Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.

Abstract

COVID‐19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID‐19 was emerged due to the SARS‐CoV‐2 that is highly infectious pandemic. Every country tried to control the COVID‐19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time‐series and machine learning models, named as random forests, K‐nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID‐19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top‐ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out‐of‐sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID‐19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID‐19.