RT Journal Article
T1 Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks
A1 Proverbio, Daniele
A1 Kemp, Françoise
A1 Magni, Stefano
A1 Husch, Andreas
A1 Aalto, Atte
A1 Mombaerts, Laurent
A1 Skupin, Alexander
A1 Gonçalves, Jorge
A1 Ameijeiras Alonso, José
A1 Ley, Christophe
AB Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical 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 lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can 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.
PB Public Library of Science
SN 1932-6203
YR 2021
FD 2021
LK http://hdl.handle.net/10347/33594
UL http://hdl.handle.net/10347/33594
LA eng
NO Proverbio, D., Kemp, F., Magni, S., Husch, A., Aalto, A., Mombaerts, L., Skupin, A., Gonçalves, J., Ameijeiras-Alonso, J., & Ley, C. (2021). Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks. PLoS ONE, 16(5 May).
NO 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.
DS Minerva
RD 23 abr 2026