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Methods incorporating both process and observation error
RELATED ARTICLES
Explain
⌅
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
⌃
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
■
Methods incorporating both process and observation error
Methods incorporating both process and observation error☜☜FFB597
↳
Expectation-maximization (EM)
Expectation-maximization (EM)☜☜FFB597
↳
Kalman filtering and related approaches
Kalman filtering and related approaches☜☜FFB597
↳
Markov chain Monte Carlo
Markov chain Monte Carlo☜☜FFB597
↳
Sequential Monte Carlo (SMC)
Sequential Monte Carlo (SMC) ☜☜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.☜FFFACD
□
Household models
Household models☜Likelihood estimation for groups of households, e.g. exact observations of numbers of cases by generation (Becker, Britton et al)☜FFB597
□
Least-squares fitting
Least-squares fitting☜Use standard optimization approaches to find parameters that minimize the SSQ deviation between predicted and observed incidence (or other disease measures). Assumes (1) constant variance; (2) measurement (observation) error only, no process error☜FFB597
□
Maximum likelihood estimation
Maximum likelihood estimation☜Allow for non-normal counts, typically Poisson/binomial/neg. binomial. Still assumes measurement error only (no process error). Generalized least-squares achieves a similar goal.☜FFB597
□
Sensitive to model misspecification
Sensitive to model misspecification☜Generally true of parametric models; we dont know how sensitive the results will be in particular cases.☜59C6EF
□
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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Entry date (GMT):
3/2/2011 3:26:00 AM
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3/2/2011 3:26:00 AM
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