Version 1
: Received: 7 October 2024 / Approved: 10 October 2024 / Online: 10 October 2024 (09:08:42 CEST)
How to cite:
Mulwa, D.; KAZUZURU, B.; Bett, B. Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya. Preprints2024, 2024100752. https://doi.org/10.20944/preprints202410.0752.v1
Mulwa, D.; KAZUZURU, B.; Bett, B. Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya. Preprints 2024, 2024100752. https://doi.org/10.20944/preprints202410.0752.v1
Mulwa, D.; KAZUZURU, B.; Bett, B. Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya. Preprints2024, 2024100752. https://doi.org/10.20944/preprints202410.0752.v1
APA Style
Mulwa, D., KAZUZURU, B., & Bett, B. (2024). Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya. Preprints. https://doi.org/10.20944/preprints202410.0752.v1
Chicago/Turabian Style
Mulwa, D., BENEDICTO KAZUZURU and Benard Bett. 2024 "Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya" Preprints. https://doi.org/10.20944/preprints202410.0752.v1
Abstract
In Kenya, Rift Valley Fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While Machine Learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains under explored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analysing climate data spanning from 1981 to 2010, including machine learning models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, and claypatterns, and RVFcases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, the study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond.
Keywords
machine learning; outbreak; training; XGBoost; Rift Valley fever
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
