Abstract:
The COVID-19 outbreak from China has infected more than 573.8 million people all
over the world including more than 6.3 million deaths. In Sri Lanka, there are more than
670,000 cases including 16,770 deaths up to now. Many studies have been conducted
on this global epidemic, with a focus on analyzing the spatial and temporal spread of
the virus, which is crucial for prevention efforts. This study aims to identify the optimal
machine learning model to predict the spatio-temporal spread of COVID-19 in the
Kandy district of Sri Lanka. The dataset, collected from the Regional Director of Health
Services, Kandy, spans the period from 06-01-2021 to 18-10-2021 and includes
confirmed cases reported through Polymerase Chain Reaction (PCR) and Rapid
Antigen Tests (RAT) across 23 Ministry of Health (MOH) areas. Four machine learning
models Logistic Regression, Linear Regression, Decision Tree, and Random Forest
were used to predict the spread of COVID-19 in the region. The models were evaluated
using R2 score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean
Squared Error (MSE), and Accuracy. Results indicate that the Random Forest model
outperformed the others, with the highest R2 score (0.571852) and lowest error rates,
making it the most suitable model for predicting the COVID-19 spread. Logistic
Regression and Linear Regression exhibited poor performance with negative R2 scores,
while the Decision Tree model demonstrated moderate predictive capability.
Additionally, ArcGIS software was used to visualize the spatial distribution of COVID
19 cases in the Kandy district, highlighting the Kandy Municipal Council area as the
most at-risk and Udadumbara as the least. These visualizations facilitate a better
understanding of the spread patterns. In conclusion, the Random Forest model proved
to be the most effective for spatio-temporal COVID-19 prediction, which can be applied
to future epidemic predictions. The study emphasizes the importance of high-quality,
centralized databases for enhancing prediction accuracy. Future work includes the
development of a live database system for real-time virus spread predictions in Sri
Lanka and the use of geographic information systems (GIS) for comprehensive visual
mapping to aid in timely intervention strategies.
