Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation-Reference-Cited by-同舟云学术

Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

Published:2023-02-25 Issue:10 Volume:111 Page:9649-9679
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Cunha Jr Americo^ORCID,Barton David A. W.^ORCID,Ritto Thiago G.^ORCID

Funder

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Engineering and Physical Sciences Research Council

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-023-08327-8.pdf

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3. Byambasuren, O., Cardona, M., Bell, K., Clark, J., McLaws, M.L., Glasziou, P.: Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. Off. J. Assoc. Med. Microbiol. Infect. Dis. Can. 5(4), 223–234 (2020). https://doi.org/10.3138/jammi-2020-0030

4. Cai, M., Karniadakis, G.E., Li, C.: Fractional SEIR model and data-driven predictions of COVID-19 dynamics of Omicron variant. Chaos 32, 071101 (2022). https://doi.org/10.1063/5.0099450

5. Cheng, C., et al.: The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients. Infect. Dis. Poverty 10(1), 119 (2021). https://doi.org/10.1186/s40249-021-00901-9

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