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Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study

BACKGROUND: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks....

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Autores principales: Sun, Kaiyuan, Chen, Jenny, Viboud, Cécile
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158945/
https://www.ncbi.nlm.nih.gov/pubmed/32309796
http://dx.doi.org/10.1016/S2589-7500(20)30026-1
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author Sun, Kaiyuan
Chen, Jenny
Viboud, Cécile
author_facet Sun, Kaiyuan
Chen, Jenny
Viboud, Cécile
author_sort Sun, Kaiyuan
collection PubMed
description BACKGROUND: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks. METHODS: In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time. FINDINGS: We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020. INTERPRETATION: News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions. FUNDING: Fogarty International Center, US National Institutes of Health.
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spelling pubmed-71589452020-04-15 Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study Sun, Kaiyuan Chen, Jenny Viboud, Cécile Lancet Digit Health Articles BACKGROUND: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks. METHODS: In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time. FINDINGS: We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020. INTERPRETATION: News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions. FUNDING: Fogarty International Center, US National Institutes of Health. The Author(s). Published by Elsevier Ltd. 2020-04 2020-02-20 /pmc/articles/PMC7158945/ /pubmed/32309796 http://dx.doi.org/10.1016/S2589-7500(20)30026-1 Text en © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Articles
Sun, Kaiyuan
Chen, Jenny
Viboud, Cécile
Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title_full Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title_fullStr Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title_full_unstemmed Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title_short Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
title_sort early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158945/
https://www.ncbi.nlm.nih.gov/pubmed/32309796
http://dx.doi.org/10.1016/S2589-7500(20)30026-1
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