Spatial‐Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

Ogashawara, Igor; Li, L.; Moreno-Madriñán, Max J.

doi:10.1029/2019gh000186

Cited by 18 publications

(10 citation statements)

References 73 publications

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“… 35 Hence, the uses of this climatic factor can be further explored as evidence showed an association between the sea surface temperature anomalies index and the number of reported dengue cases. 19 , 20 …”

Section: Discussionmentioning

confidence: 99%

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Baharom¹,

Ahmad²,

Hod³

et al. 2022

RMHP

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Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.

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Section: Discussionmentioning

confidence: 99%

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Baharom¹,

Ahmad²,

Hod³

et al. 2022

RMHP

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“…Seasonal variation in temperature (Ogashawara et al., 2019) and rainfall influences the dynamics of the vector and the incidence seasonality of the disease (Viana & Ignotti, 2013) that occurs in Brazil in the first half of the year when there is a higher incidence of cases.…”

Section: Wss and Public Healthmentioning

confidence: 99%

Aedes Aegypti—Insights on the Impact of Water Services

2022

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Epidemics in general and dengue in particular surcharge the health services and the economy. However, the fighting actions are circumscribed to the health sector despite the known positive economic impacts that the investments in water supply and sanitation services (WSS) may cause on society and public health. Besides the fact that urban WSS infrastructure is closely linked to disease prevention, in Brazil, the user's perception and demand are very few and many institutional aspects, like the integration between local WSS, health, environment, and development of city councils, need to be improved and better aligned. In this way, disease control and vector density reduction remain challenges to be overcome. This article addresses the need for greater institutionalization of urban WSS relating them to health aspects from official data. It concludes that the negative impacts of lacking universal access to WSS on dengue and other mosquito diseases are dispersed in all cities, regions, and populations regardless of their degree of development. Furthermore, contrary to what is normally emphasized, the analysis carried out shows that the lack of urban stormwater management systems may be an important component of WSS in preventing the proliferation of dengue disease.

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“…SVR and ANN are adopted to predict dengue infection in Paraguay and ANN shows the best result [37]. In Brazil which confronts the most serious dengue outbreak in 2019, GAM [38] and Pearson correlation analysis [39] are the main methods applied to predict dengue cases in Sao Paulo, whereas nearest-neighbor regression analysis [40] is the technique that researchers use for predicting cases in Rio de Janeiro. To project the spread of dengue virus globally towards the year 2020, 2050 and 2080, an international team of researchers [41] has applied the ensemble of boosted regression tree to show the trend in dengue transmission that around 60% of the global population living in Asia, Africa and Americas, in descending order, are at risk of infection.…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

Kerdprasop¹,

Kerdprasop²

2020

Adv. sci. technol. eng. syst. j.

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Dengue is a viral infection disease transmitted to people through the bite of specific mosquito species living in a tropical zone. According to the World Health Organization, dengue has been listed among the top-ten diseases for 2019 as it makes 3.9 billion people in 128 countries be at risk of infection. One major cause of substantial dengue widespread is the globally warm climate that accelerates rapid growth of mosquito vectors. In this research, we aim to build data-driven models to predict dengue cases using satellite index data to represent temperature, humidity, and greenness over the surface area of Bangkok, which is our target area of dengue prediction because of its high infection cases. Oceanic Niño Index is also used as a predictor variable to represent climate variability. The modeling methods employ seven algorithms from two broad schemes of the machine learning field. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are algorithms from the subfield of computational intelligence, whereas multiple linear regression (MLR), generalized linear model (GLM), support vector regression (SVR), classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) are from the statistical learning subfield. Performances of these algorithms are evaluated on the same set of out-of-sample data. The results are that ANFIS is the best model for predicting dengue outbreak in the capital city of Thailand.

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Spatial‐Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

Cited by 18 publications

References 73 publications

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Aedes Aegypti—Insights on the Impact of Water Services

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

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