Search Results for author: Corentin Cot

Found 6 papers, 0 papers with code

Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights

no code implementations25 Nov 2023 David Lyver, Mihai Nica, Corentin Cot, Giacomo Cacciapaglia, Zahra Mohammadi, Edward W. Thommes, Monica-Gabriela Cojocaru

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region.

Epidemiology

Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations

no code implementations21 Jul 2021 Adele de Hoffer, Shahram Vatani, Corentin Cot, Giacomo Cacciapaglia, Maria Luisa Chiusano, Andrea Cimarelli, Francesco Conventi, Antonio Giannini, Stefan Hohenegger, Francesco Sannino

Furthermore, correlating persistent variants of our approach to epidemiological data, we discover that each new wave of the COVID-19 pandemic is driven and dominated by a new emerging variant.

Epidemiological theory of virus variants

no code implementations28 Jun 2021 Giacomo Cacciapaglia, Corentin Cot, Adele de Hoffer, Stefan Hohenegger, Francesco Sannino, Shahram Vatani

We propose a physical theory underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics.

The field theoretical ABC of epidemic dynamics

no code implementations25 Jan 2021 Giacomo Cacciapaglia, Corentin Cot, Michele Della Morte, Stefan Hohenegger, Francesco Sannino, Shahram Vatani

As important test of the relevance of symmetries we show how to mathematically account for observed phenomena such as multi-wave dynamics.

Epidemiology

You better watch out: US COVID-19 wave dynamics versus vaccination strategy

no code implementations22 Dec 2020 Giacomo Cacciapaglia, Corentin Cot, Anna Sigridur Islind, María Óskarsdóttir, Francesco Sannino

We employ the epidemic Renormalization Group (eRG) framework to understand, reproduce and predict the COVID-19 pandemic diffusion across the US.

Physics and Society Populations and Evolution

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