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      Modelos predictivos de la epidemia de COVID-19 en España con curvas de Gompertz Translated title: Predictive models of the COVID-19 epidemic in Spain with Gompertz curves

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          RESUMEN

          Durante la crisis de salud internacional provocada por la pandemia de COVID-19, además de conocer los datos sobre contagios, muertes y ocupación de camas hospitalarias también es necesario hacer predicciones que ayuden a la gestión de la crisis por parte de las autoridades sanitarias. El presente trabajo tiene como objetivo describir la metodología utilizada para la elaboración de modelos predictivos de contagios y defunciones para la epidemia de COVID-19 en España basados en curvas de Gompertz. La metodología se aplica al total del país y a cada una de sus comunidades autónomas. De acuerdo con los datos oficiales publicados a la fecha de realización de este trabajo, y a través de los modelos descritos, estimamos un total de alrededor de 240.000 contagiados y 25.000 fallecidos al final de la epidemia. Pronosticamos el final de la epidemia entre los meses de junio y julio de 2020.

          Translated abstract

          During the international health crisis caused by the COVID-19 pandemic, it is necessary not only to know the data on infections, deaths and the occupation of hospital beds, but also to make predictions that help health authorities in the management of the crisis. The present work aims to describe the methodology used to develop predictive models of infections and deaths for the COVID-19 epidemic in Spain, based on Gompertz curves. The methodology is applied to the country as a whole and to each of its Autonomous Communities. Based on the official data available on the date of this work, and through the models described, we estimate a total of around 240.000 infected and 25.000 deaths at the end of the epidemic. At a national level, we forecast the end of the epidemic between June and July 2020.

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          Most cited references10

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          Is Open Access

          Dose-Response Analysis Using R

          Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.
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            Modeling of the Bacterial Growth Curve

            Several sigmoidal functions (logistic, Gompertz, Richards, Schnute, and Stannard) were compared to describe a bacterial growth curve. They were compared statistically by using the model of Schnute, which is a comprehensive model, encompassing all other models. The t test and the F test were used. With the t test, confidence intervals for parameters can be calculated and can be used to distinguish between models. In the F test, the lack of fit of the models is compared with the measuring error. Moreover, the models were compared with respect to their ease of use. All sigmoidal functions were modified so that they contained biologically relevant parameters. The models of Richards, Schnute, and Stannard appeared to be basically the same equation. In the cases tested, the modified Gompertz equation was statistically sufficient to describe the growth data of Lactobacillus plantarum and was easy to use.
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              R: A Language and Environment for Statistical Computing

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                Author and article information

                Contributors
                Journal
                Gac Sanit
                Gac Sanit
                Gaceta Sanitaria
                SESPAS. Published by Elsevier España, S.L.U.
                0213-9111
                1578-1283
                29 May 2020
                29 May 2020
                Affiliations
                [0005]Escuela Andaluza de Salud Pública, Granada, España, Observatorio de Salud y Medio Ambiente de Andalucía (OSMAN), Algeciras (Cádiz), España, CIBER de Epidemiología y Salud Pública (CIBERESP), España
                Author notes
                [* ]Autor para correspondencia. pablosvillegas@ 123456gmail.com
                Article
                S0213-9111(20)30125-4
                10.1016/j.gaceta.2020.05.005
                7256556
                ad94c8a3-3bbf-4f67-b6bb-7573c7eb94d8
                © 2020 SESPAS. Published by Elsevier España, S.L.U.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 26 April 2020
                : 15 May 2020
                Categories
                Article

                covid-19,predicción,mortalidad,contagios,forecasting,mortality,infection

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