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Assessing suppression strategies against epidemic outbreaks like COVID-19: the SPQEIR model
Daniele Proverbio; Francoise Kemp; Stefano Magni; Andreas Dominik Husch; Atte Aalto; Laurent Mombaerts; Alexander Skupin; Jorge Goncalves; Jose Ameijeiras-Alonso; Christophe Ley.
Affiliation
  • Daniele Proverbio; University of Luxembourg
  • Francoise Kemp; University of Luxembourg
  • Stefano Magni; University of Luxembourg
  • Andreas Dominik Husch; University of Luxembourg
  • Atte Aalto; University of Luxembourg
  • Laurent Mombaerts; University of Luxembourg
  • Alexander Skupin; University of Luxembourg
  • Jorge Goncalves; University of Luxembourg
  • Jose Ameijeiras-Alonso; KU Leuven
  • Christophe Ley; Ghent University
Preprint in English | medRxiv | ID: ppmedrxiv-20075804
ABSTRACT
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.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Rct Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Rct Language: English Year: 2020 Document type: Preprint
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