Since the epidemic outbreak in China our research group has directed most of its energy and efforts toward analyzing this critical situation for the societies.
I. OUR WORK
Current work directions
- We are studying the epidemic phenomena at different levels: the population level, but also at the small contact network level. More results will be announced soon.
- Regarding the tools we are employing: there is first the more straightforward direction of using epidemic modeling, however we use also (epidemic) model-agnostic methods that are more data- and ML-oriented.
- ONADAP is a newly funded research project that will try to produce practical solutions for hospital monitoring and decision making during a period such as the recent pandemic.
New studies and scientific papers
- Winning the competition: enhancing counter-contagion in SIS-like epidemic processes, A. Kalogeratos and S. S. Mannelli.
- Dynamic Epidemic Control via Sequential Resource Allocation, M. Fekom, N. Vayatis, and A. Kalogeratos.
- Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions, T. Evgeniou, M. Fekom, Anton Ovchinnikov, R. Porcher, C. Pouchol, and N. Vayatis.
- A short presentation of an online repository of a selection of papers related to COVID-10 outbreak. M. Garin, M. Limnios, A. Nicolaï, N. Vayatis. Credits: O. Boulant, A. Dib. Github : <https://github.com/
MyrtoLimnios/covid19-biblio>. Kibana: <https://covidreview. org/kibana>
Prior work related to epidemics and control
- Sequential Dynamic Resource Allocation for Epidemic Control, M. Fekom, N. Vayatis, and A. Kalogeratos, will appear at the IEEE Conference on Decision and Control, 2019.
- Information Diffusion and Rumor Spreading, A. Kalogeratos, K. Scaman, L. Corinzia, and N. Vayatis. Chapter in the book Cooperative and Graph Signal Processing – Principles and Applications, eds. P.M. Djuric and C. Richard, Elsevier, 2018.
- A Spectral Method for Activity Shaping in Continuous-Time Information Cascades, K. Scaman, A. Kalogeratos, L. Corinzia, and N. Vayatis, arxiv, Sep 2017.
- Suppressing Epidemics in Networks using Priority-Planning, K. Scaman, A. Kalogeratos, and N. Vayatis. IEEE Transactions on Network Science and Engineering, vol. 3, no. 4, 2016.
- A Greedy Approach for Dynamic Control of Diffusion Processes in Networks, K. Scaman, A. Kalogeratos, and N. Vayatis. IEEE International Conference on Tools with Artificial Intelligence, 2015.
- Tight bounds for influence in diffusion networks and application to bond percolation and epidemiology, R. Lemonnier, K. Scaman, and N. Vayatis. Advances in Neural Information Processing Systems, 2014.
Anytime influence bounds and the explosive behavior of continuous-time diffusion networks, K. Scaman, R. Lemonnier, and N. Vayatis. Advances in Neural Information Processing Systems, 2015.
Disclaimer: We plan to keep this collection of indicative references up to date, however it will in any case remain incomplete due to the large volume of scientific results appearing every day.
– Models: prediction of infections, (de)confinement
- Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries, by Seth Flaxman, Swapnil Mishra, Axel Gandy*, et al.
- Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Neil M. Ferguson et al.
- Covid-19: from model prediction to model predictive control – Ingmar Nopens, by Ingmar Nopens
- Simulation-based Estimation of the Spread of COVID-19 in Iran, Navid Ghaffarzadegan and Hazhir Rahmandad
- Using a delay-adjusted case fatality ratio to estimate under-reporting, by Timothy W. Russell, Joel Hellewell, Sam Abbott, et al.
- A model to forecast the evolution of the number of COVID-19 symptomatic, by Louis Alvarez [see online IPOL demo]
- COVID-19 Italy-France
- COVID-19 China-South Korea
- COVID-19 Italy-France
- COVID-19 BioSP Webpage
- COVID-19: Forecasting short term hospital needs in France, Clément Massonnaud, Jonathan Roux, and Pascal Crépey
- First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment, Kathy Leung et al.
- Expected impact of lockdownin Île-de-France and possible exit strategies, Laura Di Domenico et al.
- An Interactive Tool to Forecast US Hospital Needs in the Coronavirus 2019 Pandemic, K.J. Locey et al.
- Estimating the burden of SARS-CoV-2 in France, Henrik Salje et al.
- Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, S.M. Kissler et al.
- Internet Search Patterns Reveal Clinical Course of DiseaseProgression for COVID-19 and Predict Pandemic Spread in 32Countries, T. Lu and B.Y. Reis
- Facing the COVID-19 epidemicin NYC:a stochastic agent-based model of various intervention strategies, N. Hoertel et al.
– Other resource pages
- Modelling the COVID-19 epidemics, by Samuel Alizon
- COVID Analytics, Resource page from MIT
- MIDAS Online COVID-19 Portal
- Coronavirus tech handbook
- COVID-2019 outbreak assessment, from EPIcx lab
– Talks, tutorials, infonews, posts
- Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”, Washington Post
- Simulating an epidemic
- Perspectives on the Pandemic, by John Ioannidis
- COVID-19: The Exponential Power of Now, by Nicholas Jewell
- Coronavirus : diagnostiquons et traitons ! Premiers résultats pour la chloroquine, by Didier Raoult
- Estimation des paramètres épidémiologiques de la diffusion du virus, by Mircea Sofonea
- How To Tell If We’re Beating COVID-19, by minutephysics
– Web tools
- SEIR Epidemic Calculator, by Gabriel Goh
- (Probably the best) Dashboard to keep track of the evolution in each country, Data from John Hopkins
- Digital tool to help hospitals with COVID-19 capacity planning, Penn Medicine [paper 1] [paper 2] [github]
- nCoV19, from this work
- Covid analytics
- COVID-19 Open Research Dataset (CORD-19)
- Données hospitalières relatives à l’épidémie de COVID-19
- The demography of fatalities from COVID-19 in France
- Open COVID-19 french data
- Coronavirus handbook – Data
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