Empirical model for short-time prediction of COVID-19 spreading

Author:

Català MartíORCID,Alonso SergioORCID,Alvarez-Lacalle EnriqueORCID,López Daniel,Cardona Pere-JoanORCID,Prats ClaraORCID

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 short-term 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.

Funder

La Caixa Foundation

Agència de Gestió d’Ajuts Universitaris i de Recerca

Ministerio de Ciencia, Innovación y Universidades

FEDER

Directorate-General for Communications Networks, Content and Technology

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference37 articles.

1. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China;C Huang;The Lancet,2020

2. Assessing the use of influenza forecasts and epidemiological modeling in public health decision making in the United States;C Doms;Scientific reports,2018

3. Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study;PE Lekone;Biometrics,2006

4. Estimating the reproduction number of Ebola virus (EBOV) during the 2014 outbreak in West Africa;CL Althaus;PLoS currents,2014

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