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Epidemiol Infect ; 148: e155, 2020 07 20.
Article in English | MEDLINE | ID: covidwho-661154

ABSTRACT

In São Paulo, Brazil, the first case of coronavirus disease 2019 (CoViD-19) was confirmed on 26 February, the first death due to CoViD-19 was registered on 16 March, and on 24 March, São Paulo implemented the isolation of persons in non-essential activities. A mathematical model was formulated based on non-linear ordinary differential equations considering young (60 years old or less) and elder (60 years old or more) subpopulations, aiming to describe the introduction and dissemination of the new coronavirus in São Paulo. This deterministic model used the data collected from São Paulo to estimate the model parameters, obtaining R0 = 6.8 for the basic reproduction number. The model also allowed to estimate that 50% of the population of São Paulo was in isolation, which permitted to describe the current epidemiological status. The goal of isolation implemented in São Paulo to control the rapid increase of the new coronavirus epidemic was partially succeeded, concluding that if isolation of at least 80% of the population had been implemented, the collapse in the health care system could be avoided. Nevertheless, the isolated persons must be released one day. Based on this model, we studied the potential epidemiological scenarios of release by varying the proportions of the release of young and elder persons. We also evaluated three different strategies of release: All isolated persons are released simultaneously, two and three releases divided in equal proportions. The better scenarios occurred when young persons are released, but maintaining elder persons isolated for a while. When compared with the epidemic without isolation, all strategies of release did not attain the goal of reducing substantially the number of hospitalisations due to severe CoViD-19. Hence, we concluded that the best decision must be postponing the beginning of the release.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Forecasting/methods , Models, Theoretical , Pandemics/prevention & control , Patient Isolation/methods , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Age Factors , Brazil/epidemiology , COVID-19 , Humans , Infectious Disease Transmission, Professional-to-Patient/prevention & control , Middle Aged , Patient Isolation/trends , Public Policy , Software Design
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