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Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network
Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, to support the decision-making on public policies at a regional level. The tool is suitable for understanding and suggesting levels of intervention needed to contain the spread of infectious diseases...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO).
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806062/ http://dx.doi.org/10.1016/j.ejco.2022.100025 |
_version_ | 1784643365301649408 |
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author | Nonato, Luis Gustavo Peixoto, Pedro Pereira, Tiago Sagastizábal, Claudia Silva, Paulo J.S. |
author_facet | Nonato, Luis Gustavo Peixoto, Pedro Pereira, Tiago Sagastizábal, Claudia Silva, Paulo J.S. |
author_sort | Nonato, Luis Gustavo |
collection | PubMed |
description | Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, to support the decision-making on public policies at a regional level. The tool is suitable for understanding and suggesting levels of intervention needed to contain the spread of infectious diseases when the mobility of inhabitants through a regional network is a concern. Such is the case for the SARS-CoV-2 virus that is highly contagious and, therefore, makes it crucial to incorporate the circulation of people in the epidemiological compartmental models. Robot Dance anticipates the spread of an epidemic in a complex regional network, helping to identify fragile links where applying differentiated measures of containment, testing, and vaccination is important. Based on stochastic optimization, the model determines efficient strategies on the basis of commuting of individuals and the situation of hospitals in each district. Uncertainty in the capacity of intensive care beds is handled by a chance-constraint approach. Some functionalities of Robot Dance are illustrated in the state of São Paulo in Brazil, using real data for a region with more than forty million inhabitants. |
format | Online Article Text |
id | pubmed-8806062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). |
record_format | MEDLINE/PubMed |
spelling | pubmed-88060622022-02-02 Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network Nonato, Luis Gustavo Peixoto, Pedro Pereira, Tiago Sagastizábal, Claudia Silva, Paulo J.S. EURO Journal on Computational Optimization Article Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, to support the decision-making on public policies at a regional level. The tool is suitable for understanding and suggesting levels of intervention needed to contain the spread of infectious diseases when the mobility of inhabitants through a regional network is a concern. Such is the case for the SARS-CoV-2 virus that is highly contagious and, therefore, makes it crucial to incorporate the circulation of people in the epidemiological compartmental models. Robot Dance anticipates the spread of an epidemic in a complex regional network, helping to identify fragile links where applying differentiated measures of containment, testing, and vaccination is important. Based on stochastic optimization, the model determines efficient strategies on the basis of commuting of individuals and the situation of hospitals in each district. Uncertainty in the capacity of intensive care beds is handled by a chance-constraint approach. Some functionalities of Robot Dance are illustrated in the state of São Paulo in Brazil, using real data for a region with more than forty million inhabitants. The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). 2022 2022-02-01 /pmc/articles/PMC8806062/ http://dx.doi.org/10.1016/j.ejco.2022.100025 Text en © 2022 The Author(s) 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 | Article Nonato, Luis Gustavo Peixoto, Pedro Pereira, Tiago Sagastizábal, Claudia Silva, Paulo J.S. Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title | Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title_full | Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title_fullStr | Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title_full_unstemmed | Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title_short | Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network |
title_sort | robot dance: a mathematical optimization platform for intervention against covid-19 in a complex network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806062/ http://dx.doi.org/10.1016/j.ejco.2022.100025 |
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