Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

Sylvestre, Emmanuelle; Joachim, Clarisse; Cécilia-Joseph, Elsa; Bouzillé, Guillaume; Campillo‐Gimenez, Boris; Cuggia, Marc; Cabie, André

doi:10.1371/journal.pntd.0010056

Cited by 18 publications

(8 citation statements)

References 147 publications

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“…Vector-based data tend to be underused [37], despite their central place in dengue surveillance, although we observed a rather strong correlation between the number of weekly entomological interventions and the increase in DENV RT-PCR positive rates. Therefore, they should be better integrated into the dengue surveillance system to improve its efficiency because both clinical surveillance and vector-based surveillance are essential for optimal dengue management [38].…”

Section: Principal Findingsmentioning

confidence: 53%

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

Sylvestre¹,

Cécilia-Joseph²,

Bouzillé³

et al. 2022

JMIR Public Health Surveill

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Background Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of −3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of −2 weeks. The most correlated query from Google Trends was the “Dengue” topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of −3 weeks. Conclusions Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

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Section: Principal Findingsmentioning

confidence: 53%

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

Sylvestre¹,

Cécilia-Joseph²,

Bouzillé³

et al. 2022

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

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“…58 The disparity between health scientists who prefer “conventional” modelling assessment measures and information technology scientists who focus on information retrieval metrics may explain the diverse methodological choices. 59 …”

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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“…The machine learning algorithms were developed based on non-clinical data and non-traditional sources to predict outbreaks and decrease reporting delays. The researchers wanted to find studies that tracked and predicted dengue-related outcomes using Big Data, real-world data, and/or machine learning techniques [13].…”

Section: A Literature Reviewmentioning

confidence: 99%

Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models

Krishnan

Zukarnain

Ahmad

et al. 2022

JOIV : Int. J. Inform. Visualization

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Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change.

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Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

Cited by 18 publications

References 147 publications

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

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

Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models

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