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Branching Process Modelling of COVID-19 Pandemic Including Immunity and Vaccination

  • Dimitar Atanasov ORCID logo EMAIL logo , Vessela Stoimenova and Nikolay M. Yanev
Published/Copyright: December 3, 2021
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Abstract

We propose modeling COVID-19 infection dynamics using a class of two-type branching processes. These models require only observations on daily statistics to estimate the average number of secondary infections caused by a host and to predict the mean number of the non-observed infected individuals. The development of the epidemic process depends on the reproduction rate as well as on additional facets as immigration, adaptive immunity, and vaccination. Usually, in the existing deterministic and stochastic models, the officially reported and publicly available data are not sufficient for estimating model parameters. An important advantage of the proposed model, in addition to its simplicity, is the possibility of direct computation of its parameters estimates from the daily available data. We illustrate the proposed model and the corresponding data analysis with data from Bulgaria, however they are not limited to Bulgaria and can be applied to other countries subject to data availability.

Funding statement: The research was partially supported by the National Scientific Foundation of Bulgaria at the Ministry of Education and Science, Grant No KP-6-H22/3 and by the financial funds allocated to the Sofia University “St. Kliment Ohridski”, Grant No. 80-10-87/2021.

Acknowledgements

The authors are very grateful to the referee for the careful reading of the paper and for the useful remarks.

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Received: 2021-08-31
Revised: 2021-11-16
Accepted: 2021-11-16
Published Online: 2021-12-03
Published in Print: 2022-01-01

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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