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Updated: Aug 1, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
On stochastic dynamic modeling of incidence data
Emmanouil-Nektarios Kalligeris1,2, Alex Karagrigoriou2,3, Christina Parpoula4
1Laboratory of Mathematics Raphaël Salem, University of Rouen Normandy, Avenue de l'Université, BP. 12, 76801 Saint Étienne du Rouvray, Rouen, France.
Abstract:
In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological data. In addition to statistical inference, Changepoint Detection Analysis is performed for the selection of the number of regimes, which reduces the complexity associated with Likelihood Ratio Tests. Within this framework, a three-phase procedure for modeling incidence data is proposed and tested via real and simulated data.
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