title: On computer-intensive simulation and estimation methods for rare-event analysis in epidemic models.
abstract: This article focuses, in the context of epidemic models, on rare events that may possibly correspond to crisis situations from the perspective of public health. In general, no close analytic form for their occurrence probabilities is available, and crude Monte Carlo procedures fail. We show how recent intensive computer simulation techniques, such as interacting branching particle methods, can be used for estimation purposes, as well as for generating model paths that correspond to realizations of such events. Applications of these simulation-based methods to several epidemic models fitted from real datasets are also considered and discussed thoroughly.
authors.affiliation: Institut Telecom LTCI UMR Telecom ParisTech/CNRS No. 5141, F-75634, Paris, France.
authors.forename: Stéphan
authors.initials: S
authors.lastname: Clémençon
authors.affiliation: INSERM, IAME, UMR 1137, Paris, F-75018, France.|IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, F-75018, Paris, France.
authors.forename: Anthony
authors.initials: A
authors.lastname: Cousien
authors.affiliation: Université Pierre et Marie Curie LPMA UMR CNRS, No. 7599, Paris, France.
authors.forename: Miraine Dávila
authors.initials: MD
authors.lastname: Felipe
authors.affiliation: Laboratoire P. Painlevé UFR de Mathématiques UMR CNRS 8524, Université des Sciences et Technologies Lille 1, Villeneuve d'Ascq Cedex, F-59955, France.
authors.forename: Viet Chi
authors.initials: VC
authors.lastname: Tran
mesh_terms: D003198:Computer Simulation; D058872:Epidemics; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011634:Public Health; D013269:Stochastic Processes
keywords: Monte Carlo simulation; genetic models; importance sampling; interacting branching particle system; multilevel splitting; rare-event analysis; stochastic epidemic model