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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Centro Editorial Ciencias Médicas: CPICM Guantánamo
2021-02-01
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Online Access: | http://www.revinfcientifica.sld.cu/index.php/ric/article/view/3164 |
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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 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 perú tendencias covid-19 |
url |
http://www.revinfcientifica.sld.cu/index.php/ric/article/view/3164 |
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