<div class="csl-bib-body">
<div class="csl-entry">Colaneri, K., Damian, C., & Frey, R. (2022). <i>Invisible Infections: A Partial Information Approach for Estimating the Transmission Dynamics of the Covid-19 Pandemic</i>. arXiv. https://doi.org/10.48550/arXiv.2212.13443</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/158273
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dc.description.abstract
In this paper, we develop a discrete time stochastic model under partial information to explain the evolution of Covid-19 pandemic. Our model is a modification of the well-known SIR model for epidemics, which accounts for some peculiar features of Covid-19. In particular, we work with a random transmission rate and we assume that the true number of infectious people at any observation time is random and not directly observable, to account for asymptomatic and non-tested people. We elaborate a nested particle filtering approach to estimate the reproduction rate and the model parameters. We apply our methodology to Austrian Covid-19 infection data in the period from May 2020 to June 2022. Finally, we discuss forecasts and model tests.
en
dc.language.iso
en
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dc.subject
Stochastic SIR model
en
dc.subject
Covid-19
en
dc.subject
Nested particle filter
en
dc.title
Invisible Infections: A Partial Information Approach for Estimating the Transmission Dynamics of the Covid-19 Pandemic
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.identifier.arxiv
2212.13443v1
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dc.contributor.affiliation
University of Rome Tor Vergata, Italy
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dc.contributor.affiliation
Vienna University of Economics and Business, Austria
