Version 1
: Received: 1 July 2024 / Approved: 2 July 2024 / Online: 2 July 2024 (10:34:58 CEST)
How to cite:
Mulwa, D.; Kazuzuru, B.; Misinzo, G.; Bett, B. An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors. Preprints2024, 2024070187. https://doi.org/10.20944/preprints202407.0187.v1
Mulwa, D.; Kazuzuru, B.; Misinzo, G.; Bett, B. An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors. Preprints 2024, 2024070187. https://doi.org/10.20944/preprints202407.0187.v1
Mulwa, D.; Kazuzuru, B.; Misinzo, G.; Bett, B. An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors. Preprints2024, 2024070187. https://doi.org/10.20944/preprints202407.0187.v1
APA Style
Mulwa, D., Kazuzuru, B., Misinzo, G., & Bett, B. (2024). An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors. Preprints. https://doi.org/10.20944/preprints202407.0187.v1
Chicago/Turabian Style
Mulwa, D., Gerald Misinzo and Benard Bett. 2024 "An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors" Preprints. https://doi.org/10.20944/preprints202407.0187.v1
Abstract
In Kenya, reports of Rift Valley fever (RVF), one of the worst climate-sensitive zoonosis, have been common. Despite the fact that several empirical studies have demonstrated that Machine learning techniques perform better than time series models in forecasting time series data, there is little evidence of their use in predicting disease outbreaks in Africa. Recently, the literature has mentioned a number of other uses of machine learning to support intelligent decision-making in the healthcare industry and public health but there is limited knowledge on the use of the XGBoost model in the prediction of disease outbreaks. Among the Kenyan provinces, Rift valley fever cases were more pronounced in Rift valley (26.80%) and Eastern (20.60%) regions. The study explored the relationship be-tween RVF incidence and various climatic factors, such as humidity, clay content, elevation, slope, and rainfall. The strongest correlation, a meager 0.02903 for rainfall, was found in the correlation matrix, which showed weak linear relationships between various climatic factors and RVF cases. These climate variables were used to train the XGBoost model, which showed remark-able performance with an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. Rainfall was found to be the most important predictor in the feature importance analysis. These findings are consistent with other research showing how important weather conditions are to RVF outbreaks. According to the study's findings, the use of sophisticated machine learning models that take a variety of climatic factors into account can greatly improve RVF outbreak prediction and control.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
