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Journal Articles Artificial Intelligence in Medicine Year : 2022

Spatio-temporal mixture process estimation to detect population dynamical changes

Abstract

Population monitoring is a challenge in many areas such as public health or ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data evolution. Assuming that mixture models can correctly model populations, we propose new versions of the Expectation-Maximization algorithm to better estimate both the number of clusters together with their parameters. We then combine these algorithms with a temporal statistical model, allowing to detect dynamical changes in population distributions, and name it a spatio-temporal mixture process (STMP). We test STMP on synthetic data, and consider several different behaviors of the distributions, to adjust this process. Finally, we validate STMP on a real data set of positive diagnosed patients to corona virus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.
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Dates and versions

hal-02933217 , version 1 (08-09-2020)
hal-02933217 , version 2 (25-02-2021)
hal-02933217 , version 3 (25-10-2022)

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Solange Pruilh, Anne-Sophie Jannot, Stéphanie Allassonnière. Spatio-temporal mixture process estimation to detect population dynamical changes. Artificial Intelligence in Medicine, 2022, 126, pp.102258. ⟨10.1016/j.artmed.2022.102258⟩. ⟨hal-02933217v3⟩
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