Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.

Original languageEnglish
Pages (from-to)2972-3206
Number of pages235
JournalComputational and Structural Biotechnology Journal
Volume18
DOIs
Publication statusPublished - Jan 2020

Keywords

  • COVID-19
  • Coronavirus
  • Epidemic transmission
  • Forecasting models
  • Machine learning models
  • Pandemic
  • Time series models

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries'. Together they form a unique fingerprint.

Cite this