Optimal lead time for dengue forecast.
A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of deng...
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ftdoajarticles:oai:doaj.org/article:3aaf279e3e5945be9f30099f79506850 2023-05-15T15:15:14+02:00 Optimal lead time for dengue forecast. Yien Ling Hii Joacim Rocklöv Stig Wall Lee Ching Ng Choon Siang Tang Nawi Ng 2012-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0001848 https://doaj.org/article/3aaf279e3e5945be9f30099f79506850 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC3475667?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0001848 https://doaj.org/article/3aaf279e3e5945be9f30099f79506850 PLoS Neglected Tropical Diseases, Vol 6, Iss 10, p e1848 (2012) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2012 ftdoajarticles https://doi.org/10.1371/journal.pntd.0001848 2022-12-31T10:28:41Z A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of dengue cases in Singapore given the duration required by a local authority to curb an outbreak.We developed a Poisson regression model to analyze relative risks of dengue cases as functions of weekly mean temperature and cumulative rainfall with lag times of 1-5 months using spline functions. We examined the duration of vector control and cluster management in dengue clusters > = 10 cases from 2000 to 2010 and used the information as an indicative window of the time required to mitigate an outbreak. Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area. Our findings show that increasing weekly mean temperature and cumulative rainfall precede risks of increasing dengue cases by 4-20 and 8-20 weeks, respectively. These lag times provided a forecast window of 1-5 months based on the observed weather data. Based on previous vector control operations, the time needed to curb dengue outbreaks ranged from 1-3 months with a median duration of 2 months. Thus, a dengue early warning forecast given 3 months ahead of the onset of a probable epidemic would give local authorities sufficient time to mitigate an outbreak.Optimal timing of a dengue forecast increases the functional value of an early warning system and enhances cost-effectiveness of vector control operations in response to forecasted risks. We emphasize the importance of considering the forecast-mitigation gaps in respective study areas when developing a dengue forecasting model. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 6 10 e1848 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
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 Yien Ling Hii Joacim Rocklöv Stig Wall Lee Ching Ng Choon Siang Tang Nawi Ng Optimal lead time for dengue forecast. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of dengue cases in Singapore given the duration required by a local authority to curb an outbreak.We developed a Poisson regression model to analyze relative risks of dengue cases as functions of weekly mean temperature and cumulative rainfall with lag times of 1-5 months using spline functions. We examined the duration of vector control and cluster management in dengue clusters > = 10 cases from 2000 to 2010 and used the information as an indicative window of the time required to mitigate an outbreak. Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area. Our findings show that increasing weekly mean temperature and cumulative rainfall precede risks of increasing dengue cases by 4-20 and 8-20 weeks, respectively. These lag times provided a forecast window of 1-5 months based on the observed weather data. Based on previous vector control operations, the time needed to curb dengue outbreaks ranged from 1-3 months with a median duration of 2 months. Thus, a dengue early warning forecast given 3 months ahead of the onset of a probable epidemic would give local authorities sufficient time to mitigate an outbreak.Optimal timing of a dengue forecast increases the functional value of an early warning system and enhances cost-effectiveness of vector control operations in response to forecasted risks. We emphasize the importance of considering the forecast-mitigation gaps in respective study areas when developing a dengue forecasting model. |
format |
Article in Journal/Newspaper |
author |
Yien Ling Hii Joacim Rocklöv Stig Wall Lee Ching Ng Choon Siang Tang Nawi Ng |
author_facet |
Yien Ling Hii Joacim Rocklöv Stig Wall Lee Ching Ng Choon Siang Tang Nawi Ng |
author_sort |
Yien Ling Hii |
title |
Optimal lead time for dengue forecast. |
title_short |
Optimal lead time for dengue forecast. |
title_full |
Optimal lead time for dengue forecast. |
title_fullStr |
Optimal lead time for dengue forecast. |
title_full_unstemmed |
Optimal lead time for dengue forecast. |
title_sort |
optimal lead time for dengue forecast. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2012 |
url |
https://doi.org/10.1371/journal.pntd.0001848 https://doaj.org/article/3aaf279e3e5945be9f30099f79506850 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 6, Iss 10, p e1848 (2012) |
op_relation |
http://europepmc.org/articles/PMC3475667?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0001848 https://doaj.org/article/3aaf279e3e5945be9f30099f79506850 |
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https://doi.org/10.1371/journal.pntd.0001848 |
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PLoS Neglected Tropical Diseases |
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6 |
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10 |
container_start_page |
e1848 |
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