Modelling the Test, Trace and Quarantine Strategy to Control the COVID-19 Epidemic in the State of São Paulo, Brazil

Abstract

Testing for detecting the infection by SARS-CoV-2 is the bridge between the lockdown and the opening of society. In this paper we modelled and simulated a test-trace-and-quarantine strategy to control the COVID-19 outbreak in the State of São Paulo, Brasil. The State of São Paulo failed to adopt an effective social distancing strategy, reaching at most 59% in late March and started to relax the measures in late June, dropping to 41% in 08 August. Therefore, São Paulo relies heavily on a massive testing strategy in the attempt to control the epidemic.Two alternative strategies combined with economic evaluations were simulated. One strategy included indiscriminately testing the entire population of the State, reaching more than 40 million people at a maximum cost of 2.25 billion USD, that would reduce the total number of cases by the end of 2020 by 90%. The second strategy investigated testing only symptomatic cases and their immediate contacts – this strategy reached a maximum cost of 150 million USD but also reduced the number of cases by 90%.The conclusion is that if the State of São Paulo had decided to adopt the simulated strategy on April the 1st, it would have been possible to reduce the total number of cases by 90% at a cost of 2.25 billion US dollars for the indiscriminate strategy but at a much smaller cost of 125 million US dollars for the selective testing of symptomatic cases and their contacts.

How optimal allocation of limited testing capacity changes epidemic dynamics

“…Throughout, we focus our analyses on empirically supported parameter values including realistic testing rates. While many existing COVID-19 SIR-like compartmental models explore the effects testing with forms of isolation like quarantine or hospitalization, the majority of these studies assume simple linear equations for the rates at which tests are administered and individuals are isolated (Adhikari et al, 2021; Ahmed et al, 2021; Amaku et al, 2021; Choi and Shim, 2021; Dwomoh et al, 2021; Hussain et al, 2021; Ngonghala et al, 2020; Rong et al, 2020; Saldanã et al, 2020; Sturniolo et al, 2021; Tuite et al, 2020; Verma et al, 2020; Youssef et al, 2021). We show (see Methods: Testing model) that linear models can not fully describe highly limited testing capacity scenarios, and we propose a novel, non-linear testing model that flexibly accounts for resource-rich and resource-limited settings.…”