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PP25 Brazilian Collaborative Network For COVID-19 Modeling: Successful Experience Of Using Real-Time Science To Support Evidence-Based Decision-Making

2022; Cambridge University Press; Volume: 38; Issue: S1 Linguagem: Inglês

10.1017/s0266462322001702

1471-6348

Ângela Maria Bagattini, Michelle Quarti Machado da Rosa, Lorena Mendes Simon, Gabriel Berg de Almeida, Leonardo Souto Ferreira, Marcelo Eduardo Borges, R. A. Kraenkel, Renato Mendes Coutinho, José Alexandre Felizola Diniz‐Filho, Suzi Alves Camey, Ricardo de Souza Kuchenbecker, Cristiana M. Toscano,

COVID-19 epidemiological studies

Introduction Modeling is important for guiding policy during epidemics. The objective of this work was to describe the experience of structuring a multidisciplinary collaborative network in Brazil for modeling coronavirus disease 2019 (COVID-19) to support decision-making throughout the pandemic. Methods Responding to a national call in June 2020 for proposals on COVID-19 mitigation projects, we established a team of investigators from public universities located in various regions throughout Brazil. The team’s main objective was to model severe acute respiratory syndrome coronavirus 2 transmission dynamics in various demographic and epidemiologic settings in Brazil using different types of models and mitigation interventions. The modeling results aimed to provide information to support policy making. This descriptive study outlines the processes, products, challenges, and lessons learned from this innovative experience. Results The network included 18 researchers (epidemiologists, infectious diseases experts, statisticians, and modelers) from various backgrounds, including ecology, geography, physics, and mathematics. The criteria for joining the network were having a communication channel with public health decision-makers and being involved in generating evidence for public policy. During a 24-month period, the following sub-projects were established: (i) development of a susceptible-exposed-infected-recovered-like, individual-based meta-population and Markov chain model; (ii) projection of COVID-19 transmission and impact over time with respect to cases, hospitalizations, and deaths; (iii) assessment of the impact of non-pharmacological interventions for COVID-19; (iv) evaluation of the impact of reopening schools; and (v) determining optimal strategies for COVID-19 vaccination. In addition, we mapped existing COVID-19 modeling groups nationwide and conducted a systematic review of relevant published research literature from Brazil. Conclusions Infectious disease modeling for guiding public health policy requires interaction between epidemiologists, public health specialists, and modelers. Communicating modeling results in a non-academic format is an additional challenge, so close interaction with policy makers is essential to ensure that the information is useful. Establishing a network of modeling groups will be useful for future disease outbreaks.