Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation
© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law..
This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:111 |
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| Enthalten in: |
Nonlinear dynamics - 111(2023), 10 vom: 30., Seite 9649-9679 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cunha, Americo [VerfasserIn] |
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| Links: |
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| Themen: |
ABC inference |
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| Anmerkungen: |
Date Revised 12.04.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s11071-023-08327-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a COVID-19 modeling | |
| 650 | 4 | |a Cross-entropy method | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Uncertainty quantification | |
| 700 | 1 | |a Barton, David A W |e verfasserin |4 aut | |
| 700 | 1 | |a Ritto, Thiago G |e verfasserin |4 aut | |
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