Empirical model for short-time prediction of COVID-19 spreading
Català, Martí (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Alonso Muñoz, Sergio (Universitat Politècnica de Catalunya. Departament de Física)
Alvarez-Lacalle, Enrique (Universitat Politècnica de Catalunya. Departament de Física)
López, Daniel (Universitat Politècnica de Catalunya. Departament de Física)
Cardona, Pere-Joan (Institut Germans Trias i Pujol)
Prats, Clara (Institut Germans Trias i Pujol)
Universitat Autònoma de Barcelona

Date:	2020
Abstract:	The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the shortterm evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.
Grants:	European Commission LC-01485746 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017-SGR-500 Agencia Estatal de Investigación PGC2018-095456-B-I00
Note:	Altres ajuts: CP, PJC and MC received funding from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Computational Biology ; COVID-19 ; Europe ; Humans ; Models, Statistical ; Public Health ; SARS-CoV-2
Published in:	PLoS computational biology, Vol. 16 Núm. 12 (december 2020) , p. e1008431, ISSN 1553-7358

DOI: 10.1371/journal.pcbi.1008431
PMID: 33296373

18 p, 2.7 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Articles > Research articles
Articles > Published articles