Climatic variables associated with dengue incidence in a city of the Western Brazilian Amazon region

Duarte, Juliana Lúcia; Díaz-Quijano, Fredi Alexander; Batista, Antônio Carlos; Giatti, Leandro Luiz

doi:10.1590/0037-8682-0429-2018

Cited by 28 publications

(22 citation statements)

References 41 publications

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“…In agreement with preview arbovirosis work [29,30], we found the highest number of positive cases of CHIKV infection in the first 3 months of 2016 (January to March) and the peak…”

Section: Discussionsupporting

confidence: 90%

Epidemiologic and clinical investigations during a chikungunya outbreak in Rio Grande do Norte State, Brazil

et al. 2020

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The first autochthonous case of chikungunya virus (CHIKV) infection in Brazil was in September 2014 in the State of Amapá, and from there it rapidly spread across the country. The present study was conducted in 2016 in the state of Rio Grande do Norte, and the aims were to describe the epidemiological and the clinical aspects of the CHIKV outbreak. Biological samples from 284 chikungunya suspected cases were screened for CHIKV and Flavivirus (FV) RNA using qRT-PCR. Negative PCR samples were also screened for anti-CHIKV and anti-FVIgM by ELISA. CHIKV RNA were detected in 125 samples mostly occurring from January through March (46%), mainly affecting adults and older adults. We found a gradual decrease in viral RNA over the disease time. Anti-CHIKV IgM was found in 47.5% after negative CHIKV qRT-PCR. Interestingly, 45.0% simultaneously had positive results for CHIKV and FV IgM, suggesting the occurrence of virus co-circulation. The most frequent symptom was fever (91%). Women presented more chance to develop nausea and abdominal pain compared to men. Our data described and allows us to better understand the clinical and epidemiological aspects of the 2016 chikungunya outbreak in Rio Grande do Norte and can help in the early clinical diagnosis of the virus.

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“…In agreement with preview arbovirosis work [29,30], we found the highest number of positive cases of CHIKV infection in the first 3 months of 2016 (January to March) and the peak…”

Section: Discussionsupporting

confidence: 90%

Epidemiologic and clinical investigations during a chikungunya outbreak in Rio Grande do Norte State, Brazil

et al. 2020

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“…The empty water containers provided by nature (e.g., bamboo tubes, rock pools, and tree holes) or human-made (e.g., uncovered barrels, water tanks, and beverage bottles) will be filled with the water when the rainfall occurs during the rainy season. The consequent expansion in the number of available Aedes breeding sites on nature favors the mosquito development in its egg, larva, and pupa stages, which occurs dominantly in the aquatic environment ( Duarte et al., 2019 ; Viana & Ignotti, 2013 ). This process can potentially create the abundance of vector population and subsequently increase the risk of dengue infection.…”

Section: Resultsmentioning

confidence: 99%

Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis

Nuraini

Fauzi

Fakhruddin

et al. 2021

Infectious Disease Modelling

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Background Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence. Methods A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model. Results The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3–30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70 % at lag 38 days and below 50 mm at lag 50 days, respectively. Conclusion Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.

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“…Humidity and temperature can determine the proper condition to the development of the insects (El-Sayed & Kamel, 2020). Duarte et al (2019) emphasized that health-related events are closely linked to the environmental conditions and that ecological studies are of great importance. There is a need of integrated action between hot, humid season and dengue surveillance services.…”

Section: Discussionmentioning

confidence: 99%

Dengue Incidence and Climatic Factors

Loffredo¹,

Júnior²,

Figueiredo³

et al. 2020

RHS

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Introduction: This study was carried out to analyze the temporal evolution of the incidence of dengue in Araraquara-SP, Brazil, from 2012 to 2016, correlating the incidence with temperature and pluviometry. Methods: It was a cross-sectional design. The monthly number of cases from 2012 to 2016 was collected in the datafile of the Special Health Service of Araraquara-SESA of USP. Climatic variables related to temperature and rainfall were obtained from the websites of Agritempo and Department of Water and Electric Power, respectively. Statistical planning included incidence rates, and the tendency from 2012 to 2016, and it was studied the correlation between the number of dengue cases and each one of the climatic variables, considering the time-lag concept. Results: The incidence rates were 52.68, 376.52, 737.39, 3,660 and 809.48 per 100,000 inhabitants. Significant correlations were observed between the number of dengue cases and climatic variables after 2-4 months for high temperature and after 1-4 months for rainfall. Conclusions: The city faced an epidemic of dengue in 2015. It was identified the time lag in which hot weather and rain favored the occurrence of new cases: 2 to 4 months later for high temperature, and 1 to 4 months later for rainfall.

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Climatic variables associated with dengue incidence in a city of the Western Brazilian Amazon region

Cited by 28 publications

References 41 publications

Epidemiologic and clinical investigations during a chikungunya outbreak in Rio Grande do Norte State, Brazil

Epidemiologic and clinical investigations during a chikungunya outbreak in Rio Grande do Norte State, Brazil

Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis

Dengue Incidence and Climatic Factors

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