Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.
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...
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ftdoajarticles:oai:doaj.org/article:c4179604254f45cebddb05ba0962bdc7 2023-05-15T15:15:48+02:00 Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. Emmanuelle Sylvestre Clarisse Joachim Elsa Cécilia-Joseph Guillaume Bouzillé Boris Campillo-Gimenez Marc Cuggia André Cabié 2022-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0010056 https://doaj.org/article/c4179604254f45cebddb05ba0962bdc7 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0010056 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010056 https://doaj.org/article/c4179604254f45cebddb05ba0962bdc7 PLoS Neglected Tropical Diseases, Vol 16, Iss 1, p e0010056 (2022) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1371/journal.pntd.0010056 2022-12-31T13:46:32Z 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 16 1 e0010056 |
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Directory of Open Access Journals: DOAJ Articles |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Emmanuelle Sylvestre Clarisse Joachim Elsa Cécilia-Joseph Guillaume Bouzillé Boris Campillo-Gimenez Marc Cuggia André Cabié Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Emmanuelle Sylvestre Clarisse Joachim Elsa Cécilia-Joseph Guillaume Bouzillé Boris Campillo-Gimenez Marc Cuggia André Cabié |
author_facet |
Emmanuelle Sylvestre Clarisse Joachim Elsa Cécilia-Joseph Guillaume Bouzillé Boris Campillo-Gimenez Marc Cuggia André Cabié |
author_sort |
Emmanuelle Sylvestre |
title |
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
title_short |
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
title_full |
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
title_fullStr |
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
title_full_unstemmed |
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. |
title_sort |
data-driven methods for dengue prediction and surveillance using real-world and big data: a systematic review. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pntd.0010056 https://doaj.org/article/c4179604254f45cebddb05ba0962bdc7 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 16, Iss 1, p e0010056 (2022) |
op_relation |
https://doi.org/10.1371/journal.pntd.0010056 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010056 https://doaj.org/article/c4179604254f45cebddb05ba0962bdc7 |
op_doi |
https://doi.org/10.1371/journal.pntd.0010056 |
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PLOS Neglected Tropical Diseases |
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16 |
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1 |
container_start_page |
e0010056 |
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1766346147582967808 |