Determination of clusters and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Brazil

Abstract

Dengue occurrence is partially influenced by the immune status of the population. Consequently, the introduction of a new Dengue virus serotype can trigger explosive epidemics in susceptible populations. The determination of clusters in this scenario can help to identify hotspots and understand the disease dispersion regardless of the influence of the population herd immunity. The present study evaluated the pattern and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Espírito Santo state, Brazil. Data on 18,861 dengue cases reported in Vitória from September 2012 to June 2013 were included in the study. The analysis of spatial variation in temporal trend was performed to detect clusters that were compared by their respective relative risk, house index, population density, and income in an ecological study. Overall, 11 clusters were detected. The time trend increase of dengue incidence in the overall study population was 636%. The five clusters that showed a lower time trend increase than the overall population presented a higher incidence in the beginning of the epidemic and, compared to the six clusters with higher time trend increase, they presented higher relative risk for their inhabitants to acquire dengue infection (P-value = 0.02) and a lower income (P-value <0.01). House index and population density did not differ between the clusters. Early increase of dengue incidence and higher relative risk for acquiring dengue infection were favored in low-income areas. Preventive actions and improvement of infrastructure in low-income areas should be prioritized in order to diminish the magnitude of dengue dispersion after the introduction of a new serotype.

Definition of persistent areas with increased dengue risk by detecting clusters in populations with differing mobility and immunity in Rio de Janeiro, Brazil

“…Space-time analyses of dengue cases have been performed in Thailand 36 , Vietnam 37 , and Colombia 38 . In Brazil, three publications were identified that discuss dengue using SaTScan: one analyzes dengue cases in the city of Lavras, Minas Gerais State, between 2007 and 2010 39 ; the second detects clusters based on dengue seasonality in Brazilian municipalities from 2007 to 2011 40 ; and the third, by Vicente et al 41 , analyzes the determination of clusters and factors associated with the spread of dengue during the first epidemic involving DENV-4 in the city of Vitória, Espírito Santo State.…”

Factors associated with the occurrence of dengue epidemics in Brazil: a systematic review

“…Variables related to economic social factors played an important role in the dengue epidemics in the state of Amazonas ( 44 ), in the municipalities of Salvador (Bahia) ( 36 ), Natal (Rio Grande do Norte) ( 22 ), Várzea Paulista (Sao Paulo) ( 33 ), Caraguatatuba (Sao Paulo) ( 41 ), São José do Rio Preto (Sao Paulo) ( 47 ), Osasco (Sao Paulo) ( 37 ), Araraquara (Sao Paulo) ( 35 ), Rio de Janeiro (Rio de Janeiro) ( 14 ), Itaboraí (Rio de Janeiro) ( 32 ), Vitória (Espirito Santo) ( 48 ), and in Brazil as a whole ( 36 ). It was observed that in all regions of Brazil, the socioeconomic factor was determining for the dengue epidemic.…”

Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk

“…Guzetta et al (2018) [ 73 ] found that a large proportion of cases were transmitted via short-distance human movement (< 1 km) with a limited contribution of long-distance commuting within the city. Moreover, research conducted in Venezuela showed dengue infection often clustered in a smaller range which was within and around households and blocks in radius 20–110 m [ 14 ]. Dengue virus transmission is spatially heterogeneous because the dynamics are determined by a complex interplay between environmental and climatic factors, abundance and competence of vector species, human density and behavior, ecological interactions among viral strains, and profiles of immunity in the population [ 74 , 75 ].…”

“…In recent years, GIS has been utilized to assess, identify, and visualize the potential risk factors involved in disease transmission [ 9 , 11 – 14 ]. Employed by many agencies of public health epidemiology, this technology is considered to be a powerful tool in addressing epidemiological problems and providing spatial models for improving the effectiveness of interventions for various infectious diseases, such as black fever, diarrhea, typhoid, malaria, and dengue [ 15 – 18 ].…”