Wednesdays 16:00 Paris time

Recorded on Youtube. Please connect with: ZOOM Calendar.

- 11/18/2020, 16:00, Marc Lavielle (Inria & CMAP, Polytechnique) Modelling the COVID 19 pandemic requires a model... but also data! Talk, Slides.
Abstract. I will present in this talk some models for different Covid-19 data. I will first propose a SIR-type model for the data provided by the Johns-Hopkins University for several countries: these are the daily numbers of confirmed cases and deaths. The same model is used for all countries but the parameters of the model change from one country to another to reflect differences in dynamics. In particular, the model incorporates a time-dependent transmission rate, whose variations are thought to be related to the public health measures taken by the country in question.
I will then present a model for French hospital data provided by Santé Publique France: daily numbers of hospitalization, admissions in intensive care units, deaths and hospital discharges.
The proposed models may seem relatively simple, but it must be understood that they do not pretend to describe the spread of the pandemic in a precise and detailed way. Their role is to adjust the available data and provide reliable forecasts: their complexity is therefore adjusted to the amount of information available in the data. Indeed, very few parameters are needed to properly describe the outcome of interest and the prediction proves to be stable over time. Two interactive web applications are available to visualize the data and the adjusted models: for JHU data, for SPF data.

- 11/25/2020, 16:00, Félix Cheysson (Agro-Paristech Paris) Properties of Hawkes processes.
Abstract. Hawkes processes are a family of stochastic processes for which the occurrence of any event increases the probability of further events occurring. When count data are only observed in discrete time, we propose a spectral approach for the estimation of Hawkes processes, by means of Whittle's parameter estimation method. To get asymptotic properties for the estimator, we prove alpha-mixing properties for the series of counts, using the Galton-Watson properties of the cluster representation of Hawkes processes. Simulated datasets and an application to the incidence of measles in France illustrate the performances of the estimation, notably of the Hawkes excitation function, even when the time between observations is large.

- 12/2/2020, 16:00 Rolando Rebolledo (University of Valparaiso) Open-system approach to ecological networks.
- 1/6/2021, 16:00 Yahia Sahli (ISFA, Lyon) Dynamic Taylor's law.