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      Aproximación matemática del modelo epidemiológico SIR para la comprensión de las medidas de contención contra la COVID-19 Translated title: Mathematical approach of the SIR epidemiological model for the comprehension of the containment measures against the Covid-19

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          RESUMEN

          En diciembre de 2019 un brote de enfermedad respiratoria aguda de origen presumiblemente zoonótico, y cuyo agente infeccioso es un nuevo coronavirus, fue detectado en la ciudad de Wuhan, China. Desde entonces, la enfermedad por el nuevo coronavirus 2019 (Covid-19) se expandió rápidamente a más de 200 países alrededor del mundo. Para amortiguar los efectos devastadores del virus, los Estados adoptaron medidas epidemiológicas de diversa índole, lo que implicó gastos económicos ingentes y la utilización masiva de los medios de comunicación para hacer extensivas las medidas a toda la población. Para la predicción y mitigación de eventos infecciosos, diversos modelos epidemiológicos, como el SIR, SEIR, MSIR y MSEIR, son empleados. Entre ellos, el más utilizado es el modelo SIR, que se basa en el análisis de la transición de los individuos susceptibles a la infección (S) al estado de individuos infectados que infectan (I) y, finalmente, al de recuperados (curados o fallecidos) (R), mediante el uso de ecuaciones diferenciales. El objetivo del presente artículo fue el desarrollo matemático del modelo SIR y su aplicación para predecir el curso de la pandemia por Covid-19 en la ciudad de Santa Marta (Colombia), a fin de comprender la razón que subyacía a varias de las medidas de contención adoptadas por los Estados del mundo en la lucha contra la pandemia.

          ABSTRACT

          In December 2019, an acute respiratory disease outbreak from zoonotic origin was detected in the city of Wuhan, China. The outbreak’s infectious agent was a type of coronavirus never seen. Thenceforth, the Covid-19 disease has rapidly spread to more than 200 countries around the world. To minimize the devastating effects of the virus, the States have adopted epidemiological measures of various kinds that involved enormous economic expenses and the massive use of the media to explain the measures to the entire population. For the prediction and mitigation of infectious events, various epidemiological models, such as SIR, SEIR, MSIR and MSEIR, are used. Among them, the most widely used is the SIR model, which is based on the analysis of the transition of individuals susceptible to infection (S) to the state of infected individuals that infect (I) and, finally, to that of recovered (cured or deceased) (R), by using differential equations. The objective of this article was the mathematical development of the SIR model and its application to predict the course of the Covid-19 pandemic in the city of Santa Marta (Colombia), in order to understand the reason behind several of the measures of containment adopted by the States of the world in the fight against the pandemic.

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          Most cited references18

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          Using social and behavioural science to support COVID-19 pandemic response

          The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
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            Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents

            Summary Currently, the emergence of a novel human coronavirus, SARS-CoV-2, has become a global health concern causing severe respiratory tract infections in humans. Human-to-human transmissions have been described with incubation times between 2-10 days, facilitating its spread via droplets, contaminated hands or surfaces. We therefore reviewed the literature on all available information about the persistence of human and veterinary coronaviruses on inanimate surfaces as well as inactivation strategies with biocidal agents used for chemical disinfection, e.g. in healthcare facilities. The analysis of 22 studies reveals that human coronaviruses such as Severe Acute Respiratory Syndrome (SARS) coronavirus, Middle East Respiratory Syndrome (MERS) coronavirus or endemic human coronaviruses (HCoV) can persist on inanimate surfaces like metal, glass or plastic for up to 9 days, but can be efficiently inactivated by surface disinfection procedures with 62–71% ethanol, 0.5% hydrogen peroxide or 0.1% sodium hypochlorite within 1 minute. Other biocidal agents such as 0.05–0.2% benzalkonium chloride or 0.02% chlorhexidine digluconate are less effective. As no specific therapies are available for SARS-CoV-2, early containment and prevention of further spread will be crucial to stop the ongoing outbreak and to control this novel infectious thread.
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              • Article: not found

              Complexity of the Basic Reproduction Number (R 0 )

              The basic reproduction number (R0), also called the basic reproduction ratio or rate or the basic reproductive rate, is an epidemiologic metric used to describe the contagiousness or transmissibility of infectious agents. R0 is affected by numerous biological, sociobehavioral, and environmental factors that govern pathogen transmission and, therefore, is usually estimated with various types of complex mathematical models, which make R0 easily misrepresented, misinterpreted, and misapplied. R0 is not a biological constant for a pathogen, a rate over time, or a measure of disease severity, and R0 cannot be modified through vaccination campaigns. R0 is rarely measured directly, and modeled R0 values are dependent on model structures and assumptions. Some R0 values reported in the scientific literature are likely obsolete. R0 must be estimated, reported, and applied with great caution because this basic metric is far from simple.

                Author and article information

                Journal
                Rev Esp Salud Publica
                Rev Esp Salud Publica
                resp
                Revista Española de Salud Pública
                Ministerio de Sanidad, Consumo y Bienestar social
                1135-5727
                2173-9110
                23 September 2020
                Jan-Dec 2020
                : 94
                : e202009109
                Affiliations
                [1 ] originalFacultad de Ciencias de la Salud. Universidad del Magdalena. Santa Marta. Colombia. ORCID: 0000-0003-3649-5079. orgdiv1Facultad de Ciencias de la Salud orgnameUniversidad del Magdalena Santa Marta, Colombia
                [2 ] original Facultad de Ciencias de la Salud. Universidad del Magdalena. Santa Marta. Colombia. ORCID: 0000-0003-3170-3959. orgdiv1Facultad de Ciencias de la Salud orgnameUniversidad del Magdalena Santa Marta, Colombia
                Author notes
                Correspondencia: Jorge Homero Wilches Visbal Facultad de Ciencias de la Salud Universidad del Magdalena Carrera 32, No. 22-08, Sector San Pedro Alejandrino Santa Marta, Colombia. jhwilchev@ 123456gmail.com

                Los autores declaran que no existe ningún conflicto de interés.

                Author information
                https://orcid.org/0000-0003-3649-5079
                https://orcid.org/0000-0003-3170-3959
                Article
                e202009109
                11582845
                32963218
                52ad2f49-124e-47f5-91d9-3fedb82ed051

                This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. You are free to Share (copy and redistribute the material in any medium or format) under the following terms: Attribution (You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use); NonCommercial (You may not use the material for commercial purposes); NoDerivatives (If you remix, transform, or build upon the material, you may not distribute the modified material); No additional restrictions (You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits).

                History
                : 20 May 2020
                : 03 August 2020
                : 23 September 2020
                Page count
                Figures: 7, Tables: 0, Equations: 12, References: 24
                Categories
                Colaboración Especial

                modelo sir,medidas de contención,covid-19,número básico de reproducción,santa marta,colombia,sir model,containment measures,basic reproduction number

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