An Innovative Big Data Predictive Analytics Framework over Hybrid Big Data Sources with an Application for Disease Analytics

Souza, Joglas; Leung, Carson K.; Cuzzocrea, Alfredo

doi:10.1007/978-3-030-44041-1_59

Cited by 76 publications

(13 citation statements)

References 19 publications

(27 reference statements)

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“…Most of the included studies relied on machine learning methods, particularly supervised learning models, to assess traditional and also novel data streams. These models were useful also for the analysis of traditional data sources, and allowed scientists to harness non-structured data with NLP methods [40,43,48,49,[51][52][53]56,60,65,66,[69][70][71]73,76,77,[79][80][81]84,85,92,98,100,102,105,[110][111][112]114,115,126,127,130,[134][135][136][137][138][139]. Unsupervised learning models were not the method of choice in most studies, possibly because these studies wanted to identify relevant data sources and/or indicators for dengue monitoring and prediction.…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

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

et al. 2022

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Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

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Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

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

et al. 2022

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“…To improve these systems and reduce the delay between diagnosis and reporting, researchers have evaluated novel data sources, especially real-world data (ie, data not collected in experimental conditions [14]), such as emergency department visits, mobile data, and internet-based systems [15][16][17][18]. Other studies on surveillance and forecasting, especially those using climate data [19][20][21], have also shown promising results. Scientists mostly rely on correlation methods to test these data sources [22,23], but other approaches have also been tested, for instance Naive Bayes methods [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Most of these studies were conducted in Asia (70% of the global dengue burden) [2]. Studies in the Americas concerned large territories or countries, such as Brazil and Mexico [24,25], and in the Caribbean, they focused on the bigger islands of the Greater Antilles [21,26].…”

Section: Introductionmentioning

confidence: 99%

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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“…x healthcare, bio-medical, and/or bio-engineering applications (e.g., disease reports [41,42], omic data like genomic data [43,44], epidemiological data and statistics [45][46][47]).…”

Section: Introductionmentioning

confidence: 99%

Big Data Science on COVID-19 Data

Leung

Chen

Shang

et al. 2020

2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE)

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In the current era of big data, high volume of big data can be generated and collected from a wide variety of rich data sources at a rapid rate. Embedded in these big data are useful information and valuable knowledge. Examples include healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a data science solution for analyzing big COVID-19 epidemiological data. The solution helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our data science solution in discovering useful knowledge from big COVID-19 data.

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An Innovative Big Data Predictive Analytics Framework over Hybrid Big Data Sources with an Application for Disease Analytics

Cited by 76 publications

References 19 publications

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

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

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

Big Data Science on COVID-19 Data

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