Mobility in cities of Peru during the COVID-19 pandemic

Introduction: Real-time mobility data from Wuhan, China, and detailed case data, including travel history, was of vital importance for the control of COVID-19, in order to determine the impact of control measures. Objective: to analyze the cases reported in the five most affected regions by COVID-...

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Main Authors: Johnny Leandro Saavedra-Camacho, Sebastian Iglesias-Osores, Miguel Alcántara-Mimbela, Lizbeth Maribel Córdova-Rojas
Format: Article
Language:Spanish
Published: Centro Editorial Ciencias Médicas: CPICM Guantánamo 2021-02-01
Series:Revista Información Científica
Subjects:
Online Access:http://www.revinfcientifica.sld.cu/index.php/ric/article/view/3164
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spelling doaj-d4604f558ffa4eb396cd489c8d9a92f92021-06-02T20:48:41ZspaCentro Editorial Ciencias Médicas: CPICM GuantánamoRevista Información Científica1028-99331028-99332021-02-011001e3164e31642136Mobility in cities of Peru during the COVID-19 pandemicJohnny Leandro Saavedra-Camacho0Sebastian Iglesias-Osores1Miguel Alcántara-Mimbela2Lizbeth Maribel Córdova-Rojas3Universidad Nacional "Pedro Ruiz Gallo"Universidad Nacional "Pedro Ruiz Gallo"Universidad Nacional "Pedro Ruiz Gallo"Universidad Nacional de JaénIntroduction: Real-time mobility data from Wuhan, China, and detailed case data, including travel history, was of vital importance for the control of COVID-19, in order to determine the impact of control measures. Objective: to analyze the cases reported in the five most affected regions by COVID-19 in Peru, and its correlation with mobility data. Method: data of the confirmed cases of COVID-19 obtained from the Centro Nacional de Epidemiologia, Prevención y Control de Enfermedades de Perú (National Center for Epidemiology, Prevention and Control of Diseases of Peru) (https://www.dge.gob.pe/) in the period from 6 From March until August 17, 2020 were included; and the regions with the highest number of cases (CDC-Peru) (Arequipa, Callao, Lima, Lambayeque and Piura) were selected. The mobility data was obtained from the Local Mobility Reports (Community Mobility Reports-Google Mobility Reports) (https://www.google.com/covid19/mobility/) of Peru and downloaded in a CSV file. The categories included in the mobility reports were: retail stores and leisure, public transport stations, workplaces and residential areas. Results: 165 data found in Google Mobility Reports were analyzed; these having a daily data frequency. The same amount of data was obtained from the CDC-Peru. A drop was observed in all places studied except for residential areas in the country. Regarding associations, a negative correlation was found only in residential areas. Conclusion: there was a reduction in mobility due to quarantine, and staying at home is a factor to avoid infections.http://www.revinfcientifica.sld.cu/index.php/ric/article/view/3164informes de movilidadgoogleperútendenciascovid-19
collection DOAJ
language Spanish
format Article
sources DOAJ
author Johnny Leandro Saavedra-Camacho
Sebastian Iglesias-Osores
Miguel Alcántara-Mimbela
Lizbeth Maribel Córdova-Rojas
spellingShingle Johnny Leandro Saavedra-Camacho
Sebastian Iglesias-Osores
Miguel Alcántara-Mimbela
Lizbeth Maribel Córdova-Rojas
Mobility in cities of Peru during the COVID-19 pandemic
Revista Información Científica
informes de movilidad
google
perú
tendencias
covid-19
author_facet Johnny Leandro Saavedra-Camacho
Sebastian Iglesias-Osores
Miguel Alcántara-Mimbela
Lizbeth Maribel Córdova-Rojas
author_sort Johnny Leandro Saavedra-Camacho
title Mobility in cities of Peru during the COVID-19 pandemic
title_short Mobility in cities of Peru during the COVID-19 pandemic
title_full Mobility in cities of Peru during the COVID-19 pandemic
title_fullStr Mobility in cities of Peru during the COVID-19 pandemic
title_full_unstemmed Mobility in cities of Peru during the COVID-19 pandemic
title_sort mobility in cities of peru during the covid-19 pandemic
publisher Centro Editorial Ciencias Médicas: CPICM Guantánamo
series Revista Información Científica
issn 1028-9933
1028-9933
publishDate 2021-02-01
description Introduction: Real-time mobility data from Wuhan, China, and detailed case data, including travel history, was of vital importance for the control of COVID-19, in order to determine the impact of control measures. Objective: to analyze the cases reported in the five most affected regions by COVID-19 in Peru, and its correlation with mobility data. Method: data of the confirmed cases of COVID-19 obtained from the Centro Nacional de Epidemiologia, Prevención y Control de Enfermedades de Perú (National Center for Epidemiology, Prevention and Control of Diseases of Peru) (https://www.dge.gob.pe/) in the period from 6 From March until August 17, 2020 were included; and the regions with the highest number of cases (CDC-Peru) (Arequipa, Callao, Lima, Lambayeque and Piura) were selected. The mobility data was obtained from the Local Mobility Reports (Community Mobility Reports-Google Mobility Reports) (https://www.google.com/covid19/mobility/) of Peru and downloaded in a CSV file. The categories included in the mobility reports were: retail stores and leisure, public transport stations, workplaces and residential areas. Results: 165 data found in Google Mobility Reports were analyzed; these having a daily data frequency. The same amount of data was obtained from the CDC-Peru. A drop was observed in all places studied except for residential areas in the country. Regarding associations, a negative correlation was found only in residential areas. Conclusion: there was a reduction in mobility due to quarantine, and staying at home is a factor to avoid infections.
topic informes de movilidad
google
perú
tendencias
covid-19
url http://www.revinfcientifica.sld.cu/index.php/ric/article/view/3164
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