Christophe Ley
Profile Url: christophe-ley
Professor of Mathematical Statistics at Ghent University
I am Associate Professor of Statistics at the Department of Applied Mathematics, Computer Science and Statistics at Ghent University. My research group currently consists of Domien Craens, Fatemeh Ghaderinezhad, Hans Van Eetvelde (all PhD students at Ghent University), Slađana Babić (PhD student at Ghent University and Vlerick Business School, co-supervised by David Veredas), Brett Rowland (PhD student at the University of Pretoria, co-supervised by Andriette Bekker and Mohammad Arashi), Mingo Damian (PhD student at the University of Luxembourg, co-supervised by Jack Hale and Stanislaus Schymanski), and Ola Ronning (PhD student at the University of Copenhagen, co-supervised by Thomas Hamelryck). Till December 2020 Luciana De Michelis Mendonca was my post-doc at Ghent University, co-supervised with Erik Witvrouw. For further details about my professional activities, I invite you to use the dropdown menu to access other parts of my homepage ;
my full Curriculum Vitae is available here.
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The math behind corona: epidemiological models and simulations
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ResearchGate Page
<h6 style="-webkit-font-smoothing: antialiased; margin-top: 15px; margin-bottom: 15px; margin-left: -15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.43em;"> Abstract</h6><p id="p-2" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to suppress it, but the efficacy of distinct measures is not yet well quantified. In this paper, we propose a novel tool to achieve this quantification. In fact, this paper develops a new extended epidemic SEIR model, informed by a socio-political classification of different interventions, to assess the value of several suppression approaches. First, we inquire the conceptual effect of suppression parameters on the infection curve. Then, we illustrate the potential of our model on data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lock-down is an effective pandemic suppression measure, a combination of social distancing and contact tracing can achieve similar suppression synergistically. This quantitative understanding will support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.</p><h6 style="-webkit-font-smoothing: antialiased; margin-top: 15px; margin-bottom: 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.57em;">Competing Interest Statement: The authors have declared no competing interest.</h6><div><h6 style="-webkit-font-smoothing: antialiased; margin-top: 15px; margin-bottom: 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.57em;">Funding Statement</h6><p id="p-4" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">DP and SM's work is supported by the FNR PRIDE DTU CriTiCS, ref 10907093. FK's work is supported by the Luxembourg National Research Fund PRIDE17/12244779/PARK-QC. A.H. work was partially supported by the Fondation Cancer Luxembourg. JG is partly supported by the 111 Project on Computational Intelligence and Intelligent Control, ref B18024. AA is supported by the Luxembourg National Research Fund (FNR) (Project code: 13684479). JAA is supported by the FWO research project G.0826.15N (Flemish Science Foundation), GOA/12/014 project (Research Fund KU Leuven), Project MTM2016-76969-P from the Spanish State Research Agency (AEI) co-funded by the European Regional Development Fund (ERDF) and the Competitive Reference Groups 2017-2020 (ED431C 2017/38) from the Xunta de Galicia through the ERDF. LM and AA are partly supported by FNR COVID-19 Fast-Track (project PREVID 14863306).</p></div><div><h6 style="-webkit-font-smoothing: antialiased; margin-top: 15px; margin-bottom: 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.57em;">Author Declarations</h6><p id="p-5" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.</p><p id="p-6" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Yes</p><p id="p-7" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">The details of the IRB/oversight body that provided approval or exemption for the research described are given below:</p><p id="p-8" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Luxembourg Center for Systems Biomedicine</p><p id="p-9" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.</p><p id="p-10" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Yes</p><p id="p-11" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).</p><p id="p-12" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Yes</p><p id="p-13" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.</p><p id="p-14" style="-webkit-font-smoothing: antialiased; margin: 0px 0px 15px; border: 0px; outline: 0px; vertical-align: baseline; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: inherit; overflow-wrap: break-word;">Yes</p></div>
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SĂ©minaire Statistique et Sport de la SFdS
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The COVID-19 pandemic has left its marks in the sports world, forcing the full-stop of all sports-related activities in the first half of 2020. Football leagues were suddenly stopped and each country was hesitating between a relaunch of the competition and a premature ending. Some opted for the latter option, and took as the final standing of the season the ranking from the moment the competition got interrupted. This decision has been perceived as unfair, especially by those teams who had remaining matches against easier opponents. In this paper, we introduce a tool to calculate in a fairer way the final standings of domestic leagues that have to stop prematurely: our Probabilistic Final Standing Calculator (PFSC). It is based on a stochastic model taking into account the results of the matches played and simulating the remaining matches, yielding the probabilities for the various possible final rankings. We have compared our PFSC with state-of-the-art prediction models, using previous seasons which we pretend to stop at different points in time. We illustrate our PFSC by showing how a probabilistic ranking of the French Ligue 1 in the stopped 2019-2020 season could have led to alternative, potentially fairer, decisions on the final standing.
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Video Abstract (AI generated)
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BackgroundWorldwide more than 72 million people have been infected and 1.6 million died with SARS-CoV-2 by 15th December 2020. Non-pharmaceutical interventions which decrease social interaction have been implemented to reduce the spread of SARS-CoV-2 and to mitigate stress on healthcare systems and prevent deaths. The pandemic has been tackled with disparate strategies by distinct countries resulting in different epidemic dynamics. However, with vaccines now becoming available, the current urgent open question is how the interplay between vaccination strategies and social interaction will shape the pandemic in the next months. MethodsTo address this question, we developed an extended Susceptible-Exposed-Infectious-Removed (SEIR) model including social interaction, undetected cases and the progression of patients trough hospitals, intensive care units (ICUs) and death. We calibrated our model to data of Luxem-bourg, Austria and Sweden, until 15th December 2020. We incorporated the effect of vaccination to investigate under which conditions herd immunity would be achievable in 2021. ResultsThe model reveals that Sweden has the highest fraction of undetected cases, Luxembourg displays the highest fraction of infected population, and all three countries are far from herd immunity as of December 2020. The model quantifies the level of social interactions, and allows to assess the level which would keep Reff (t) below 1. In December 2020, this level is around 1/3 of what it was before the pandemic for all the three countries. The model allows to estimate the vaccination rate needed for herd immunity and shows that 2700 vaccinations/day are needed in Luxembourg to reach it by mid of April and 45,000 for Austria and Sweden. The model estimates that vaccinating the whole countrys population within 1 year could lead to herd immunity by July in Luxembourg and by August in Austria and Sweden. ConclusionThe model allows to shed light on the dynamics of the epidemics in different waves and countries. Our results emphasize that vaccination will help considerably but not immediately and therefore social measures will remain important for several months before they can be fully alleviated.
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Video Abstract (AI generated)
Paper
Preprint
Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to suppress it, but the efficacy of distinct measures is not yet well quantified. In this paper, we propose a novel tool to achieve this quantification. In fact, this paper develops a new extended epidemic SEIR model, informed by a socio-political classification of different interventions, to assess the value of several suppression approaches. First, we inquire the conceptual effect of suppression parameters on the infection curve. Then, we illustrate the potential of our model on data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lock-down is an effective pandemic suppression measure, a combination of social distancing and contact tracing can achieve similar suppression synergistically. This quantitative understanding will support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.