Modelo de simulación para el cálculo de rutas de menor contagio durante la pandemia de covid19

The pandemic generated by the new SARS VOC-2 virus has spread globally since the beginning of 2020 and has posed a new risk to global health. Currently, according to the World Health Organization, or as WHO is better known, it records a total of 42,906,084 confirmed cases of infection, 31,650,029 ca...

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Autores:
Puentes Ramírez, Miguel ángel
Arias Rivera, Ramón Alejandro
Cortés Vesga, David Alejandro
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2020
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
spa
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/51457
Acceso en línea:
http://hdl.handle.net/1992/51457
Palabra clave:
Movilidad urbana
Redes de sensores
Enfermedades transmisibles
COVID-19 (Enfermedad)
Ingeniería
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description
Summary:The pandemic generated by the new SARS VOC-2 virus has spread globally since the beginning of 2020 and has posed a new risk to global health. Currently, according to the World Health Organization, or as WHO is better known, it records a total of 42,906,084 confirmed cases of infection, 31,650,029 cases of recovery and 1,154,210 deaths globally as of October 24, 2020, and is an active problem until 2021 according to estimates by the organization [9]. Given that in cities like Bogotá there is overcrowding, there is a risk of contagion in the streets due to the crowding that can occur, and therefore a way to intervene and reduce the probability of contagion is to design routes that guide citizens to walk on streets where the probability of contagion is as low as possible. By means of a modeling system with the CupCarbon tool of events for spaces such as cities, a system of wireless sensorial networks is integrated, which are communicated among them, allowing the monitoring of the probabilities of contagion and thus designing the routes that represent the least risk possible.