ArticlePloS one2022
Overlap between dengue, Zika and chikungunya hotspots in the city of Rio de Janeiro.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Spatial epidemiology of dengue and chikungunya in Karnataka using GIS-based analysis.Global health action · 2025Article
- Spatio-temporal analysis of the distribution and co-circulation of dengue, chikungunya, and Zika in Medellín, Colombia, from 2013 to 2021.PLoS neglected tropical diseases · 2025Article
- Rising Incidence and Spatiotemporal Dynamics of Emerging and Reemerging Arboviruses in Brazil.Viruses · 2025Article
- Diagnostic Accuracy of Five Molecular Assays for the Detection of Dengue Virus.Medicina (Kaunas, Lithuania) · 2024Article
- Envelope Protein-Targeting Zika Virus Entry Inhibitors.International journal of molecular sciences · 2024Review
- Temperature affects viral kinetics and vectorial capacity of Aedes aegypti mosquitoes co-infected with Mayaro and Dengue viruses.Parasites & vectors · 2024Article
- Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors.Scientific reports · 2023Article
- Development and validation of a clinical rule for the diagnosis of chikungunya fever in a dengue-endemic area.PloS one · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArboviruses represent a threat to global public health. In the Americas, the dengue fever is endemic. This situation worsens with the introduction of emerging, Zika fever and chikungunya fever, causing epidemics in several countries within the last decade. Hotspot analysis contributes to understanding the spatial and temporal dynamics in the context of co-circulation of these three arboviral diseases, which have the same vector: Aedes aegypti.
objectiveTo analyze the spatial distribution and agreement between the hotspots of the historical series of reported dengue cases from 2000 to 2014 and the Zika, chikungunya and dengue cases hotspots from 2015 to 2019 in the city of Rio de Janeiro.
methodsTo identify hotspots, Gi* statistics were calculated for the annual incidence rates of reported cases of dengue, Zika, and chikungunya by neighborhood. Kendall's W statistic was used to analyze the agreement between diseases hotspots.
resultsThere was no agreement between the hotspots of the dengue fever historical series (2000-2014) and those of the emerging Zika fever and chikungunya fever (2015-2019). However, there was agreement between hotspots of the three arboviral diseases between 2015 and 2019.
conclusionThe results of this study show the existence of persistent hotspots that need to be prioritized in public policies for the prevention and control of these diseases. The techniques used with data from epidemiological surveillance services can help in better understanding of the dynamics of these diseases wherever they circulate in the world.
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