Modera: Pedro Gajardo \/ Fernando Mardones, Universidad T\u00e9cnica Federico Santa Mar\u00eda \/ Pontificia Universidad Cat\u00f3lica de Chile<\/p>\n
Expositor<\/strong>: <\/strong>Patricio Cumsille (<\/strong>pcumsille@ubiobio.cl<\/a>) Facultad de Ciencias, Universidad del Biobio; Centro de Biotecnolog\u00eda y Bioingenier\u00eda Universidad de Chile<\/p>\n Resumen:<\/strong> The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic\u2019s consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected\u2019s curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor<\/strong>: <\/strong>Tabita Catal\u00e1n <\/strong>(tabicm.nhg@gmail.com<\/a>) DIM\/CMM Universidad de Chile<\/p>\n Co-autores: <\/strong>Axel Osses (CMM-UCh), Carla Castillo (UDD), H\u00e9ctor Ram\u00edrez (CMM-UCh), Pedro Gajardo (UTFSM)<\/p>\n Resumen:<\/strong> Se dise\u00f1a y se genera informaci\u00f3n y modelaci\u00f3n para desarrollar un modelo lagrangeano de tipo SEIR usando tiempos de residencia y riesgos por ambientes y clases. Los ambientes y clases son seleccionados inspir\u00e1ndose en la estructura de las encuestas origen-destino EOD. El modelo es aplicado para evaluar estrategias de salida y rebrotes de covid19 en el caso de la Regi\u00f3n Metropolitana de Santiago. La metodolog\u00eda permite parametrizar el plan paso a paso y modelar situaciones que otros modelos no permiten como uso de mascarillas en el transporte p\u00fablico, riesgo en comercio y \u00e1reas de esparcimiento, reducci\u00f3n de trabajo\/estudio presencial, etc. El mismo estudio puede extenderse a otras regiones donde est\u00e9 disponible la encuesta origen-destino y pueden actualizarse sus par\u00e1metros con la actualizaci\u00f3n de la encuesta.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor<\/strong>: <\/strong>Antoine Brault<\/strong> (abrault@dim.uchile.cl<\/a>) DIM\/CMM Universidad de Chile<\/p>\n Resumen<\/strong>:<\/strong> Actualmente, no hay estudio de seroprevalencia publicado para determinar el porcentaje de personas que se han infectado con el Covid-19 en Chile. Utilizamos los datos de muertos y de hospitalizados para estimar esta proporci\u00f3n por regiones.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor<\/strong>: <\/strong>Constanza Fosco <\/strong>(constanza.fosco@cigiden.cl<\/a>) Centro de Investigaci\u00f3n para la Gesti\u00f3n Integrada del Riesgo de Desastres (CIGIDEN), Grupo Interdisciplinar de Sistemas Complejos (GISC), Universidad Carlos III, Madrid<\/p>\n Co-autores: <\/strong>Felipe Zurita, CIGIDEN e Instituto de Econom\u00eda UC<\/p>\n Resumen:<\/strong> This paper develops a methodology for the assessment of the short-run effects of lockdown policies on economic activity. The methodology combines labor market data with simulation of an agent-based model. We apply our methodology to the Santiago Metropolitan Region, Chile. We recover the model parameters from observed data, taking into account the recurring policy adjustments that characterized the study window. The model is used to build counterfactual scenarios. We estimate an 8 percent output loss in the first 5 months of the pandemic from the policy that was put in place, achieving a 56 percent reduction in the total number of infections. At the same time, with an output loss to 10.5 percent of GDP, the drop in infections would have reached 92 percent.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor: Gonzalo Mena <\/strong>(gonzalo.mena@stats.ox.ac.uk<\/a>), Department of Statistics, University of Oxford, UK<\/p>\n Co-autores: <\/strong>Pamela P.