A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm.
Infect Dis Model
; 2(2): 268-275, 2017 May.
Article
en En
| MEDLINE
| ID: mdl-29928741
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
/
Screening_studies
Idioma:
En
Revista:
Infect Dis Model
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos