Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science Processes

Siriyasatien, Padet; Chadsuthi, Sudarat; Jampachaisri, Katechan; Kesorn, Kraisak

doi:10.1109/access.2018.2871241

Cited by 57 publications

(42 citation statements)

References 203 publications

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“…Finally, data characteristics were discussed, classifying them into unstructured, structured, and semi-structured data, identified in 10, 5, and 2 articles, respectively. Siriyasatien et al ( 2018 ) defined the three types of data: Unstructured Data Data stored as text, images, pictures, recordings, and videos. The processing of this type of data may require complex processing systems, such as text mining.…”

Section: Resultsmentioning

confidence: 99%

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Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

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Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

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Section: Resultsmentioning

confidence: 99%

“…To assist in statistical analysis, the software SPSS (Statistical Package for the Social Sciences), validated by different authors (Bragazzi et al 2017 ; Siriyasatien et al 2018 ; Gianfredi et al 2018 ; Li et al 2019 ; Zhang et al 2020 ) is largely used.…”

Section: Resultsmentioning

confidence: 99%

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

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“…This study says that the link between climatic factors and dengue cases are powerful control measure towards dengue outbreak. S. Chadsuthiet.al [13] , They proposed a model for prediction of dengue outbreak using climatic dataset and pre-processing techniques like normalization, data transformation, standardization, feature selection and data cleansing ,algorithms used are decision tree, regression analysis, ANN, SVM, KNN. M. H. Sulaimanet.al [14] , They developed a model for prediction of dengue outbreaks by considering dengue cases, rainfall, temperature as dataset and machine learning algorithm like Least Squares Support Vector Machines.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Epidemic Outbreaks in Specific Region using Machine Learning

Bhavana*,

Suresh,

Kiranmayee

et al. 2020

IJITEE

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Dengue is one amongst the common infectious disease which is caused by dengue virus and transmitted to humans by mosquitoes with this many are infected in varied regions around the world per year. The reason for this virus is atmospheric conditions, which plays a vital role in the outbreak of dengue. Therefore early prediction of dengue is the key to regulate outbreak and reduces the transmission within the community. To overcome this we are using various machine learning (ML) algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest tree (RF) and Decision Tree (DT) are used to predict the dengue outbreak. Prediction is done based on weather parameters like monthly wise maximum temperature, minimum temperature, average temperature, mean temperature, humidity and Precipitation which is considered as weather dataset and this weather dataset is pre- processed using label encoding function before applying into the training models. The performances of all the models are calculated based on weather dataset. After considering performance of all the models we choose random forest as a best predictor for dengue outbreak.

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“…In response to recent outbreaks, there has been an explosion in the number research papers related to disease nowcasting and forecasting 14,15 as well as on the applicability of data fusion approaches 16,17 to capture various aspects of infectious disease dynamics. Accurate, real-time forecasting can inform public health decision makers to allow for improved response, targeted surveillance, and improved mitigation 18 .…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

Manore

Fairchild

Ziemann

et al. 2020

Preprint

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Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil's 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models.

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Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science Processes

Cited by 57 publications

References 203 publications

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Prediction of Epidemic Outbreaks in Specific Region using Machine Learning

Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

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