\u00a0Martinez,\u00a0Ayesha S.\u00a0Mahmud, Pablo A. Marquet, Caroline O. Buckee,\u00a0 Mauricio Santillana. Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan, School of Public Health, Boston, Massachusetts, US; Department of Microbiology, University of Illinois at Urbana Champaign, Illinois, US; Department of Statistics, University of Illinois at Urbana Champaign, Illinois, US; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana Champaign, Illinois, US;\u00a0 Department of Demography, University of California Berkeley, US; Departamento de Ecolog\u0131a, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Chile; Instituto de Ecolog\u00b4\u0131a y Biodiversidad (IEB), Chile; The Santa Fe Institute, Santa Fe, New Mexico, US; Instituto de Sistema Complejos de Valpara\u0131so (ISCV), Valpara\u0131so, Chile; Centro de Cambio Global UC, Pontificia Universidad Catolica de Chile, Santiago, Chile; Computational Health Informatics Program, Boston Children\u2019s Hospital, US; Department of Pediatrics, Harvard Medical School, US.<\/p>\n Resumen:<\/strong> The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality \u2013measured either by direct COVID-19 attributed deaths or excess deaths\u2013 and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n <\/p>\n Modera: Mauricio Lima \/ Eduardo Undurraga Pontificia Universidad Cat\u00f3lica de Chile<\/p>\n Expositora: Alejandro Bernardin <\/strong>(abernardin@dlab.cl<\/a>), Computational Biology Lab, Fundaci\u00f3n Ciencia & Vida<\/p>\n Co-autores:<\/strong> Alejandro Martinez, Tomas Perez-Acle. Computational Biology Lab, Fundaci\u00f3n Ciencia & Vida; Centro Interdisciplinario de Neurociencia de Valpara\u00edso, Universidad de Valpara\u00edso; Facultad de Ingenier\u00eda y Tecnolog\u00eda, Universidad San Sebasti\u00e1n<\/p>\n Resumen:<\/strong> In this work we show how communication strategies, both global and local, can affect the spread of an infectious disease. To do so, we implemented an agent-based simulation coupling infectious and information dynamics. As a result, we show the importance of informing the population on a daily basis highlighting the role that communication between people plays during the pandemic.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor: Samuel Ropert <\/strong>(sropert@dlab.cl<\/a>), Computational Biology Lab, Fundaci\u00f3n Ciencia & Vida<\/p>\n Co-autores: <\/strong>Alejandro Bernardin, <\/strong>Tomas Perez-Acle. Computational Biology Lab, Fundaci\u00f3n Ciencia & Vida; Centro Interdisciplinario de Neurociencia de Valpara\u00edso, Universidad de Valpara\u00edso; Facultad de Ingenier\u00eda y Tecnolog\u00eda, Universidad San Sebasti\u00e1n<\/p>\n Resumen:<\/strong> In this work we studied the influence of non-pharmaceutical interventions relying on rapid testing and quarantine, on the dynamics of the COVID-19 pandemic. To do so, we developed a set of SEIR-TQ\u00a0computational models using both ODEs and an agent-based model.\u00a0Our\u00a0models suggest that iterative testing over 10% of the population could\u00a0effectively suppress the spread of COVID-19. Moreover, using this strategy,\u00a0a drastic reduction in the number of\u00a0superspreaders can also be achieved. The importante of the delay between testing and quarantine is strongly enforced.