Forecasting daily new infections, deaths and recovery cases due to COVID-19 in Pakistan by using Bayesian Dynamic Linear Models

Firdos Khan, Shaukat Ali, Alia Saeed, Ramesh Kumar and Abdul Wali Khan

PLOS ONE, 2021, vol. 16, issue 6, 1-14

Abstract: The COVID-19 has caused the deadliest pandemic around the globe, emerged from the city of Wuhan, China by the end of 2019 and affected all continents of the world, with severe health implications and as well as financial-damage. Pakistan is also amongst the top badly effected countries in terms of casualties and financial loss due to COVID-19. By 20th March, 2021, Pakistan reported 623,135 total confirmed cases and 13,799 deaths. A state space model called ‘Bayesian Dynamic Linear Model’ (BDLM) was used for the forecast of daily new infections, deaths and recover cases regarding COVID-19. For the estimation of states of the models and forecasting new observations, the recursive Kalman filter was used. Twenty days ahead forecast show that the maximum number of new infections are 4,031 per day with 95% prediction interval (3,319–4,743). Death forecast shows that the maximum number of the deaths with 95% prediction interval are 81 and (67–93), respectively. Maximum daily recoveries are 3,464 with 95% prediction interval (2,887–5,423) in the next 20 days. The average number of new infections, deaths and recover cases are 3,282, 52 and 1,840, respectively, in the upcoming 20 days. As the data generation processes based on the latest data has been identified, therefore it can be updated with the availability of new data to provide latest forecast.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0253367

DOI: 10.1371/journal.pone.0253367

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