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      El modelo Kermack-McKendrick en la propagación de cepas COVID-19: Perú 2020-2021 Translated title: The Kermack-McKendrick model in the spread of COVID-19 strains: Peru 2020-2021

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          Abstract

          RESUMEN: Introducción El modelo epidémico SIR es útil para medir la velocidad de propagación de las cepas COVID-19 (B.1.617.2/P.1/C.37/B.1.621), en términos de umbral epidemiológico R0 a lo largo del tiempo. Objetivo: Evaluar un modelo matemático de tipo diferencial, propio del comportamiento del COVID-19 para el colectivo peruano. Métodos: Se desarrolló un modelo matemático diferencial del comportamiento de la pandemia para el colectivo peruano, partiendo de la experiencia en el control de infecciones Kermack-McKendrick. Se estimó el número de susceptibles S, infectados y diseminando la infección I y recuperados R, con el uso de conjuntos de datos oficiales de la Organización Mundial de la Salud, partiendo del histórico entre el 07 de marzo y el 12 de septiembre de 2020 y; proyectado durante 52 semanas hasta el 11 de septiembre de 2021. Resultados: La menor tasa de infectados ocurrirá a partir del 3 de abril de 2021. Evidenciando un pronóstico de menor transmisibilidad para el 29 de mayo de 2021 con una tasa de infectados (β=0.08) y umbral (R0=0,000), además se cuantificó la exactitud del modelo en 97,795 %, con 2,205 % de error porcentual medio, siendo el valor promedio temporal R0 <1, así que cada persona que contrae la enfermedad infectará a menos de una persona antes de morir o recuperarse, por lo que el brote desaparecerá. Conclusión: La curva de contagios en el Perú dependerá directamente de las medidas de mitigación para frenar la propagación de la infección y predecir una transmisión sostenida a través de la vacunación contra las cepas tipo del COVID-19; con la observancia de las personas de las medidas preventivas.

          Translated abstract

          ABSTRACT: Introduction: The SIR epidemic model is useful for measuring the rate of spread of COVID-19 strains (B.1.617.2/P.1/C.37/B.1.621), in terms of epidemiological threshold R0 over time. Objective: To evaluate a mathematical model of differential type, typical of the behavior of COVID-19 for the Peruvian collective. Methods: A differential mathematical model of the behavior of the pandemic was developed for the Peruvian collective, based on the experience in the control of Kermack-McKendrick infections. The number of susceptible S, infected and spreading infection I and recovered R was estimated, using official datasets from the World Health Organization, based on the history between March 7 and September 12, 2020 and; projected for 52 weeks until September 11, 2021. Results: The lowest rate of infections will occur from April 3, 2021. Evidencing a prognosis of lower transmissibility for May 29, 2021 with an infected rate (β=0.08) and threshold (R0=0.000), the accuracy of the model was also quantified at 97.795%, with 2.205% of average percentage error, with the temporary average value being R0 <1, so each person who contracts the disease will infect less than one person before dying or recovering, so the outbreak will disappear. Conclusion: The curve of infections in Peru will depend directly on mitigation measures to curb the spread of infection and predict sustained transmission through vaccination against covid-19 type strains; with the observance of people of preventive measures.

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          Early dynamics of transmission and control of COVID-19: a mathematical modelling study

          Summary Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. Findings We estimated that the median daily reproduction number (R t) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of R t, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. Funding Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.
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            Modeling the epidemic dynamics and control of COVID-19 outbreak in China

            Background The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures. Methods We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models. Results The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R > 1, and after January 30, all regions had a reproductive number R < 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty. Conclusions We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran. Supplementary Materials The supplementary materials can be found online with this article at 10.1007/s40484-020-0199-0.
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              Impacto de la epidemia del Coronavirus (COVID-19) en la salud mental del personal de salud y en la población general de China

              Resumen En la lucha contra la epidemia del Coronavirus (COVID-19), el personal de salud puede experimentar problemas de salud mental tales como estrés, ansiedad, síntomas depresivos, insomnio, negación, ira y temor. En un estudio en China se observó que la tasa de ansiedad del personal de salud fue del 23,04%, mayor en mujeres que en hombres y mayor entre las enfermeras que entre los médicos. Asimismo, en la población general de China se observó un 53,8% de impacto psicológico moderado a severo; un 16,5% de síntomas depresivos, un 28,8% de síntomas ansiosos y un 8,1% de estrés, todos entre moderados y severos. Los factores asociados con un alto impacto psicológico y niveles elevados de estrés, síntomas de ansiedad y depresión fueron sexo femenino, ser estudiante, tener síntomas físicos específicos y una percepción pobre de la propia salud. Otro estudio en el mismo país detectó un 35% de distrés psicológico en la población general, con las mujeres presentando mayores niveles que los varones, al igual que los sub-grupos de 18-30 años y los mayores de 60 años. La pandemia plantea pues el desafío de cuidar la salud mental del personal de salud tanto como la de la población general. Así, el uso de instrumentos breves de detección de problemas de salud mental, validados en nuestra población, sería de mucha utilidad para los retos de salud pública que afronta el país.
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                Author and article information

                Journal
                eg
                Enfermería Global
                Enferm. glob.
                Universidad de Murcia (Murcia, Murcia, Spain )
                1695-6141
                2023
                : 22
                : 69
                : 309-336
                Affiliations
                [1] Punto Fijo orgnameUniversidad Nacional Experimental Francisco de Miranda Venezuela josefrankpl@ 123456gmail.com
                Article
                S1695-61412023000100011 S1695-6141(23)02206900011
                10.6018/eglobal.521971
                91d5e2d6-945b-4562-8f93-1a3b814d034b

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International License.

                History
                : 23 April 2022
                : 20 July 2022
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 26, Pages: 28
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                SciELO Spain

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                infected,threshold,susceptible,spread,recovered,umbral,propagación,recuperado,infectado

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