COVID-19 Modeling To Support Decision Making In Brazil: A Scoping Review

IntroductionIn 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.MethodsWe 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.ResultsWe 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 decision-makers were not well established in most groups.ConclusionsDespite 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.

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

IntroductionModeling 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.MethodsResponding 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.ResultsThe 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.ConclusionsInfectious 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.

Modeling the impact of child vaccination (5-11 y) on overall COVID-19 related hospitalizations and mortality in a context of omicron variant predominance and different vaccination coverage paces in Brazil.

Background: Developing countries have experienced significant COVID-19 disease burden. With the emergence of new variants, particularly omicron, the disease burden in children has increased. When the first COVID-19 vaccine was approved for use in children aged 5-11 years of age, very few countries recommended vaccination due to limited risk-benefit evidence for vaccination of this population. In Brazil, ranking second in the global COVID-19 death toll, the childhood COVID-19 disease burden increased significantly in early 2022. This prompted a risk-benefit assessment of the introduction and scaling-up of COVID-19 vaccination of children. Methods: To estimate the potential impact of vaccinating children aged 5-11 years with mRNA-based COVID-19 vaccine in the context of omicron dominance, we developed a discrete-time SEIR-like model stratified in age groups, considering a three-month time horizon. We considered three scenarios: No vaccination, slow, and maximum vaccination paces. In each scenario, we estimated the potential reduction in total COVID-19 cases, hospitalizations, deaths, hospitalization costs, and potential years of life lost, considering the absence of vaccination as the base-case scenario. Findings: We estimated that vaccinating at a maximum pace could prevent, between mid-January and April 2022, about 26,000 COVID-19 hospitalizations, and 4200 deaths in all age groups; of which 5400 hospitalizations and 410 deaths in children aged 5-11 years. Continuing vaccination at a slow/current pace would prevent 1450 deaths and 9700 COVID-19 hospitalizations in all age groups in this same time period; of which 180 deaths and 2390 hospitalizations in children only. Interpretation: Maximum vaccination of children results in a significant reduction of COVID-19 hospitalizations and deaths and should be enforced in developing countries with significant disease incidence in children. Funding: This manuscript was funded by the Brazilian Council for Scientific and Technology Development (CNPq - Process # 402834/2020-8).

Modelling optimal vaccination strategies against COVID-19 in a context of Gamma variant predominance in Brazil.

INTRODUCTION: Brazil experienced moments of collapse in its health system throughout 2021, driven by the emergence of variants of concern (VOC) combined with an inefficient initial vaccination strategy against Covid-19. OBJECTIVES: To support decision-makers in formulating COVID-19 immunization policy in the context of limited vaccine availability and evolving variants over time, we evaluate optimal strategies for Covid-19 vaccination in Brazil in 2021, when vaccination was rolled out during Gamma variant predominance. METHODS: Using a discrete-time epidemic model we estimate Covid-19 deaths averted, considering the currently Covid-19 vaccine products and doses available in Brazil; vaccine coverage by target population; and vaccine effectiveness estimates. We evaluated a 5-month time horizon, from early August to the end of December 2021. Optimal vaccination strategies compared the outcomes in terms of averted deaths when varying dose intervals from 8 to 12 weeks, and choosing the minimum coverage levels per age group required prior to expanding vaccination to younger target populations. We also estimated dose availability required over time to allow the implementation of optimal strategies. RESULTS: To maximize the number of averted deaths, vaccine coverage of at least 80 % should be reached in older age groups before starting vaccination into subsequent younger age groups. When evaluating varying dose intervals for AZD1222, reducing the dose interval from 12 to 8 weeks for the primary schedule would result in fewer COVID-19 deaths, but this can only be implemented if accompanied by an increase in vaccine supply of at least 50 % over the coming six-months in Brazil. CONCLUSION: Covid-19 immunization strategies should be tailored to local vaccine product availability and supply over time, circulating variants of concern, and vaccine coverage in target population groups. Modelling can provide valuable and timely evidence to support the implementation of vaccination strategies considering the local context, yet following international and regional technical evidence-based guidance.

Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil.

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.

