Cargando…
Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19
The main aim of the present paper is threefold. First, it aims at presenting an extended contact-based model for the description of the spread of contagious diseases in complex networks with consideration of asymptomatic evolutions. Second, it presents a parametrization method of the considered mode...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552782/ https://www.ncbi.nlm.nih.gov/pubmed/36249287 http://dx.doi.org/10.1016/j.chaos.2022.111887 |
_version_ | 1784806324195819520 |
---|---|
author | Schaum, A. Bernal-Jaquez, R. Alarcon Ramos, L. |
author_facet | Schaum, A. Bernal-Jaquez, R. Alarcon Ramos, L. |
author_sort | Schaum, A. |
collection | PubMed |
description | The main aim of the present paper is threefold. First, it aims at presenting an extended contact-based model for the description of the spread of contagious diseases in complex networks with consideration of asymptomatic evolutions. Second, it presents a parametrization method of the considered model, including validation with data from the actual spread of COVID-19 in Germany, Mexico and the United States of America. Third, it aims at showcasing the fruitful combination of contact-based network spreading models with a modern state estimation and filtering technique to (i) enable real-time monitoring schemes, and (ii) efficiently deal with dimensionality and stochastic uncertainties. The network model is based on an interpretation of the states of the nodes as (statistical) probability densities samples, where nodes can represent individuals, groups or communities, cities or countries, enabling a wide field of application of the presented approach. |
format | Online Article Text |
id | pubmed-9552782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95527822022-10-11 Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 Schaum, A. Bernal-Jaquez, R. Alarcon Ramos, L. Chaos Solitons Fractals Frontiers The main aim of the present paper is threefold. First, it aims at presenting an extended contact-based model for the description of the spread of contagious diseases in complex networks with consideration of asymptomatic evolutions. Second, it presents a parametrization method of the considered model, including validation with data from the actual spread of COVID-19 in Germany, Mexico and the United States of America. Third, it aims at showcasing the fruitful combination of contact-based network spreading models with a modern state estimation and filtering technique to (i) enable real-time monitoring schemes, and (ii) efficiently deal with dimensionality and stochastic uncertainties. The network model is based on an interpretation of the states of the nodes as (statistical) probability densities samples, where nodes can represent individuals, groups or communities, cities or countries, enabling a wide field of application of the presented approach. Elsevier Ltd. 2022-04 2022-03-11 /pmc/articles/PMC9552782/ /pubmed/36249287 http://dx.doi.org/10.1016/j.chaos.2022.111887 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Frontiers Schaum, A. Bernal-Jaquez, R. Alarcon Ramos, L. Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title | Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title_full | Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title_fullStr | Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title_full_unstemmed | Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title_short | Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19 |
title_sort | data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: application to covid-19 |
topic | Frontiers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552782/ https://www.ncbi.nlm.nih.gov/pubmed/36249287 http://dx.doi.org/10.1016/j.chaos.2022.111887 |
work_keys_str_mv | AT schauma dataassimilationandstateestimationforcontactbasedspreadingprocessesusingtheensemblekalmanfilterapplicationtocovid19 AT bernaljaquezr dataassimilationandstateestimationforcontactbasedspreadingprocessesusingtheensemblekalmanfilterapplicationtocovid19 AT alarconramosl dataassimilationandstateestimationforcontactbasedspreadingprocessesusingtheensemblekalmanfilterapplicationtocovid19 |