Weekly Forecasting Model for Dengue Hemorrhagic Fever Outbreak in Thailand

Puengpreeda, Akeamorn; Yhusumrarn, Suphawit; Sirikulvadhana, Surapong

doi:10.4186/ej.2020.24.3.71

Cited by 10 publications

(9 citation 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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“…Ainda sobre os dados, a grande parte deles utilizou dados epidemiológicos ou climáticos fornecidos por órgãos oficiais do país onde o estudo foi conduzido. Os trabalhos de (Dharmawardana et al, 2018) e de (Puengpreeda, Yhusumrarn, Sirikulvadhana, 2020) chamam atenção por inovar e utilizar, respectivamente, dados de telefonia móvel e de pesquisas realizadas no Google.…”

Section: Resultsunclassified

“…Outro exemplo da utilização de predição através de ML e de DL ocorreu na Tailândia. Em seu trabalho, os pesquisadores (Puengpreeda, Yhusumrarn, Sirikulvadhana, 2020) utilizaram dados climáticos e informações de pesquisas realizadas no Google a respeito de dengue.…”

Section: Introductionunclassified

Previsão de casos de dengue através de Machine Learning e Deep Learning: uma revisão sistemática

Batista

Araújo

Lira

et al. 2021

RSD

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Introdução: a dengue é uma arbovirose causada pelo vírus DENV e transmitida para o homem através do mosquito Aedes aegypti. Atualmente, não existe uma vacina eficaz para combater todas as sorologias do vírus. Diante disso, o combate à doença se volta para medidas preventivas contra a proliferação do mosquito. Os pesquisadores estão utilizando Machine Learning (ML) e Deep Learning (DL) como ferramentas para prever casos de dengue e ajudar os governantes nesse combate. Objetivo: identificar quais técnicas e abordagens de ML e de DL estão sendo utilizadas na previsão de dengue. Métodos: revisão sistemática realizada nas bases das áreas de Medicina e de Computação com intuito de responder as perguntas de pesquisa: é possível realizar previsões de casos de dengue através de técnicas de ML e de DL, quais técnicas são utilizadas, onde os estudos estão sendo realizados, como e quais dados estão sendo utilizados? Resultados: após realizar as buscas, aplicar os critérios de inclusão, exclusão e leitura aprofundada, 14 artigos foram aprovados. As técnicas Random Forest (RF), Support Vector Regression (SVR), e Long Short-Term Memory (LSTM) estão presentes em 85% dos trabalhos. Em relação aos dados, na maioria, foram utilizados 10 anos de dados históricos da doença e informações climáticas. Por fim, a técnica Root Mean Absolute Error (RMSE) foi a preferida para mensurar o erro. Conclusão: a revisão evidenciou a viabilidade da utilização de técnicas de ML e de DL para a previsão de casos de dengue, com baixa taxa de erro e validada através de técnicas estatísticas.

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“…In other words, dengue cases are generally considered as: dengue fever, dengue hemorrhagic fever and dengue shock syndrome, including local and imported cases. However, the studies of (22)(23)(24)(25)(26)(27) are only focused on prediction models for dengue hemorrhagic fever. Regarding the other two diseases transmitted by the Ae.…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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Weekly Forecasting Model for Dengue Hemorrhagic Fever Outbreak in Thailand

Cited by 10 publications

References 0 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

Previsão de casos de dengue através de Machine Learning e Deep Learning: uma revisão sistemática

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

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