This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Data driven inference of the reproduction number (R0) for COVID-19 before and after interventions for 51 European countries (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.05.21.20109314
ABSTRACT
The reproduction number (R0) is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. The estimation of its value with respect to the key threshold of 1.0 is a measure of the need, 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 R0 as well as the time points of interventions for 51 European countries. The study relies on the Bayesian calibration of the simple and well established SIR model with data from reported daily infections. The model is able to fit the data for most countries without individual tuning of parameters. We deploy an open source Bayesian inference framework and efficient sampling algorithms to present a publicly available GUI (https//www.cse-lab.ethz.ch/coronavirus/) that allows the user to assess custom data and compare predictions for pairs of European countries. The results provide a ranking based on the rate of the disease's spread suggesting a metric for the effectiveness of social distancing measures. They also serve to demonstrate how geographic proximity and related times of interventions can lead to similarities in the progression of the epidemic.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS