Bayesian inference in epidemics: linear noise analysis

Bronstein, SamuelDepartment of Mathematics and Applications, ENS Paris (författare)

Bayesian inference in epidemics : linear noise analysis

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 toa 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.

Engblom, StefanUppsala universitet,Numerisk analys,Tillämpad beräkningsvetenskap,Avdelningen för beräkningsvetenskap(Swepub:uu)steng957 (författare)
Marin, RobinUppsala universitet,Avdelningen för beräkningsvetenskap,Tillämpad beräkningsvetenskap(Swepub:uu)rober323 (författare)
Department of Mathematics and Applications, ENS ParisNumerisk analys (creator_code:org_t)

Ingår i:Mathematical Biosciences and Engineering: American Institute of Mathematical Sciences20:2, s. 4128-41521547-10631551-0018

Av författaren/redakt...: Bronstein, Samue ...; Engblom, Stefan; Marin, Robin

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