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Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic
SIMPLE SUMMARY: Using tools from the reduced order modeling of parametric ODEs and PDEs, including a new positivity-preserving greedy reduced basis method, we present a novel forecasting method for predicting the propagation of an epidemic. The method takes a collection of highly detailed compartmen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823858/ https://www.ncbi.nlm.nih.gov/pubmed/33396488 http://dx.doi.org/10.3390/biology10010022 |
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author | Bakhta, Athmane Boiveau, Thomas Maday, Yvon Mula, Olga |
author_facet | Bakhta, Athmane Boiveau, Thomas Maday, Yvon Mula, Olga |
author_sort | Bakhta, Athmane |
collection | PubMed |
description | SIMPLE SUMMARY: Using tools from the reduced order modeling of parametric ODEs and PDEs, including a new positivity-preserving greedy reduced basis method, we present a novel forecasting method for predicting the propagation of an epidemic. The method takes a collection of highly detailed compartmental models (with different initial conditions, initial times, epidemiological parameters and numerous compartments) and learns a model with few compartments which best fits the available health data and which is used to provide the forecasts. We illustrate the promising potential of the approach to the spread of the current COVID-19 pandemic in the case of the Paris region during the period from March to November 2020, in which two epidemic waves took place. ABSTRACT: We propose a forecasting method for predicting epidemiological health series on a two-week horizon at regional and interregional resolution. The approach is based on the model order reduction of parametric compartmental models and is designed to accommodate small amounts of sanitary data. The efficiency of the method is shown in the case of the prediction of the number of infected people and people removed from the collected data, either due to death or recovery, during the two pandemic waves of COVID-19 in France, which took place approximately between February and November 2020. Numerical results illustrate the promising potential of the approach. |
format | Online Article Text |
id | pubmed-7823858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78238582021-01-24 Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic Bakhta, Athmane Boiveau, Thomas Maday, Yvon Mula, Olga Biology (Basel) Article SIMPLE SUMMARY: Using tools from the reduced order modeling of parametric ODEs and PDEs, including a new positivity-preserving greedy reduced basis method, we present a novel forecasting method for predicting the propagation of an epidemic. The method takes a collection of highly detailed compartmental models (with different initial conditions, initial times, epidemiological parameters and numerous compartments) and learns a model with few compartments which best fits the available health data and which is used to provide the forecasts. We illustrate the promising potential of the approach to the spread of the current COVID-19 pandemic in the case of the Paris region during the period from March to November 2020, in which two epidemic waves took place. ABSTRACT: We propose a forecasting method for predicting epidemiological health series on a two-week horizon at regional and interregional resolution. The approach is based on the model order reduction of parametric compartmental models and is designed to accommodate small amounts of sanitary data. The efficiency of the method is shown in the case of the prediction of the number of infected people and people removed from the collected data, either due to death or recovery, during the two pandemic waves of COVID-19 in France, which took place approximately between February and November 2020. Numerical results illustrate the promising potential of the approach. MDPI 2020-12-31 /pmc/articles/PMC7823858/ /pubmed/33396488 http://dx.doi.org/10.3390/biology10010022 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bakhta, Athmane Boiveau, Thomas Maday, Yvon Mula, Olga Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title | Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title_full | Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title_fullStr | Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title_full_unstemmed | Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title_short | Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic |
title_sort | epidemiological forecasting with model reduction of compartmental models. application to the covid-19 pandemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823858/ https://www.ncbi.nlm.nih.gov/pubmed/33396488 http://dx.doi.org/10.3390/biology10010022 |
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