Open Access
Monitoring COVID-19 contagion growth
Loading...
Files
Renda_2021.pdf (1.73 MB)
Full text in Open Access, Published Version
License
Cadmus Permanent Link
Full-text via DOI
ISBN
ISSN
0277-6715; 1097-0258
Issue Date
Type of Publication
Keyword(s)
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Citation
Statistics in medicine, 2021, Vol. 40, No. 18, pp. 4150-4160
Cite
AGOSTO, Arianna, CAMPMAS, Alexandra, GIUDICI, Paolo, RENDA, Andrea, Monitoring COVID-19 contagion growth, Statistics in medicine, 2021, Vol. 40, No. 18, pp. 4150-4160 - https://hdl.handle.net/1814/73176
Abstract
We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective.
Table of Contents
Additional Information
First published online: 11 May 2021
External Links
Publisher
Version
Research Projects
European Commission, 101016233
Sponsorship and Funder Information
This article has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 (grant agreement No 101016233).