ABSTRACT
Introduction. In the context of the COVID-19 pandemic, which required urgent responses from health systems, and ongoing decision making in a context of limited and evolving evidence, modeling played a significant role in supporting public policy making. Nonetheless, particularly in low and middle-income countries, modeling groups are scarce, and usually not routinely involved in supporting public health policy making. We aimed to appraise COVID-19 modeling work in Brazil during the pandemic. Methods. We performed a scoping review following PRISMA guidelines to identify groups conducting COVID-19 modeling to support health decision-making in Brazil. Search strategies were applied to MEDLINE, LILACS, Embase, ArXiv, and also included National data repositories and gray literature. We excluded reports of models without modeling results. Titles, s, data repository descriptions and full-text articles identified were read and selected by two reviewers. Data extracted included modeling questions, model characteristics (structure, type, and programming), epidemiologic data sources, main outcomes reported, and parameters. To further identify modeling groups that might have not yet published results, snowball sampling was performed, and a short survey was sent electronically. Investigators and policymakers were invited to an online interview, to obtain further information on how they interacted, communicated, and used modeling results. Results. We retrieved 1,061 references. After removing duplicates (127), 1,016 s and titles were screened. From an initial selection of 142 s, 133 research groups were identified, of which 67 didn't meet the eligibility criteria. Of these, 66 groups were invited for an interview, of which 24 were available, including 18 modeling groups from academic institutions, and four groups from State Health departments. Most models assessed the impact of mitigation measures in cases/hospitalization/deaths and healthcare service demand. Interaction and communication with decisionmakers were not well established in most groups. Conclusions. Despite a large number of modeling groups in Brazil, we observed a significant gap in modeling demand and communicating its results to support the decision-making process during the COVID-19 pandemic.
ABSTRACT
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 decisionmaking 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 metapopulation 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 nonacademic 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.