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Women in the sexually active age group are far more likely to get Zika than men (+90% increase); sexual transmission is the most probable cause. Women in the 15-65 years age group are also 30% more likely to be reported with dengue than men, which is probably due to women being more careful with their health.

One difficulty for real‐time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.

Resumo: A vigilância de síndrome respiratória aguda grave (SRAG) no Brasil visa a caracterizar a circulação dos vírus Influenza A e B em casos hospitalizados e óbitos, tendo sido ampliada em 2012 para incluir outros vírus respiratórios. A COVID-19 foi detectada no Brasil pela primeira vez na 9ª semana epidemiológica de 2020 e o teste para o vírus SARS-CoV-2 foi incluído no protocolo de vigilância a partir da 12ª semana epidemiológica. O objetivo deste estudo foi investigar o padrão de hospitalizações por SRAG no país após a entrada do SARS-CoV-2, comparando o perfil temporal, etário e de resultados laboratoriais com os anos de 2010 a 2019. Em 2020, a hospitalização por SRAG, contabilizada desde a data do primeiro caso de COVID-19 confirmado até a 12ª semana, superou o observado, no mesmo período, em cada um dos 10 anos anteriores. A faixa etária acima de 60 anos foi a mais acometida, em nível acima do histórico. Houve um aumento considerável de testes laboratoriais negativos, sugerindo a circulação de um vírus diferente dos presentes no painel. Concluímos que o aumento das hospitalizações por SRAG, a falta de informação específica sobre o agente etiológico e a predominância de casos entre idosos, no mesmo período de tempo em que cresce o número de casos novos de COVID-19, é coerente com a hipótese de que os casos graves da doença já estejam sendo detectados pela vigilância de SRAG com sobrecarga para o sistema de saúde. A inclusão da testagem para SARS-CoV-2 no protocolo de vigilância de SRAG e sua efetiva implementação são de grande importância para acompanhar a evolução dos casos graves da doença no país.

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socioeconomic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.

O presente estudo tem o objetivo de descrever os pacientes hospitalizados por síndrome respiratória aguda grave (SRAG) em decorrência da COVID-19 (SRAG-COVID), no Brasil, quanto às suas características demográficas e comorbidades até a 21ª Semana Epidemiológica de 2020. Buscou-se comparar essas características com as dos hospitalizados por SRAG em decorrência da influenza em 2019/2020 (SRAG-FLU) e com a população geral brasileira. As frequências relativas das características demográficas, comorbidades e de gestantes/puérperas entre os pacientes hospitalizados por SRAG-COVID e SRAG-FLU foram obtidas por meio do Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe), e as estimativas para a população geral brasileira foram obtidas por meio de projeções populacionais realizadas pelo Instituto Brasileiro de Geografia e Estatística, dados do Sistema de Informações sobre Nascidos Vivos e de pesquisas de âmbito nacional. Entre os hospitalizados por SRAG-COVID, observou-se uma elevada proporção, em relação ao perfil da população geral brasileira, de indivíduos do sexo masculino, idosos ou com 40 a 59 anos, com comorbidades (diabetes mellitus, doença cardiovascular, doença renal crônica e pneumopatias crônicas) e de gestantes/puérperas. Já entre os hospitalizados por SRAG-FLU, observou-se prevalências superiores às populacionais de indivíduos de 0 a 4 anos de idade ou idosos, de raça ou cor branca, com comorbidades (diabetes mellitus, doença renal crônica, asma e outras pneumopatias crônicas) e de gestantes/puérperas. Esses grupos podem estar evoluindo para casos mais graves da doença, de forma que estudos longitudinais na área são de extrema relevância para investigar esta hipótese e melhor subsidiar políticas públicas de saúde.

Background With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.