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Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries.
Swiss Med Wkly. 2020 Jul 13; 150:w20313.SM

Abstract

The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic.

Authors+Show Affiliations

Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.Department of Mechanical Engineering, University of Thessaly, Greece.Computational Science and Engineering Laboratory, ETH Zurich, Switzerland.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32677705

Citation

Karnakov, Petr, et al. "Data-driven Inference of the Reproduction Number for COVID-19 Before and After Interventions for 51 European Countries." Swiss Medical Weekly, vol. 150, 2020, pp. w20313.
Karnakov P, Arampatzis G, Kičić I, et al. Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries. Swiss Med Wkly. 2020;150:w20313.
Karnakov, P., Arampatzis, G., Kičić, I., Wermelinger, F., Wälchli, D., Papadimitriou, C., & Koumoutsakos, P. (2020). Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries. Swiss Medical Weekly, 150, w20313. https://doi.org/10.4414/smw.2020.20313
Karnakov P, et al. Data-driven Inference of the Reproduction Number for COVID-19 Before and After Interventions for 51 European Countries. Swiss Med Wkly. 2020 Jul 13;150:w20313. PubMed PMID: 32677705.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries. AU - Karnakov,Petr, AU - Arampatzis,Georgios, AU - Kičić,Ivica, AU - Wermelinger,Fabian, AU - Wälchli,Daniel, AU - Papadimitriou,Costas, AU - Koumoutsakos,Petros, Y1 - 2020/07/10/ PY - 2020/7/18/entrez PY - 2020/7/18/pubmed PY - 2020/8/1/medline SP - w20313 EP - w20313 JF - Swiss medical weekly JO - Swiss Med Wkly VL - 150 N2 - The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic. SN - 1424-3997 UR - https://www.unboundmedicine.com/medline/citation/32677705/Data_driven_inference_of_the_reproduction_number_for_COVID_19_before_and_after_interventions_for_51_European_countries_ DB - PRIME DP - Unbound Medicine ER -