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Statistical Methods for model selection and parameter estimation
RELATED ARTICLES
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⌅
Controlling Infectious Diseases
Controlling Infectious Diseases☜Mapping the issues around endemic diseases—such as malaria, measles, influenza, HIV, Dengue and West Nile Virus—with the aims of identifying improved control strategies and of providing insights into to the work of mathematical biologists, epidemiologists and public health officials in this field.☜F1CEB7
⌃
Research Questions
Research Questions☜What are the research priorities for the field?☜FFB597
⌃
Open mathematical/computational problems?
Open mathematical/computational problems?☜☜FFB597
■
Statistical Methods for model selection and parameter estimation
Statistical Methods for model selection and parameter estimation☜☜FFB597
↳
Nonparametric approaches
Nonparametric approaches☜Use information or summary statistics on disease incidence or dynamics to estimate metrics such as R0, without using a fully specified disease model☜FFB597
↳
Parametric approaches
Parametric approaches☜Given an explicit (full) model of disease, estimate all parameters (or some parameters, e.g. beta/R0 only assuming others known from independent sources) by fitting to disease dynamics☜FFB597
↳
References
References☜☜FFB597
↳
Unobservable events
Unobservable events☜Data are based on observable events. Some key events in disease transmission are unobservable. Inference for unobservable events based on observed data (usually subsequent events of the event of interest) often subject to bias, and/or, non-identifiability issues.☜FFB597
□
Are we too focused on R0?
Are we too focused on R0?☜☜FFB597
□
Graph of this discussion
Graph of this discussion☜Click this to see the whole debate, excluding comments, in graphical form☜dcdcdc
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Entered by:-
Daihai He
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Entry date (GMT):
2/9/2011 8:29:00 PM
Last edit date (GMT):
3/12/2011 8:13:00 PM
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