Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases.

ABSTRACT:

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 Absolu

SUBMITTER: Ahmad G 

PROVIDER: S-EPMC8139504 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature