Cargando…
Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand
Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electro...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255319/ https://www.ncbi.nlm.nih.gov/pubmed/25267408 http://dx.doi.org/10.1017/S0950268813003348 |
_version_ | 1782347415968284672 |
---|---|
author | AGUIAR, M. PAUL, R. SAKUNTABHAI, A. STOLLENWERK, N. |
author_facet | AGUIAR, M. PAUL, R. SAKUNTABHAI, A. STOLLENWERK, N. |
author_sort | AGUIAR, M. |
collection | PubMed |
description | Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets. |
format | Online Article Text |
id | pubmed-4255319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-42553192014-12-05 Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand AGUIAR, M. PAUL, R. SAKUNTABHAI, A. STOLLENWERK, N. Epidemiol Infect Original Papers Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets. Cambridge University Press 2014-11 2014-01-24 /pmc/articles/PMC4255319/ /pubmed/25267408 http://dx.doi.org/10.1017/S0950268813003348 Text en © Cambridge University Press 2014 https://creativecommons.org/licenses/by-nc-sa/3.0/The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence <http://creativecommons.org/licenses/by-nc-sa/3.0/ (https://creativecommons.org/licenses/by-nc-sa/3.0/) >. The written permission of Cambridge University Press must be obtained for commercial re-use. |
spellingShingle | Original Papers AGUIAR, M. PAUL, R. SAKUNTABHAI, A. STOLLENWERK, N. Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title | Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title_full | Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title_fullStr | Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title_full_unstemmed | Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title_short | Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand |
title_sort | are we modelling the correct dataset? minimizing false predictions for dengue fever in thailand |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255319/ https://www.ncbi.nlm.nih.gov/pubmed/25267408 http://dx.doi.org/10.1017/S0950268813003348 |
work_keys_str_mv | AT aguiarm arewemodellingthecorrectdatasetminimizingfalsepredictionsfordenguefeverinthailand AT paulr arewemodellingthecorrectdatasetminimizingfalsepredictionsfordenguefeverinthailand AT sakuntabhaia arewemodellingthecorrectdatasetminimizingfalsepredictionsfordenguefeverinthailand AT stollenwerkn arewemodellingthecorrectdatasetminimizingfalsepredictionsfordenguefeverinthailand |