Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/112819
Title: An empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after social distancing interventions
Authors: Álvarez León, Luis Miguel 
UNESCO Clasification: 120317 Informática
120601 Construcción de algoritmos
3202 Epidemologia
Issue Date: 2020
Journal: ArXiv.org 
Abstract: We present an empirical algorithm to forecast the evolution of the number of COVID19 symptomatic patients in the early stages of the pandemic spread and after strict social distancing interventions. The algorithm is based on a low dimensional model for the variation of the exponential growth rate that decreases after the implementation of strict social distancing measures. From the observable data given by the number of tested positive, our model estimates the number of infected hindcast introducing in the model formulation the incubation time. We also use the model to follow the number of infected patients who later die using the registered number of deaths and the distribution time from infection to death. The relationship of the proposed model with the SIR models is studied. Model parameters fitting is done by minimizing a quadratic error between the data and the model forecast. An extended model is also proposed that allows a longer term forecast. An online implementation of the model is avalaible at www.ctim.es/covid19
URI: http://hdl.handle.net/10553/112819
ISSN: 2331-8422
Source: ArXiv.org [ISSN 2331-8422], 2003.10017, 19 de noviembre de 2020
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