About: Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries
Creator	Muhammad, Javed Ikram, Aamer Ahmad, Ghufran Ahmed, Furqan Fatima, Hira Rizwan, Suhail Zeeb, Hajo
source	MedRxiv
abstract	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 as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.
has issue date	2020-08-04(xsd:dateTime)
bibo:doi	10.1101/2020.08.03.20167189
has license	medrxiv
sha1sum (hex)	18e298af3165dc6ac6211c391fd5610cc22bcc5d
schema:url	https://doi.org/10.1101/2020.08.03.20167189
resource representing a document's title	Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries
resource representing a document's body	covid:18e298af3165dc6ac6211c391fd5610cc22bcc5d#body_text
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