Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.
Juliane Fonseca de OliveiraDaniel Cardoso Pereira JorgeRafael V VeigaMoreno Magalhães de Souza RodriguesMatheus F TorquatoNivea B da SilvaRosemeire L FiacconeLuciana L CardimFelipe A C PereiraCaio P de CastroAureliano S S PaivaAlan A S AmadErnesto A B F LimaDiego S SouzaSuani T R PinhoPablo Ivan Pereira RamosRoberto Fernandes Silva AndradePublished in: Nature communications (2021)
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.
Keyphrases
- coronavirus disease
- sars cov
- healthcare
- public health
- respiratory syndrome coronavirus
- mental health
- physical activity
- intensive care unit
- type diabetes
- health information
- cardiovascular events
- machine learning
- electronic health record
- quality improvement
- mechanical ventilation
- coronary artery disease
- acute respiratory distress syndrome
- health insurance
- affordable care act