Assessing suppression strategies against epidemic outbreaks like COVID-19: the SPQEIR model

28 views • May 14, 2021


Author Name


Christophe Ley

Professor of Mathematical Statistics at Ghent University

Field of Study: Math , Published 2 Projects

COVID-19 SARS-CoV-2 SEIR Model Epidemiology Applied Statistics

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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.

Competing Interest Statement: The authors have declared no competing interest.
Funding Statement

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).

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Luxembourg Center for Systems Biomedicine

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COVID-19 19 Projects
SARS-CoV-2 17 Projects
SEIR Model
SEIR Model 1 Project
Epidemiology 14 Projects