Bárbara Campos Silva Valente, Ana Paula Razal Dalvi, Guilherme Loureiro Werneck
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引用次数: 0
Abstract
Dengue, the most prevalent urban arbovirus in the world, has triggered recurrent epidemics in Rio de Janeiro, Brazil, since the 1980s. This study aimed to describe the spatial-temporal patterns of dengue spread during the epidemic years of 2002, 2008, 2011, 2012, 2013, and 2024 in Rio de Janeiro. This is an ecological study using secondary data on notified confirmed dengue cases aggregated by neighbourhood. The incidence rates were estimated via the local empirical Bayes method. The local spatial autocorrelation indicators assessed incidence clusters, and the monthly geographic trajectory was outlined for each year. The results revealed changes in the spatial distribution of dengue over time, with clusters of high incidences predominating in the northern and central neighbourhoods in 2002 and 2008, and in the western zone in 2011, 2012, and 2013. In 2024, the distribution was predominant throughout the city, with emphasis in the central and western zones. The monthly geographic centre of dengue cases shifted from the west to the north during the peak of the epidemic. These results highlight the heterogeneous nature of dengue transmission in Rio de Janeiro. The incorporation of spatial and temporal analyses in epidemiological studies can enhance targeted and localized dengue control strategies.
期刊介绍:
Epidemiology & Infection publishes original reports and reviews on all aspects of infection in humans and animals. Particular emphasis is given to the epidemiology, prevention and control of infectious diseases. The scope covers the zoonoses, outbreaks, food hygiene, vaccine studies, statistics and the clinical, social and public-health aspects of infectious disease, as well as some tropical infections. It has become the key international periodical in which to find the latest reports on recently discovered infections and new technology. For those concerned with policy and planning for the control of infections, the papers on mathematical modelling of epidemics caused by historical, current and emergent infections are of particular value.