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

Martí Català, Sergio Alonso, Enrique Álvarez-Lacalle, Daniel López, Père-Joan Cardona, Clara Prats

2020PLoS Computational Biology45 citationsDOIOpen Access PDF

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.

Topics & Concepts

Gompertz functionCoronavirus disease 2019 (COVID-19)Term (time)SurpriseEconometrics2019-20 coronavirus outbreakComputer scienceStatisticsOutbreakMathematicsMedicinePhysicsPsychologyVirologyPathologyQuantum mechanicsInfectious disease (medical specialty)Social psychologyDiseaseCOVID-19 epidemiological studiesSARS-CoV-2 and COVID-19 ResearchCOVID-19 Pandemic Impacts
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