后，我们使用这种定性预测模型为ASF疫情建立了一个全球在线预测系统，希望这项研究将对决策者有所帮助，他们随后可以采取相关的预防和控制措施，以防止未来的进一步扩散该病的流行。并且推测该流行病的爆发与降水显着相关。最后，我们使用这种定性预测模型为ASF疫情建立了一个全球在线预测系统，希望这项研究将对决策者有所帮助，他们随后可以采取相关的预防和控制措施，以防止未来的进一步扩散该病的流行。