What type of study is this?

This is a Quantitative Study study.

September 19, 2025Open Access

Statistical, machine learning, and deep learning models for COVID-19 forecasting in Kenya

JKJoyce KiarieStrathmore University SMSamuel MwaliliStrathmore University RMRachel Waema MbogoStrathmore University

Key Points

Random forest achieved the lowest forecasting errors and highest R² metrics, indicating superior predictive accuracy.
Comparative evaluation across six models employed error metrics like root mean squared error and R².
The study utilized data from April 2020 to August 2021 for testing model performance on COVID-19 cases and deaths.
Significant differences in forecasting errors were confirmed by the Diebold-Mariano test, with random forest as the leading model.

Abstract

Abstract This study aims to enhance coronavirus disease 2019 forecasting in Kenya by comparing the predictive performance of statistical, machine learning, and deep learning (DL) models for total cases, critical cases, severe cases, and total deaths, using data from April 2020 to August 2021. Six models – autoregressive integrated moving average (ARIMA), support vector regression, random forest (RF), recurrent neural network, long short-term memory, and gated recurrent unit – were evaluated with an 80–20 train-test split, employing root mean squared error, mean absolute error, mean absolute percentage error, and R 2 R^2 metrics. The Diebold-Mariano (DM) test assessed statistical significance of error differences. Results reveal RF as the top performer, consistently achieving the lowest errors and highest R 2 R^2 across all datasets, indicating superior accuracy in capturing nonlinear epidemic patterns. GRU outperformed other DL models, while ARIMA showed the weakest performance. The DM test confirmed significant differences in forecasting errors, with RF generally outperforming other models.

AI에게 질문

Bookmark

View Full Paper