# GTT (Groupe de Travail des Thésards) 14/06

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*schedule*
le lundi 14 juin 2021 de 17h00 à 18h00

**Organisé par :**E.Bodiot, L.Broux, G.Buritica, D.Lee, T.Randrianarisoa, Y.Tardy

**Intervenant :**Yoan Tardy + Francesco Bonacina (LPSM)

**Lieu :**Jussieu Salle Paul Lévy 16-26 209

**Sujet :**GTT (Groupe de Travail des Thésards)

**Résumé :**

## Collisions of the supercritical Keller-Segel particle system (by Yoan Tardy)

We study a particle system naturally associated to the 2-dimensional Keller-Segel equation. It consists of N Brownian particles in the plane, interacting through a binary attraction in 1/r, where r stands for the distance between two particles. We will discuss about the two cases : the subcritical and the supercritical cases which correspond to the factor of attractivity less and greater than 2. In particular, we will see first that in the subcritical case there are only collisions between pair of particules which are not an issue to define properly the solution in the classical sense, and secondly that in the supercritical case there is an explosion due to a collision between several particles that we will study precisely.

## Influenza Decline During COVID-19 Pandemic: a Global Analysis Leveraging Classification and Regression Trees (by Francesco Bonacina)

The COVID-19 pandemic has caused a profound shock on the ecology of infectious diseases, in particular some studies highlighted that the circulation of influenza dramatically reduced in specific countries after the COVID-19 emergence. Also, they pointed out that the phenomenon could be associated with the non-pharmaceutical interventions (NPIs) applied by governments to control the pandemic. Here we address the problem at the global scale analyzing the FluNet influenza public repository for the periods before (2015-19) and during (2020-21) COVID-19 pandemic. Firstly, we map the space-time variation of influenza and we find that the percentage of positive tests decreased globally by 98.6%, but showing very heterogeneous patterns across countries and seasons. Then, we use Random Forests and Classification And Regression Trees to link the variation of influenza incidence with several covariates such as COVID-19 incidence, strictness of NPIs, change in human mobility, demography, season and geographical region.