The spatiotemporal trajectory of a dengue epidemic in a medium-sized city

Morato, Daniela Gonçalves; Barreto, Florisneide Rodrigues; Braga, José Ueleres; Natividade, Márcio Santos da; Costa, Maria da Conceição Nascimento; Morato, Vanêssa; Teixeira, Maria da Glória Lima Cruz

doi:10.1590/0074-0276140388

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…In Recife, a high intensity of dengue transmission was detected earlierbefore 2002 -while in Goiania this threshold was reached later: between 2002 and 2005. In general, differences in the incidence of dengue reflect the time period of the intensity of dengue virus circulation in different urban areas, the vector density and the remaining susceptible population (Amaku et al, 2016;Barcellos and Lowe, 2014;Morato et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The present study has the inherent limitation of using secondary data from dengue surveillance. Such data are prone to underreporting or over-reporting during endemic or epidemic years, or bias to health units capacity in reporting dengue cases (Barcellos and Lowe, 2014;Morato et al, 2015;Runge-Ranzinger et al, 2008). Another drawback of dengue surveillance is the scarce data on serotypes, hampering the identification of the main serotypes causing outbreaks (Barcellos and Lowe, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Time series analysis of dengue surveillance data in two Brazilian cities

et al. 2018

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The aim of the study was to evaluate the temporal patterns of dengue incidence from 2001 to 2014 and forecast for 2015 in two Brazilian cities. We analysed dengue surveillance data (SINAN) from Recife, 1.6 million population, and Goiania, 1.4 million population. We used Auto-Regressive Integrated Moving Average (ARIMA) modelling of monthly notified dengue incidence (2001-2014). Forecasting models (95% prediction interval) were developed to predict numbers of dengue cases for 2015. During the study period, 73,479 dengue cases were reported in Recife varying from 11 cases/100,000 inhab (2004) to 2418 cases/100,000 inhab (2002). In Goiania, 253,008 dengue cases were reported and the yearly incidence varied from 293 cases/100,000 inhab (2004) to 3927 cases/100,000 inhab (2013). Trend was the most important component for Recife, while seasonality was the most important one in Goiania. For Recife, the best fitted model was ARIMA (1,1,3) and for Goiania Seasonal ARIMA (1,0,2) (1,1,2). The model predicted 4254 dengue cases for Recife in 2015; SINAN registered 35,724 cases. For Goiania the model predicted 33,757 cases for 2015; the reported number of cases by SINAN was 74,095, within the 95% prediction interval. The difference between notified and forecasted dengue cases in Recife can be explained by the co-circulation of dengue and Zika virus in 2015. In this year, all cases with rash were notified as "dengue-like" illness. The ARIMA models may be considered a baseline for the time series analysis of dengue incidence before the Zika epidemic.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Time series analysis of dengue surveillance data in two Brazilian cities

et al. 2018

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“…analyze the spatial diffusion of arboviruses, with an emphasis on investigating Dengue fever in the state of Bahia, Brazil 17,18 . This study aimed to identify and compare the 2015-2016 spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia.…”

Section: A Very Limited Number Of Studies Have Sought To Specificallymentioning

confidence: 99%

“…Morato et al (2015) 18 identified a different pattern in their study on the spatial diffusion of Dengue fever in the municipality of Jequié, Bahia, in 2009, where they showed that the disease epidemic occurred initially through expansion and later through relocation.…”

Section: /11mentioning

confidence: 99%

Spatial diffusion of Zika fever epidemics in the Municipality of Salvador-Bahia, Brazil, in 2015-2016: does Zika fever have the same spread pattern as Dengue and Chikungunya fever epidemics?

Santana

Braga

2020

Rev. Soc. Bras. Med. Trop.

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Introduction: The recent emergence and rapid spread of Zika and Chikungunya fevers in Brazil, occurring simultaneously to a Dengue fever epidemic, together represent major challenges to public health authorities. This study aimed to identify and compare the 2015-2016 spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia. Methods: We used two study designs comprising a cross-sectional-to-point pattern and an ecological analysis of lattice data. Residential addresses involving notified cases were geocoded. We used four spatial diffusion analysis techniques: (i) visual inspection of the sequential kernel and choropleth map, (ii) spatial correlogram analysis, (iii) spatial local autocorrelation (LISA) changes analysis and, (iv) nearest neighbor index (NNI) modeling. Results: Kernel and choropleth maps indicated that arboviruses spread to neighboring areas near the first reported cases and occupied these new areas, suggesting a diffusion expansion pattern. A greater case density occurred in central and western areas. In 2015 and 2016, the NNI best-fit model had an S-curve compatible with an expansion pattern for Zika (R 2 = 0.94; 0.95), Chikungunya (R 2 = 0.99; 0.98) and Dengue (R 2 = 0.93; 0.99) epidemics, respectively. Spatial correlograms indicated a decline in spatial lag autocorrelations for the three diseases (expansion pattern). Significant LISA changes suggested different diffusion patterns, although a small number of changes were detected. Conclusions: These findings indicate diffusion expansion, a unique spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia, namely. Knowing how and where arboviruses spread in Salvador-Bahia can help improve subsequent specific epidemic control interventions.

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“…A high dengue incidence rate, like the one seen in Pinhalzinho, generally occurs when surveillance and control actions used to prevent the disease are not fully effective. 7,8 Moreover, such a high incidence rate can be attributed to high infestation by the Ae. aegypti vector and to the introduction or reintroduction of virus serotypes in areas where there is a large proportion of susceptible individuals.…”

Section: Discussionmentioning

confidence: 99%

Características da epidemia de dengue em Pinhalzinho, Santa Catarina, 2015-2016*

Andrioli¹,

Lutinski²

2020

Epidemiologia e Serviços de Saúde

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Objective: To describe the characteristics of the dengue epidemic in Pinhalzinho, Santa Catarina, Brazil, and to investigate the effects of climate variables on Aedes aegypti mosquito infestation. Methods: This was an ecological study using data on dengue cases, hospitalizations and deaths in 2015 and 2016; in addition to climate variables and Aedes aegypti breeding grounds from 2015 to 2018. Results: In the 2015-2016 epidemic, the dengue incidence rate was 12,695.2/100,000 inhabitants. Higher incidence was registered in the female sex (13,926.4/100,000 inhabitants) and in the 50 years and over age group (17,162.0/100,000 inhabitants). Average temperature and relative humidity showed a positive relationship with increase in Aedes aegypti breeding grounds. Conclusion: Dengue incidence during the epidemic was the highest ever recorded in the country. Climate conditions must be considered when planning vector control and dengue prevention actions.

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The spatiotemporal trajectory of a dengue epidemic in a medium-sized city

Cited by 9 publications

References 26 publications

Time series analysis of dengue surveillance data in two Brazilian cities

Time series analysis of dengue surveillance data in two Brazilian cities

Spatial diffusion of Zika fever epidemics in the Municipality of Salvador-Bahia, Brazil, in 2015-2016: does Zika fever have the same spread pattern as Dengue and Chikungunya fever epidemics?

Características da epidemia de dengue em Pinhalzinho, Santa Catarina, 2015-2016*

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