Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Stochastic epidemiological models"'
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 2, Pp 4128-4152 (2023)
This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's
Externí odkaz:
https://doaj.org/article/682c354db65b4354849ae1b5c57e9033
Autor:
M. Lau, Z. G. Arenas
Publikováno v:
Trends in Computational and Applied Mathematics, Vol 24, Iss 3 (2023)
Development of mathematical models and its numerical implementations are essential tools in epidemiological modeling. Susceptible-Infected-Recovered (SIR) compartmental model, proposed by Kermack and McKendrick, in 1927, is a widely used deterministi
Externí odkaz:
https://doaj.org/article/4b62af795e2b46919bcdb285dc95e4cd
Autor:
David J. Warne, Anthony Ebert, Christopher Drovandi, Wenbiao Hu, Antonietta Mira, Kerrie Mengersen
Publikováno v:
BMC Public Health, Vol 20, Iss 1, Pp 1-14 (2020)
Abstract Background The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of reported COVI
Externí odkaz:
https://doaj.org/article/30d7ed67ca23455c8b10c3f2a31ddb45
This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::511ed812062458f548a9eccbe27b433d
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473443
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473443
Akademický článek
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Autor:
Marin, Robin
Computer simulations play a vital role in the modeling of infectious diseases. Different modeling regimes fit specific purposes, from ordinary differential equations to probabilistic formulations. Throughout the COVID-19 pandemic, we have seen how th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::166371475a4344b0b8b880d0eb632272
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473445
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473445
Autor:
Christopher C. Drovandi, Antonietta Mira, Kerrie Mengersen, Wenbiao Hu, Anthony Ebert, David J. Warne
Publikováno v:
BMC Public Health
BMC Public Health, Vol 20, Iss 1, Pp 1-14 (2020)
BMC Public Health, Vol 20, Iss 1, Pp 1-14 (2020)
BackgroundThe global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of trajectories of reporte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b1d07ce8d0d9cad3933673bf6584b03
https://doi.org/10.1186/s12889-020-09972-z
https://doi.org/10.1186/s12889-020-09972-z
Akademický článek
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Autor:
Shatskikh, Katherine
Publikováno v:
Shatskikh, Katherine. (2017). Epidemic Detection in Two Populations. 0035: Statistics and Applied Probability. Retrieved from: http://www.escholarship.org/uc/item/4x1556mb
Traditional epidemic detection algorithms make decisions using only local information. We propose a novel approach that explicitly models spatial information fusion from several meta-populations. Our method also takes into account cost-benefit consid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::371e88b39cc958df6bcd4962f5608dfa
http://www.escholarship.org/uc/item/4x1556mb
http://www.escholarship.org/uc/item/4x1556mb