Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil - Borges, Marcelo Eduardo; Ferreira, Leonardo Souto [UNESP]; Poloni, Silas [UNESP]; Bagattini, Angela Maria; Franco, Caroline [UNESP]; da Rosa, Michelle Quarti Machado; Simon, Lorena Mendes; Camey, Suzi Alves; Kuchenbecker, Ricardo de Souza; Prado, Paulo Inácio; Diniz-Filho, José Alexandre Felizola; Kraenkel, Roberto André [UNESP]; et al. (Elsevier, 2022)
We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.
Keywords: Brazil; COVID-19; Decision support techniques; Dynamic transmission models; Non-pharmaceutical interventions; Schools