Social distancing measures in the fight against COVID-19 in Brazil: description and epidemiological analysis by state Medidas de distanciamiento social para el combate a la COVID-19 en Brasil: caracterización y análisis epidemiológico por estado

Medidas de distanciamento social vêm sendo amplamente adotadas para mitigar a pandemia da COVID-19. No entanto, pouco se sabe quanto ao seu impacto no momento da implementação, abrangência e duração da vigência das medidas. O objetivo deste estudo foi caracterizar as medidas de distanciamento social implementadas pelas Unidades da Federação (UF) brasileiras, incluindo o tipo de medida e o momento de sua adoção. Trata-se de um estudo descritivo com caracterização do tipo, momento cronológico e epidemiológico da implementação e abrangência das medidas. O levantamento das medidas foi realizado por meio de buscas em sites oficiais das Secretarias de Governo e no Diário Oficial de cada UF. Os números de casos e óbitos por COVID-19 foram obtidos de uma plataforma de informações oficiais. Consideramos as seguintes categorias de medidas de distanciamento social: suspensão de eventos, suspensão de aulas, quarentena para grupos de risco, paralisação econômica (parcial ou plena), restrição de transporte e quarentena para a população. O momento de implementação considerou a data cronológica e também o momento epidemiológico, levando em conta o tempo após o décimo caso ou primeiro óbito por COVID-19 em cada UF. Todas as UF implementaram medidas de distanciamento, em sua maioria durante a segunda quinzena de março de 2020. Paralisação econômica foi implementada precocemente, anterior ao décimo caso por 67% e anterior ao primeiro óbito por COVID-19 por 89% das UF. As medidas de distanciamento social foram amplamente implementadas no Brasil, de maneira precoce, antes ou na fase inicial da curva de crescimento exponencial de casos e óbitos por COVID-19 na grande maioria das UF. Medidas de distanciamiento social están siendo ampliamente adoptadas para mitigar la pandemia de la COVID-19. No obstante, poco se sabe en cuanto al momento de implementación, alcance y duración de la vigencia de las medidas en su impacto. El objetivo de este estudio fue caracterizar las medidas de distanciamiento social, implementadas por las Unidades de la Federación (UF) brasileñas, incluyendo el tipo de medida y el momento de su implementación. Se trata de un estudio descriptivo con caracterización del tipo, momento cronológico y epidemiológico de la implementación y alcance de las medidas. La obtención de las medidas se realizó a través de búsquedas en sitios oficiales de las Secretarías de Gobierno y Boletín Oficial de cada UF. Los números de casos y óbitos por COVID-19 se obtuvieron de una plataforma de información oficial. Consideramos las siguientes categorías de medidas de distanciamiento social: suspensión de eventos, suspensión de clases, cuarentena para grupos de riesgo, paralización económica (parcial o plena), restricción de transporte y cuarentena para la población. El momento de implementación consideró la fecha cronológica y también el momento epidemiológico, considerando el tiempo tras el 10º caso o 1er óbito por COVID-19 en cada UF. Todas las UF implementaron medidas de distanciamiento, en su mayoría durante la segunda quincena de marzo de 2020. Se implementó la paralización económica precozmente, anterior al 10º caso por 67% y anterior al 1er óbito por COVID-19 por 89% de las UF. Las medidas de distanciamiento social fueron ampliamente implementadas en Brasil, de manera precoz, antes o en la fase inicial de la curva de crecimiento exponencial de casos y óbitos por COVID-19 en la gran mayoría de las UF. Social distancing measures have been widely adopted to mitigate the COVID-19 pandemic. However, little is known about the timing of measures'implementation, scope, and duration in relation to their impact. The study aimed to describe the social distancing measures implemented by Brazil's states and the Federal District, including the types of measures and the timing of their implementation. This is a descriptive study of the measures'type, chronological and epidemiological timing of the implementation, and scope. The survey of measures used searches in official websites of the government departments and each state's Government Register. The official number of COVID-19 cases and deaths were obtained from an official a data platform. We considered the following categories of social distancing measures: suspension of events, school closure, quarantine of risk groups, economic lockdown (partial or full), restrictions on transportation, and quarantine of the population. The implementation's timing considered both the chronological date and the epidemiological timing, based on the time since the 10th case or 1st death from COVID-19 in each state. All the states implemented distancing measures, mostly during the latter half of March 2020. Economic lockdown was implemented early, prior to the 10th case by 67% of the states and prior to the 1st death from COVID-19 by 89% of the states. Early social distancing measures were widely implemented in Brazil, before or in the initial phase of the exponential growth curve of COVID-19 cases and deaths in the great majority of states.

Social distancing measures in the fight against COVID-19 in Brazil: description and epidemiological analysis by state. / Medidas de distanciamento social para o enfrentamento da COVID-19 no Brasil: caracterização e análise epidemiológica por estado.

