 Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in PakistanFirdos Khan, Alia Saeed and Shaukat Ali Chaos, Solitons & Fractals, 2020, vol. 140, issue C Abstract: COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013–8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112–233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182–6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future. Keywords: Forecasting; COVID-19; Confirmed cases; Recoveries; Death; Vector Autoregressive model, Time series (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305853 DOI: 10.1016/j.chaos.2020.110189 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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