Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence

Renato Machado Cotta; Carolina Palma Naveira-Cotta; pierre magal

A SIRU-type model is implemented for the direct problem solution, while a mixture of an analytical parametric estimation for the early phase epidemic exponential behavior with a Bayesian inference approach for the entire period, are considered for the inverse problem analysis. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset to estimate parameters, and retaining the rest of the evolution data for direct comparison with the predicted results, with excellent agreement. Then, the same approach is applied to the Brazilian case, this time employing the available time series so far for the parametric estimates, and then offering an evolution prediction. Also, some public health intervention measures are critically examined, in addition to those already implemented, permitting the inspection of their impact on the overall dynamics of the disease proliferation. Clearly, a combination of public health interventions can offer a considerable impact reduction on the disease progression within Brasil, as illustrated by the implemented modelling. It was also analyzed the negative impact due to the scarcity of testing kits during a period, which if not solved and even incremented, would lead to an increase on the ratio of unreported to reported symptomatic cases, and consequently on a dramatic epidemic evolution. All rights reserved. No reuse allowed without permission.