Social distancing measures have been widely adopted to mitigate the COVID-19 pandemic. However, little is known about the timing of measures' implementation, scope, and duration in relation to their impact. The study aimed to describe the social distancing measures implemented by Brazil's states and the Federal District, including the types of measures and the timing of their implementation. This is a descriptive study of the measures' type, chronological and epidemiological timing of the implementation, and scope. The survey of measures used searches in official websites of the government departments and each state's Government Register. The official number of COVID-19 cases and deaths were obtained from an official a data platform. We considered the following categories of social distancing measures: suspension of events, school closure, quarantine of risk groups, economic lockdown (partial or full), restrictions on transportation, and quarantine of the population. The implementation's timing considered both the chronological date and the epidemiological timing, based on the time since the 10th case or 1st death from COVID-19 in each state. All the states implemented distancing measures, mostly during the latter half of March 2020. Economic lockdown was implemented early, prior to the 10th case by 67% of the states and prior to the 1st death from COVID-19 by 89% of the states. Early social distancing measures were widely implemented in Brazil, before or in the initial phase of the exponential growth curve of COVID-19 cases and deaths in the great majority of states.

Medidas de distanciamento social vêm sendo amplamente adotadas para mitigar a pandemia da COVID-19. No entanto, pouco se sabe quanto ao seu impacto no momento da implementação, abrangência e duração da vigência das medidas. O objetivo deste estudo foi caracterizar as medidas de distanciamento social implementadas pelas Unidades da Federação (UF) brasileiras, incluindo o tipo de medida e o momento de sua adoção. Trata-se de um estudo descritivo com caracterização do tipo, momento cronológico e epidemiológico da implementação e abrangência das medidas. O levantamento das medidas foi realizado por meio de buscas em sites oficiais das Secretarias de Governo e no Diário Oficial de cada UF. Os números de casos e óbitos por COVID-19 foram obtidos de uma plataforma de informações oficiais. Consideramos as seguintes categorias de medidas de distanciamento social: suspensão de eventos, suspensão de aulas, quarentena para grupos de risco, paralisação econômica (parcial ou plena), restrição de transporte e quarentena para a população. O momento de implementação considerou a data cronológica e também o momento epidemiológico, levando em conta o tempo após o décimo caso ou primeiro óbito por COVID-19 em cada UF. Todas as UF implementaram medidas de distanciamento, em sua maioria durante a segunda quinzena de março de 2020. Paralisação econômica foi implementada precocemente, anterior ao décimo caso por 67% e anterior ao primeiro óbito por COVID-19 por 89% das UF. As medidas de distanciamento social foram amplamente implementadas no Brasil, de maneira precoce, antes ou na fase inicial da curva de crescimento exponencial de casos e óbitos por COVID-19 na grande maioria das UF.

Medidas de distanciamiento social están siendo ampliamente adoptadas para mitigar la pandemia de la COVID-19. No obstante, poco se sabe en cuanto al momento de implementación, alcance y duración de la vigencia de las medidas en su impacto. El objetivo de este estudio fue caracterizar las medidas de distanciamiento social, implementadas por las Unidades de la Federación (UF) brasileñas, incluyendo el tipo de medida y el momento de su implementación. Se trata de un estudio descriptivo con caracterización del tipo, momento cronológico y epidemiológico de la implementación y alcance de las medidas. La obtención de las medidas se realizó a través de búsquedas en sitios oficiales de las Secretarías de Gobierno y Boletín Oficial de cada UF. Los números de casos y óbitos por COVID-19 se obtuvieron de una plataforma de información oficial. Consideramos las siguientes categorías de medidas de distanciamiento social: suspensión de eventos, suspensión de clases, cuarentena para grupos de riesgo, paralización económica (parcial o plena), restricción de transporte y cuarentena para la población. El momento de implementación consideró la fecha cronológica y también el momento epidemiológico, considerando el tiempo tras el 10º caso o 1er óbito por COVID-19 en cada UF. Todas las UF implementaron medidas de distanciamiento, en su mayoría durante la segunda quincena de marzo de 2020. Se implementó la paralización económica precozmente, anterior al 10º caso por 67% y anterior al 1er óbito por COVID-19 por 89% de las UF. Las medidas de distanciamiento social fueron ampliamente implementadas en Brasil, de manera precoz, antes o en la fase inicial de la curva de crecimiento exponencial de casos y óbitos por COVID-19 en la gran mayoría de las UF.