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

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.

SUBMITTER: Oliveira JF

PROVIDER: S-EPMC7803757 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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

Oliveira Juliane F JF Jorge Daniel C P DCP Veiga Rafael V RV Rodrigues Moreno S MS Torquato Matheus F MF da Silva Nivea B NB Fiaccone Rosemeire L RL Cardim Luciana L LL Pereira Felipe A C FAC de Castro Caio P CP Paiva Aureliano S S ASS Amad Alan A S AAS Lima Ernesto A B F EABF Souza Diego S DS Pinho Suani T R STR Ramos Pablo Ivan P PIP Andrade Roberto F S RFS

Nature communications 20210112 1

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 ...[more]

PMID: 33436608

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Project description:With the recent licensure of mRNA vaccines against COVID-19 in the 5- to 11-year-old age group, the public health impact of a childhood immunization campaign is of interest. Using a mathematical epidemiological model, we project that childhood vaccination carries minimal risk and yields modest public health benefits. These include large relative reductions in child morbidity and mortality, although the absolute reduction is small because these events are rare. Furthermore, the model predicts "altruistic" absolute reductions in adult cases, hospitalizations, and mortality. However, vaccinating children to benefit adults should be considered from an ethical as well as a public health perspective. From a global health perspective, an additional ethical consideration is the justice of giving priority to children in high-income settings at low risk of severe disease while vaccines have not been made available to vulnerable adults in low-income settings. IMPORTANCE Countries have recently begun implementation of childhood vaccination against SARS-CoV-2 with the Pfizer/BioNTech mRNA vaccine in children 5 to 11 years of age. Because SARS-CoV-2 disease severity is remarkably age dependent, vaccinating children may have modest public health benefits, relative to the unequivocal benefit of vaccinating vulnerable older adults. Furthermore, vaccinating children to "altruistically" increase herd immunity should be considered from an ethical as well as a public health perspective. An additional question is related to global social justice: should priority be given to vaccinating children in high-income settings while older adult populations in low-resource settings have limited access to vaccine? To address the risks and benefits of childhood vaccination, we provide a balanced commentary, supported by a mathematical epidemiological model, using Australia and Alberta, Canada, as case studies. We give highlights of the modeling findings in the commentary and include details in the supplemental materials for interested readers.

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Project description:34,354,966 active cases and 460,787 deaths because of COVID-19 pandemic were recorded on November 06, 2021, in India. To end this ongoing global COVID-19 pandemic, there is an urgent need to implement multiple population-wide policies like social distancing, testing more people and contact tracing. To predict the course of the pandemic and come up with a strategy to control it effectively, a compartmental model has been established. The following six stages of infection are taken into consideration: susceptible (S), asymptomatic infected (A), clinically ill or symptomatic infected (I), quarantine (Q), isolation (J) and recovered (R), collectively termed as SAIQJR. The qualitative behavior of the model and the stability of biologically realistic equilibrium points are investigated in terms of the basic reproduction number. We performed sensitivity analysis with respect to the basic reproduction number and obtained that the disease transmission rate has an impact in mitigating the spread of diseases. Moreover, considering the non-pharmaceutical and pharmaceutical intervention strategies as control functions, an optimal control problem is implemented to mitigate the disease fatality. To reduce the infected individuals and to minimize the cost of the controls, an objective functional has been constructed and solved with the aid of Pontryagin's maximum principle. The implementation of optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time period. Extensive numerical simulations show that the implementation of intervention strategy has an impact in controlling the transmission dynamics of COVID-19 epidemic. Further, our numerical solutions exhibit that the combination of three controls are more influential when compared with the combination of two controls as well as single control. Therefore, the implementation of all the three control strategies may help to mitigate novel coronavirus disease transmission at this present epidemic scenario.Supplementary informationThe online version supplementary material available at 10.1007/s11071-022-07235-7.

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

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

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