Distanciamento social e condições de vida na pandemia COVID-19 em Salvador-Bahia, Brasil

Natividade, Márcio Santos da; Bernardes, Kionna Oliveira; Pereira, Marcos; Miranda, Samilly Silva; Bertoldo, Juracy; Teixeira, Maria da Glória Lima Cruz; Livramento, Humberto Lago; Aragão, Érika

doi:10.1590/1413-81232020259.22142020

Cited by 67 publications

(40 citation statements)

References 15 publications

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“…It may also indicate diagnosis failures due to low population testing. However, we emphasise that further studies are required to understand the dynamics of the disease in the state of Bahia [30].…”

Section: Discussionmentioning

confidence: 99%

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

et al. 2020

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This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

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Section: Discussionmentioning

confidence: 99%

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

et al. 2020

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“…In addition, in many cases, it is not enough to guarantee basic survival needs. It is also important to highlight the lack of access to hygiene information and basic protection items, such as masks and alcohol, by the less favored population (35). As a consequence, these people need to work and thus break the imposed social isolation policies.…”

Section: Resultsmentioning

confidence: 99%

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Lima

Silva

et al. 2020

Front. Public Health

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Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America.

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“…Addressing the pandemic therefore needs to take into consideration social vulnerabilities not only in relation to health conditions but also health service structure, organization and quality in each territory. 23 Danúbia Hillesheim et al…”

Section: Discussionmentioning

confidence: 99%

Síndrome respiratória aguda grave por COVID-19 em crianças e adolescentes no Brasil: perfil dos óbitos e letalidade hospitalar até a 38ᵃ Semana Epidemiológica de 2020

Hillesheim

Tomasi

Figueiró

et al. 2020

Epidemiol. Serv. Saúde

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Resumo Objetivo: Descrever o perfil dos óbitos e a letalidade de síndrome respiratória aguda grave (SRAG) por COVID-19 em crianças e adolescentes hospitalizados no Brasil. Métodos: Estudo transversal, realizado com dados das fichas de notificação de SRAG de crianças e adolescentes (0 a 19 anos) com confirmação laboratorial para COVID-19. Foram incluídas as notificações com evolução completa de SRAG por COVID-19 até a 38ᵃ Semana Epidemiológica de 2020. Resultados: Foram investigadas 6.989 hospitalizações, das quais 661 evoluíram a óbito, perfazendo uma letalidade hospitalar de 9,5%. Observou-se maior letalidade entre menores de 1 ano de idade (14,2%), crianças e adolescentes do sexo feminino (9,7%), indígenas (23,0%) e residentes em zonas rurais (18,1%), como também nas regiões Nordeste (15,4%) e Norte (9,7%) do país. Conclusão: Foram observadas diferenças na letalidade hospitalar, conforme as características sociodemográficas e marcantes desigualdades regionais.

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Distanciamento social e condições de vida na pandemia COVID-19 em Salvador-Bahia, Brasil

Cited by 67 publications

References 15 publications

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Síndrome respiratória aguda grave por COVID-19 em crianças e adolescentes no Brasil: perfil dos óbitos e letalidade hospitalar até a 38ᵃ Semana Epidemiológica de 2020

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