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. 2023 Jul 1;15(7):1496.
doi: 10.3390/v15071496.

Spatio-Temporal Cluster Detection of Dengue, Chikungunya, and Zika Viruses' Infection in Rio de Janeiro State from 2010 to 2019

Affiliations

Spatio-Temporal Cluster Detection of Dengue, Chikungunya, and Zika Viruses' Infection in Rio de Janeiro State from 2010 to 2019

Paula Maria Pereira de Almeida et al. Viruses. .

Abstract

Dengue (DENV), chikungunya (CHIKV), and Zika (ZIKV) virus infections are widespread throughout the Rio de Janeiro state. The co-circulation of these emergent arboviruses constitutes a serious public health problem, resulting in outbreaks that can spatially and temporally overlap. Environmental conditions favor the presence, maintenance, and expansion of Aedes aegypti, the primary vector of these urban arboviruses. This study assessed the detection of clusters of urban arboviruses in the Rio de Janeiro state from 2010 to 2019. Notified cases of dengue, chikungunya, and Zika were grouped by year according to the onset of symptoms and their municipality of residence. The study period recorded the highest number of dengue epidemics in the state along with the simultaneous circulation of chikungunya and Zika viruses. The analyzes showed that the central municipalities of the metropolitan regions were associated with higher risk areas. Central municipalities in metropolitan regions were the first most likely clusters for dengue and Zika, and the second most likely cluster for chikungunya. Furthermore, the northwest and north regions were comprised clusters with the highest relative risk for the three arboviruses, underscoring the impact of these arboviruses in less densely populated regions of Brazil. The identification of high-risk areas over time highlights the need for effective control measures, targeted prevention and control interventions for these urban arboviral diseases.

Keywords: Aedes aegypti; Brazil; Rio de Janeiro state; arbovirus infections; spatio-temporal analysis.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Map of Rio de Janeiro state showing (a) health regions; (b) municipalities; and (c) population density.
Figure 2
Figure 2
Time series of reported cases of dengue, chikungunya and Zika, from 2010 to 2019, Rio de Janeiro state.
Figure 3
Figure 3
Distribution of dengue clusters according to (a) order of most probable clusters, (b) relative risk of clusters and (c) relative risk of clusters by municipality.
Figure 3
Figure 3
Distribution of dengue clusters according to (a) order of most probable clusters, (b) relative risk of clusters and (c) relative risk of clusters by municipality.
Figure 4
Figure 4
Distribution of chikungunya clusters according to (a) order of the most likely clusters, (b) relative risk of clusters and (c) individual relative risk of clusters by municipality.
Figure 5
Figure 5
Distribution of Zika clusters according to (a) order of the most likely cluster, (b) relative risk of clusters, and (c) individual relative risk of clusters by municipality.
Figure 5
Figure 5
Distribution of Zika clusters according to (a) order of the most likely cluster, (b) relative risk of clusters, and (c) individual relative risk of clusters by municipality.

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