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositora: Ignacio Acevedo <\/strong>(iacevedo@dim.uchile.cl<\/a>), DIM\/CMM Universidad de Chile<\/p>\n Co-autores: <\/strong>Javier Monreal, Alejandro Maass, CMM Universidad de Chile, Mauricio Canals, Escuela de Salud P\u00fablica, Universidad de Chile<\/p>\n Resumen<\/strong>:<\/strong> Estudiamos y analizamos el grado de complejidad de la din\u00e1mica generada por la actual pandemia del COVID19. Se emplean diferentes m\u00e9todos no-lineales para caracterizar el atractor y la caoticidad del sistema, obteniendo interesantes resultados para distintos grupos de poblaciones.<\/p>\n [divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositora: Pamela Martinez <\/strong>(pamelapm@illinois.edu<\/a>), University of Illinois at Urbana Champaign, Illinois, US; Harvard T.H. Chan School of Public Health<\/p>\n Co-autores: <\/strong>Rebecca Smith, Christopher Brooke, University of Illinois at Urbana Champaign<\/p>\n Resumen<\/strong>:<\/strong> Entender la din\u00e1mica viral de SARS-CoV-2 dentro del hu\u00e9sped es fundamental para poder evaluar diferentes m\u00e9todos de detecci\u00f3n del virus. La Universidad de Illinois ha implementado un estudio longitudinal para poder evaluar la respuesta de tres tipos de tests en el tiempo: PCR nasal, PCR de saliva, y tests antig\u00e9nicos. Mostrare resultados preliminares de este estudio y las consecuencias para una detecci\u00f3n masiva y temprana de SARS-CoV-2.<\/p>\n [su_divider top=\u00bbno\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb1″ margin=\u00bb15″]<\/p>\n Expositor: Fernando Cordova <\/strong>(fcordovalepe@gmail.com<\/a>), Universidad Cat\u00f3lica del Maule<\/p>\n Resumen<\/strong> A un modelo por compartimentos tipo SEIR se le suma una ecuaci\u00f3n para la tasa de contagio. Algunos patrones de primeras olas, mesetas y segunda o terceras olas en la curva de activos son observandos. Adem\u00e1s, se presenta una hiper sensibilidad a ciertos par\u00e1metros sociales.<\/strong><\/p>\n [su_divider top=\u00bbyes\u00bb text=\u00bbIr arriba\u00bb anchor=\u00bb#\u00bb style=\u00bbdefault\u00bb divider_color=\u00bb#d4686c\u00bb link_color=\u00bb#555″ size=\u00bb3″ margin=\u00bb15″]<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Bloque 1 Viernes 29 enero 2020 (9:15 \u2013 11:45am) Modera: Pedro Gajardo \/ Fernando Mardones, Universidad T\u00e9cnica Federico Santa Mar\u00eda \/ Pontificia Universidad Cat\u00f3lica de Chile 9:15 \u2013 9:45 Forecasting COVID-19 Chile\u2019s second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate Expositor: Patricio Cumsille (pcumsille@ubiobio.cl) Facultad de Ciencias, … Seguir leyendo Res\u00famenes<\/span> 9:45 \u2013 10:15 \u00b7 Desarrollo de un modelo lagrangeano de tiempos de residencia y riesgos en ambientes y clases para covid19 en la RM con informaci\u00f3n EOD<\/h3>\n
10:15 \u2013 10:45 \u00b7 Una estimaci\u00f3n de la seroprevalencia del Covid-19 en Chile<\/h3>\n
10:45 \u2013 11:15 \u00b7 Assessing the short-run effects of lockdown policies on economic activity, with an application to the Santiago Metropolitan Region, Chile<\/h3>\n
11:15 \u2013 11:45 \u00b7 Sobre la asociaci\u00f3n entre nivel socioecon\u00f3mico, edad y letalidad en el Gran Santiago<\/h3>\n
Bloque 2<\/h2>\n
Viernes 29 enero 2020 (12:00 \u2013 14:30)<\/h2>\n
12:00 \u2013 12:30 \u00b7 On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics<\/h3>\n
12:30 \u2013 13:00 \u00b7Assessing the effects of rapid testing and quarantine on the dynamics of the COVID-19 pandemic<\/h3>\n
13:00 \u2013 13:30 \u00b7 An\u00e1lisis no-lineal de la din\u00e1mica del SARS-CoV-2<\/h3>\n
13:30 \u2013 14:00 \u00b7 Iniciativa SHIELD y la dinamica viral de SARS-CoV-2<\/h3>\n
14:00 \u2013 14:30 \u00b7 Sobre una ley din\u00e1mica para la tasa de contagio<\/h3>\n