Environmental data in epidemic forecasting: Insights from predictive analytics


  • Charles Chukwudalu Ebulue Department of Community Medicine and Primary Healthcare, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria
  • Ogochukwu Virginia Ekkeh Independent Researcher, Akwa Ibom, Nigeria
  • Ogochukwu Roseline Ebulue Nigerian Institute for Trypanosomiasis and Onchocerciasis Research (NITRA), Asaba, Nigeria
  • Chukwunonso Sylvester Ekesiobi Department of Economics, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria




Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change.

Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.



How to Cite

Charles Chukwudalu Ebulue, Ogochukwu Virginia Ekkeh, Ogochukwu Roseline Ebulue, & Chukwunonso Sylvester Ekesiobi. (2024). Environmental data in epidemic forecasting: Insights from predictive analytics. Computer Science & IT Research Journal, 5(5), 1113-1125. https://doi.org/10.51594/csitrj.v5i5.1118