Background The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. Methodology This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average ( ARIMA ), exponential smoothing model ( ETS ), and random walk forecasts ( RWF ) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error ( MAPE ) and Mean Absolute Error ( MAE ), respectively. Findings The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS , can help in anticipating the future outbreaks of SARS-CoV2.
【초록키워드】 SARS-CoV2, pandemic, risk, global pandemic, Accuracy, outbreak, International, WHO, public health emergency, error, Guidance, ARIMA, COVID-19 case, average, help, finding, cumulative, random, country, data-driven, the epidemic, heat map, evaluate, spread to, generate, less, increase in, MAE, percentage, 【제목키워드】 SARS-CoV2, cumulative, data-driven, Evaluating,