Cargando…
A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm
Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks. For instance, simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6002070/ https://www.ncbi.nlm.nih.gov/pubmed/29928741 http://dx.doi.org/10.1016/j.idm.2017.05.004 |
_version_ | 1783332136542011392 |
---|---|
author | Smirnova, Alexandra Chowell, Gerardo |
author_facet | Smirnova, Alexandra Chowell, Gerardo |
author_sort | Smirnova, Alexandra |
collection | PubMed |
description | Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks. For instance, simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts. In the absence of reliable information about transmission mechanisms of emerging infectious diseases, phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease. In this article, our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014–15 Ebola epidemic in West Africa. |
format | Online Article Text |
id | pubmed-6002070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | KeAi Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-60020702018-06-20 A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm Smirnova, Alexandra Chowell, Gerardo Infect Dis Model Article Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks. For instance, simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts. In the absence of reliable information about transmission mechanisms of emerging infectious diseases, phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease. In this article, our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014–15 Ebola epidemic in West Africa. KeAi Publishing 2017-05-25 /pmc/articles/PMC6002070/ /pubmed/29928741 http://dx.doi.org/10.1016/j.idm.2017.05.004 Text en © 2017 KeAi Communications Co., Ltd. Production and hosting by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Smirnova, Alexandra Chowell, Gerardo A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title_full | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title_fullStr | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title_full_unstemmed | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title_short | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
title_sort | primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6002070/ https://www.ncbi.nlm.nih.gov/pubmed/29928741 http://dx.doi.org/10.1016/j.idm.2017.05.004 |
work_keys_str_mv | AT smirnovaalexandra aprimeronstableparameterestimationandforecastinginepidemiologybyaproblemorientedregularizedleastsquaresalgorithm AT chowellgerardo aprimeronstableparameterestimationandforecastinginepidemiologybyaproblemorientedregularizedleastsquaresalgorithm AT smirnovaalexandra primeronstableparameterestimationandforecastinginepidemiologybyaproblemorientedregularizedleastsquaresalgorithm AT chowellgerardo primeronstableparameterestimationandforecastinginepidemiologybyaproblemorientedregularizedleastsquaresalgorithm |