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.
a b s t r a c tThis paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2:51 Â 2:51 longitude-latitude grid with time lags relevant to dengue transmission, an El Niñ o Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM-generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.
SummaryBackground: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played.
Diagnoses of living conditions and health status, the constitutive elements for the reproduction of social life in various places, are listed and treated as contents disconnected from the territory. The recognition of social dynamics, habits, and customs is highly important for the determination of human health vulnerabilities, which originate in the interactions of social groups in given geographic spaces. The full use of the territory as a strategy for analyzing and intervening in health conditions presupposes the identification of geographic objects, their utilization by the population, and their importance for flows of persons and materials. It is thus necessary to develop methodologies for the recognition (both in the field and through secondary data) of objects and their forms, which are a condition for human action and existence. This article presents an approach to the incorporation of concepts from human geography in health practices, in light of two main authors: Milton Santos ("constitution of territory") and Anthony Giddens ("constitution of society").
Previous studies have demonstrated statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations to such studies include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues in the context of dengue fever in South East Brazil, where dengue epidemics impact heavily on Brazilian public health services. A spatio-temporal generalised linear mixed model (GLMM) is developed, including both climate and non-climate covariates. Overdispersion and unobserved confounding factors are accounted for via a Negative Binomial formulation and inclusion of both spatial and temporal random effects. Model parameters are estimated in a Bayesian framework to allow full posterior predictive distributions for disease risk to be derived in time and space. Detailed probabilistic forecasts can then be issued for any pre-defined 'alert' thresholds, allowing probabilistic early warnings for dengue epidemics to be made. Using this approach with the criterion 'greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', successful epidemic alerts would have been issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in South East Brazil, during the major 2008 epidemic. Use of seasonal climate forecasts in this model allows predictions to be made several months ahead of an impending epidemic. We argue that the general modelling framework, described here in the context of dengue in Brazil, is potentially valuable in similar applications, both outside of Brazil and for other climate-sensitive diseases.
Desastres naturais e saúde: uma análise da situação do Brasil Natural disasters and health: an analysis of the situation in Brazil (meteorological; hydrological; climatological; geophysical/ geological)
Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities where 0–7% of the remaining forests were selectively logged had the highest malaria risk (1.72, 95% CI 1.18–2.51), and areas with higher rates of selective logging had the lowest risk (0.39, 95% CI 0.23–0.67). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.