Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases.
PLoS One
; 16(5): e0252147, 2021.
Article
in English
| MEDLINE | ID: covidwho-1238775
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 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Models, Statistical
/
Data Science
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pone.0252147
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