Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan

The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to obser...

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Bibliographic Details
Published in:Data in brief Vol. 31; p. 105854
Main Author: Aslam, Muhammad
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2020
Elsevier
Subjects:
Arima model
COVID-19 infection
COVID-2019 pandemic
data collection
demography
Forecast
Holt-winters’ method
Infection control
Kalman filter
Mathematics
Pakistan
pandemic
State space model
SutteARIMA
viruses
Pakistan
Kalman filter
Arima model
Forecast
Holt-winters’ method
State space model
SutteARIMA
COVID-2019 pandemic
Infection control
ISSN:2352-3409, 2352-3409
Online Access:Get full text
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Summary:The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan.
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ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2020.105854