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Bayesian inference in epidemics : linear noise analysis
- Publication Year :
- 2023
- Publisher :
- Uppsala universitet, Numerisk analys, 2023.
-
Abstract
- This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of `best case' as well as a `worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.<br />This version final after internal revision
- Subjects :
- FOS: Computer and information sciences
Beräkningsmatematik
Applied Mathematics
Mathematics - Statistics Theory
Statistics Theory (math.ST)
General Medicine
Statistics - Applications
Statistics - Computation
Bayesian modeling
Network model
Primary: 60J70, 62F12, 62F15, Secondary: 65C30, 65C60, 92D30
Computational Mathematics
Modeling and Simulation
FOS: Mathematics
Parameter estimation
Applications (stat.AP)
Ornstein-Uhlenbeck process
General Agricultural and Biological Sciences
Stochastic epidemiological models
Computation (stat.CO)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....511ed812062458f548a9eccbe27b433d