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Covid-19 confirmed cases and deaths prediction in Bangladesh using hidden Markov model

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Date

1/1/2021

Publisher

Institute of Electrical and Electronics Engineers Inc.

Authors

Full item page

URI

https://hdl.handle.net/10361/28554

Citation

D. F. Dofadar, R. H. Khan and M. G. Rabiul Alam, "COVID-19 Confirmed Cases and Deaths Prediction in Bangladesh Using Hidden Markov Model," 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris / Créteil, France, 2021, pp. 1-4, doi: 10.1109/BioSMART54244.2021.9677841.

Abstract

The number of people affected by Coronavirus is quite concerning in Bangladesh. It has become a necessity to forecast the future cases since it involves ensuring adequate resources to help people and imposing strict guidelines to deal with this epidemic. This research is about predicting upcoming COVID-19 confirmed cases and deaths from a time series dataset using Hidden Markov Model. The optimal number of hidden states were determined using AIC and BIC. The proposed models are implemented to forecast the daily confirmed cases and daily deaths of Bangladesh for next 90 days.

Keywords

Covid-19, Hidden markov models, Markov processes, Maximum likelihood estimation, Time series data

LC Subject Headings

COVID-19 (Disease)--Bangladesh., Forecasting., Markov processes.

Description

Publisher Link

https://ieeexplore.ieee.org/document/9677841

DOI

10.1109/BioSMART54244.2021.9677841

Department

Department of Computer Science and Engineering

Type

Conference Proceedings

Collections

Scholarly Indexed Publications