Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.
Background Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, th...
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ftdoajarticles:oai:doaj.org/article:a2c469815e8949a5b2a096a75ea68427 2023-05-15T15:16:25+02:00 Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. Corey M Benedum Kimberly M Shea Helen E Jenkins Louis Y Kim Natasha Markuzon 2020-10-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0008710 https://doaj.org/article/a2c469815e8949a5b2a096a75ea68427 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0008710 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008710 https://doaj.org/article/a2c469815e8949a5b2a096a75ea68427 PLoS Neglected Tropical Diseases, Vol 14, Iss 10, p e0008710 (2020) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1371/journal.pntd.0008710 2022-12-31T10:07:26Z Background Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models. Methods We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC). Results For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61. Conclusions Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic San Juan PLOS Neglected Tropical Diseases 14 10 e0008710 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
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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 Corey M Benedum Kimberly M Shea Helen E Jenkins Louis Y Kim Natasha Markuzon Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models. Methods We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC). Results For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61. Conclusions Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be ... |
format |
Article in Journal/Newspaper |
author |
Corey M Benedum Kimberly M Shea Helen E Jenkins Louis Y Kim Natasha Markuzon |
author_facet |
Corey M Benedum Kimberly M Shea Helen E Jenkins Louis Y Kim Natasha Markuzon |
author_sort |
Corey M Benedum |
title |
Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
title_short |
Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
title_full |
Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
title_fullStr |
Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
title_full_unstemmed |
Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. |
title_sort |
weekly dengue forecasts in iquitos, peru; san juan, puerto rico; and singapore. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doi.org/10.1371/journal.pntd.0008710 https://doaj.org/article/a2c469815e8949a5b2a096a75ea68427 |
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Arctic San Juan |
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Arctic San Juan |
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Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 14, Iss 10, p e0008710 (2020) |
op_relation |
https://doi.org/10.1371/journal.pntd.0008710 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008710 https://doaj.org/article/a2c469815e8949a5b2a096a75ea68427 |
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https://doi.org/10.1371/journal.pntd.0008710 |
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PLOS Neglected Tropical Diseases |
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