A Multi-Step Predictive Model for COVID-19 Cases in Nigeria Using Machine Learning

COronaVIrus Disease 2019 (COVID-19) is a vastly communicable disease that has the whole world in a panic state with a huge negative impact. Forecasting for future occurrences for timely intervention requires a good model. The aim of this chapter is to model a multistep forecast for 14-days (2 weeks) incidence of COVID-19 daily cases in Nigeria with Machine Learning (ML) models. The study dataset contains 241 instances (days) of the daily incidence of COVID-19 for confirmed, recovered, and patients who eventually died from February 27th to October 24th, 2020 in Nigeria. The proposed methodology jointly model multiple targets field (cases) simultaneously in order to capture dependencies between them. This 3-daily time-series study dataset was transformed to a supervised learning format for analysis by ML models. Linear Regression (LR), MultiLayerPerceptron Regressor (MLPR), Support Vector Regressor (SVR), k-Nearest Neighbor Regressor (k-NNR), and Random Forest Regressor (RFR) models were employed and their prediction errors were compared based on average values of Directional Accuracy (DAC), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The result obtained showed that SVR outperformed all other models and can accurately forecast a 14-day occurrence of COVID-19 cases in Nigeria based on all the metrics used in this study. This study will aid the Government and health practitioners to plan ahead for the next 14 days (2 weeks). This 14-ahead step forecasting model could also be extended for longer terms. ML model-based forecasting can effectively model a small sample size and is useful for decision-making and planning for future pandemics by the government and health service workers.