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 F. Torquato, Nivea B. Silva, Rosemeire L. Fiaccone, Luciana L. Cardim, Felipe A. C. Pereira, Caio P. Castro, Aureliano S. S. Paiva, Alan A. S. Amad, Ernesto A. B. F. Lima, Diego S. Souza, Suani T. R. Pinho, Pablo Ivan P. Ramos and Roberto F. S. Andrade
Additional contact information
Juliane F. Oliveira: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Daniel C. P. Jorge: Universidade Federal da Bahia
Rafael V. Veiga: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Moreno S. Rodrigues: Fundação Oswaldo Cruz, Porto Velho
Matheus F. Torquato: Swansea University
Nivea B. Silva: Universidade Federal da Bahia
Rosemeire L. Fiaccone: Universidade Federal da Bahia
Luciana L. Cardim: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Felipe A. C. Pereira: Universidade de São Paulo
Caio P. Castro: Universidade Federal da Bahia
Aureliano S. S. Paiva: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Alan A. S. Amad: Swansea University
Ernesto A. B. F. Lima: The University of Texas at Austin
Diego S. Souza: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Suani T. R. Pinho: Universidade Federal da Bahia
Pablo Ivan P. Ramos: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
Roberto F. S. Andrade: Instituto Gonçalo Moniz, Fundação Oswaldo Cruz

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: 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, 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.

Date: 2021
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DOI: 10.1038/s41467-020-19798-3

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