ACIS 2024 Proceedings
Abstract
While machine learning (ML) has been used in the fight against COVID-19, there is limited research using ML to make predictions and identify essential variables in a developing country in East Africa. With COVID-19 widening the gap between developing and developed countries, it is necessary to reduce this disparity, which is also noted as a significant UN Sustainable Development Goal. This paper aims to use ML models to compare the spread of COVID-19 in Uganda. The daily and cumulative number of COVID-19 cases are modelled using two machine learning models: linear regression and the cat boost regressor model. The cat-boost model was the best-performing model with auspicious results. The significant variables in the cat-boost model were then determined using SHAP values, which showed the number of government hospitals, GDP per capita, and temperature significantly impacted COVID-19 cases in Uganda.
Recommended Citation
Sebiranda, Stuart; Busch, Peter; and Smith, Stephen, "Developing a predictive model for COVID-19 in Uganda" (2024). ACIS 2024 Proceedings. 90.
https://aisel.aisnet.org/acis2024/90