Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies.

Askery Canabarro,Elayne Tenório,Rafael Chaves,Samuraí Brito,Laís Martins,Renato Martins,Lidia Adriana Braunstein

doi:10.1371/journal.pone.0236310

Askery Canabarro, Elayne Tenório + Show 5 more

Open Access

https://doi.org/10.1371/journal.pone.0236310

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Abstract

In this work we propose a data-driven age-structured census-based SIRD-like epidemiological model capable of forecasting the spread of COVID-19 in Brazil. We model the current scenario of closed schools and universities, social distancing of people above sixty years old and voluntary home quarantine to show that it is still not enough to protect the health system by explicitly computing the demand for hospital intensive care units. We also show that an urgent intense quarantine might be the only solution to avoid the collapse of the health system and, consequently, to minimize the quantity of deaths. On the other hand, we demonstrate that the relaxation of the already imposed control measures in the next days would be catastrophic.

Highlights

Coronavirus disease-2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (Sars-CoV-2) is a main threat for the public health systems throughout the globe [1,2,3,4,5,6,7,8,9,10,11,12,13]
By modeling the current Brazilian scenario, we investigate the effects of applying one of the following non-phamaceutical interventions (NPIs) policies: 0) a complete absence of control measures (No NPI); 1) closure of schools and universities (CSU); 2) Social distancing of those over sixty years old (SD60+); 3) voluntary home quarantine (VHQ) and social distance of the entire population as an intense quarantine (IQ)
Our main result is to show that, even though the current NPI measure taken in Brazil have led to a substantial decrease in the number of infections as compared to no NPI since the beginning of the reported cases in Brazil (see red and blue curves in Fig 3), the current measures are not enough to prevent the collapse of the health system in a short period of time with million of infected persons (see Figs 3 and 4)

Summary

Introduction

Coronavirus disease-2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (Sars-CoV-2) is a main threat for the public health systems throughout the globe [1,2,3,4,5,6,7,8,9,10,11,12,13]. It comes from the reasonable assumption that in the early stage of the infection S N, it fights the infection by reducing the number of susceptible persons In our approach it reflects the amount of susceptible individuals undergoing the specific control measure and gi Si represents the fraction of Si not complying with the policy gi. For the case of closure of school and universities (CSU), we use the census-based data of the number of students per age group to reduce the respective number of susceptible individuals They are all supposed to be uninfected in the early stage of the epidemic and we assume that 100% of this target population will not disobey the policy as all schools and universities are closed in Brazil. ; tNPI Þ=tb ð6Þ where this modulation function is the sigmoid function, tNPI is the date at which the measure is implemented and tβ is the number of days it takes to produce effects

Initial conditions and model calibration

Results and discussion

Conclusion

25. Coronavirus