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Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

BACKGROUND: Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is...

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Autores principales: Marques-Toledo, Cecilia de Almeida, Degener, Carolin Marlen, Vinhal, Livia, Coelho, Giovanini, Meira, Wagner, Codeço, Claudia Torres, Teixeira, Mauro Martins
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533462/
https://www.ncbi.nlm.nih.gov/pubmed/28719659
http://dx.doi.org/10.1371/journal.pntd.0005729
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author Marques-Toledo, Cecilia de Almeida
Degener, Carolin Marlen
Vinhal, Livia
Coelho, Giovanini
Meira, Wagner
Codeço, Claudia Torres
Teixeira, Mauro Martins
author_facet Marques-Toledo, Cecilia de Almeida
Degener, Carolin Marlen
Vinhal, Livia
Coelho, Giovanini
Meira, Wagner
Codeço, Claudia Torres
Teixeira, Mauro Martins
author_sort Marques-Toledo, Cecilia de Almeida
collection PubMed
description BACKGROUND: Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. METHODOLOGY / PRINCIPAL FINDINGS: In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. CONCLUSIONS: Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.
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spelling pubmed-55334622017-08-07 Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level Marques-Toledo, Cecilia de Almeida Degener, Carolin Marlen Vinhal, Livia Coelho, Giovanini Meira, Wagner Codeço, Claudia Torres Teixeira, Mauro Martins PLoS Negl Trop Dis Research Article BACKGROUND: Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. METHODOLOGY / PRINCIPAL FINDINGS: In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. CONCLUSIONS: Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity. Public Library of Science 2017-07-18 /pmc/articles/PMC5533462/ /pubmed/28719659 http://dx.doi.org/10.1371/journal.pntd.0005729 Text en © 2017 Marques-Toledo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Marques-Toledo, Cecilia de Almeida
Degener, Carolin Marlen
Vinhal, Livia
Coelho, Giovanini
Meira, Wagner
Codeço, Claudia Torres
Teixeira, Mauro Martins
Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title_full Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title_fullStr Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title_full_unstemmed Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title_short Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level
title_sort dengue prediction by the web: tweets are a useful tool for estimating and forecasting dengue at country and city level
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533462/
https://www.ncbi.nlm.nih.gov/pubmed/28719659
http://dx.doi.org/10.1371/journal.pntd.0005729
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