Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Oliveira, Juliane F.; P., Daniel C.; Veiga, Rafael V.; Rodrigues, Moreno S.; Torquato, Matheus; da, Nivea B.; Fiaccone, Rosemeire L.; Cardim, Luciana L.; C., Felipe A.; de, Caio P.; S., Aureliano S.; Amad, Alan; B., Ernesto A.; Souza, Diego S.; R., Suani T.; P., Pablo Ivan; S., Roberto F.

doi:10.1038/s41467-020-19798-3

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Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Juliane F. Oliveira, Daniel C. P. Jorge, Rafael V. Veiga, Moreno S. Rodrigues, Matheus Torquato

, Nivea B. da Silva, Rosemeire L. Fiaccone, Luciana L. Cardim, Felipe A. C. Pereira, Caio P. de Castro, Aureliano S. S. Paiva, Alan Amad

, Ernesto A. B. F. Lima, Diego S. Souza, Suani T. R. Pinho, Pablo Ivan P. Ramos, Roberto F. S. Andrade

Nature Communications, Volume: 12, Issue: 1

Swansea University Authors: Matheus Torquato , Alan Amad

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DOI (Published version): 10.1038/s41467-020-19798-3

Abstract

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here,...

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Published in:	Nature Communications
ISSN:	2041-1723
Published:	Springer Science and Business Media LLC 2021
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URI:	https://cronfa.swan.ac.uk/Record/cronfa56082
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spelling	2022-07-05T16:34:53.9381331 v2 56082 2021-01-20 Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil 7a053c668886b4642286baed36fdba90 0000-0001-6356-3538 Matheus Torquato Matheus Torquato true false fe2123481afa7460a369317354cba4ec 0000-0001-7709-5536 Alan Amad Alan Amad true false 2021-01-20 SCS COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses. Journal Article Nature Communications 12 1 Springer Science and Business Media LLC 2041-1723 12 1 2021 2021-01-12 10.1038/s41467-020-19798-3 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001. STRP was supported by an International Cooperation grant (process number INT0002/2016) from Bahia Research Foundation (FAPESB). STRP and RFSA were supported by the National Institute of Science and Technology—Complex Systems from CNPq, Brazil. JFO was supported by the Center of Data and Knowledge Integration for Health (CIDACS) through the Zika Platform—a long-term surveillance platform for Zika virus and microcephaly (Unified Health System (SUS), Brazilian Ministry of Health). AASA gratefully acknowledges the financial support received from the Engineering and Physical Sciences Research Council (EPSRC) in the form of grant EP/R002134/1. The authors acknowledge the helpful suggestions from members of the CoVida Network (http://www.redecovida.org), in special to contributors to the Rede CoVida Modelling Task-force (See Supplementary Note 1). Andris K. Walter is gratefully acknowledged for English language revision and manuscript copy-editing assistance. 2022-07-05T16:34:53.9381331 2021-01-20T09:21:41.3422175 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Juliane F. Oliveira 1 Daniel C. P. Jorge 2 Rafael V. Veiga 3 Moreno S. Rodrigues 4 Matheus Torquato 0000-0001-6356-3538 5 Nivea B. da Silva 6 Rosemeire L. Fiaccone 7 Luciana L. Cardim 8 Felipe A. C. Pereira 9 Caio P. de Castro 10 Aureliano S. S. Paiva 11 Alan Amad 0000-0001-7709-5536 12 Ernesto A. B. F. Lima 13 Diego S. Souza 14 Suani T. R. Pinho 15 Pablo Ivan P. Ramos 16 Roberto F. S. Andrade 17 56082__19129__1bbbaba475514efd9049755f1ea1598d.pdf 56082.pdf 2021-01-20T09:23:37.6478532 Output 1877738 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
spellingShingle	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil Matheus Torquato Alan Amad
title_short	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
title_full	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
title_fullStr	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
title_full_unstemmed	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
title_sort	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
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author_id_fullname_str_mv	7a053c668886b4642286baed36fdba90_*_Matheus Torquato fe2123481afa7460a369317354cba4ec_*_Alan Amad
author	Matheus Torquato Alan Amad
author2	Juliane F. Oliveira Daniel C. P. Jorge Rafael V. Veiga Moreno S. Rodrigues Matheus Torquato Nivea B. da Silva Rosemeire L. Fiaccone Luciana L. Cardim Felipe A. C. Pereira Caio P. de Castro Aureliano S. S. Paiva Alan Amad Ernesto A. B. F. Lima Diego S. Souza Suani T. R. Pinho Pablo Ivan P. Ramos Roberto F. S. Andrade
format	Journal article
container_title	Nature Communications
container_volume	12
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description	COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.
published_date	2021-01-12T04:10:46Z
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Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

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