Modelling Dengue Epidemics with Autoregressive Switching Markov Models (AR-HMM)

Madalina Olteanu (1, 2) , Esther Garcia Garaluz (3) , Miguel Atencia (4) , Gonzalo Joya (3)

1 CES - Centre d'économie de la Sorbonne
2 SAMOS - Statistique Appliquée et MOdélisation Stochastique
3 Departamento de Tecnología Electrónica
4 Departamento de Matemática Aplicada

This work presents the Autorregresive switching-Markov Model (AR-HMM) as a technique that allows modelling time series which are controlled by some unobserved process and finite time lags. Our objetive is to bring to light the potential of this method to give valuable information about how an efficient control strategy can be performed. As a case of study, we apply the method to the dengue fever epidemics (DF) in 2001 in Havana. For this time series, a first experiment with real data is performed in order to obtain the characterization of differents stages of the epidemics.

Mots clés: Time series; Markov switching models; epidemics data

Domaines: Applications [stat.AP]

Madalina Olteanu, Esther Garcia Garaluz, Miguel Atencia, Gonzalo Joya. Modelling Dengue Epidemics with Autoregressive Switching Markov Models (AR-HMM). Joan Cabestany, Francisco Sandoval, Alberto Prieto, Juan M. Corchado. Bio-Inspired Systems: Computational and Ambient Intelligence, Springer Berlin / Heidelberg, pp.886-892, 2009, Lecture Notes in Computer Science n°5517, ⟨10.1007/978-3-642-02478-8_111⟩. ⟨hal-00409115⟩