Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil
Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil
The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.
Supplementary Information: The online version contains supplementary material available at 10.1186/s13362-020-00098-w.
COVID-19, Coronavirus, Lockdown, Neural network, Quarantine, Seir models
Tarrataca, Luís
4b864905-484c-43d6-b0f4-6e634e49a8d3
Dias, Claudia Mazza
95b06278-5b4f-4b12-aa33-68a096f4a436
Haddad, Diego Barreto
339a6dff-218b-4cd4-bcae-abf3bf2554fa
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
6 January 2021
Tarrataca, Luís
4b864905-484c-43d6-b0f4-6e634e49a8d3
Dias, Claudia Mazza
95b06278-5b4f-4b12-aa33-68a096f4a436
Haddad, Diego Barreto
339a6dff-218b-4cd4-bcae-abf3bf2554fa
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Tarrataca, Luís, Dias, Claudia Mazza, Haddad, Diego Barreto and Arruda, Edilson F.
(2021)
Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil.
Journal of Mathematics in Industry, 11 (1), [2].
(doi:10.1186/s13362-020-00098-w).
Abstract
The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.
Supplementary Information: The online version contains supplementary material available at 10.1186/s13362-020-00098-w.
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2004.06916
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s13362-020-00098-w
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Accepted/In Press date: 26 December 2020
e-pub ahead of print date: 6 January 2021
Published date: 6 January 2021
Keywords:
COVID-19, Coronavirus, Lockdown, Neural network, Quarantine, Seir models
Identifiers
Local EPrints ID: 444559
URI: http://eprints.soton.ac.uk/id/eprint/444559
ISSN: 2190-5983
PURE UUID: d191e126-287b-47e4-b782-e21fcc8d7816
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Date deposited: 26 Oct 2020 17:30
Last modified: 27 Apr 2022 02:17
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Author:
Luís Tarrataca
Author:
Claudia Mazza Dias
Author:
Diego Barreto Haddad
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