No abstract

The first confirmed case of Zika virus infection in the Americas was reported in Northeast Brazil in May 2015, although phylogenetic studies indicate virus introduction as early as 2013. Zika rapidly spread across Brazil and to more than 50 other countries and territories on the American continent. The Aedes aegypti mosquito is thought to be the principal vector responsible for the widespread transmission of the virus. However, sexual transmission has also been reported. The explosively emerging epidemic has had diverse impacts on population health, coinciding with cases of Guillain–Barré Syndrome and an unexpected epidemic of newborns with microcephaly and other neurological impairments. This led to Brazil declaring a national public health emergency in November 2015, followed by a similar decision by the World Health Organization three months later. While dengue virus serotypes took several decades to spread across Brazil, the Zika virus epidemic diffused within months, extending beyond the area of permanent dengue transmission, which is bound by a climatic barrier in the south and low population density areas in the north. This rapid spread was probably due to a combination of factors, including a massive susceptible population, climatic conditions conducive for the mosquito vector, alternative non-vector transmission, and a highly mobile population. The epidemic has since subsided, but many unanswered questions remain. In this article, we provide an overview of the discovery of Zika virus in Brazil, including its emergence and spread, epidemiological surveillance, vector and non-vector transmission routes, clinical complications, and socio-economic impacts. We discuss gaps in the knowledge and the challenges ahead to anticipate, prevent, and control emerging and re-emerging epidemics of arboviruses in Brazil and worldwide.

BackgroundRio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM).Methodology/Principal FindingsThree neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007) and during the epidemic (February through April 2008). Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before–after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not), and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue seroprevalence hotspots located at the entrances of the two isolated communities, which are commercial activity areas with high human movement. No association between recent dengue infection and household's high mosquito abundance was observed in this sample.Conclusions/SignificanceThis study contributes to better understanding the dynamics of dengue in Rio de Janeiro by assessing the relationship between dengue seroprevalence, recent dengue infection, and vector density. In conclusion, the variation in spatial seroprevalence patterns inside the neighborhoods, with significantly higher risk patches close to the areas with large human movement, suggests that humans may be responsible for virus inflow to small neighborhoods in Rio de Janeiro. Surveillance guidelines should be further discussed, considering these findings, particularly the spatial patterns for both human and mosquito populations.

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.

BackgroundIn the process of geographical retraction of malaria, some important endemicity pockets remain. Here, we report results from a study developed to obtain detailed community data from an important malaria hotspot in Latin America (Alto Juruá, Acre, Brazil), to investigate the association of malaria with socioeconomic, demographic and living conditions.MethodsA household survey was conducted in 40 localities (n = 520) of Mâncio Lima and Rodrigues Alves municipalities, Acre state. Information on previous malaria, schooling, age, gender, income, occupation, household structure, habits and behaviors related to malaria exposure was collected. Multiple correspondence analysis (MCA) was applied to characterize similarities between households and identify gradients. The association of these gradients with malaria was assessed using regression.ResultsThe first three dimensions of MCA accounted for almost 50% of the variability between households. The first dimension defined an urban/rurality gradient, where urbanization was associated with the presence of roads, basic services as garbage collection, water treatment, power grid energy, and less contact with the forest. There is a significant association between this axis and the probability of malaria at the household level, OR = 1.92 (1.23–3.02). The second dimension described a gradient from rural settlements in agricultural areas to those in forested areas. Access via dirt road or river, access to electricity power-grid services and aquaculture were important variables. Malaria was at lower risk at the forested area, OR = 0.55 (1.23–1.12). The third axis detected intraurban differences and did not correlate with malaria.ConclusionsLiving conditions in the study area are strongly geographically structured. Although malaria is found throughout all the landscapes, household traits can explain part of the variation found in the odds of having malaria. It is expected these results stimulate further discussions on modelling approaches targeting a more systemic and multi-level view of malaria dynamics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-017-2045-z) contains supplementary material, which is available to authorized users.

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.