About: Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks
Creator	Chen, Bin Gu, Hua Jiang, Jianmin Jiang, Zhenggang Wang, Xinyi Chen, Lei Jiang, Tao Zheng, Dawei Timpka, Toomas Eysenbach, Gunther Zhu, Honghong Freifeld, Clark Leal Neto, Onicio
Source	Medline; PMC
abstract	BACKGROUND: Outbreaks of human infection with a new avian influenza A H7N9 virus occurred in China in the spring of 2013. Control and prevention of a new human infectious disease outbreak can be strongly affected by public reaction and social impact through the Internet and social media. OBJECTIVE: This study aimed to investigate the potential roles of Internet surveillance in control and prevention of the human H7N9 outbreaks. METHODS: Official data for the human H7N9 outbreaks were collected via the China National Health and Family Planning Committee website from March 31 to April 24, 2013. We obtained daily posted and forwarded number of blogs for the keyword “H7N9” from Sina microblog website and a daily Baidu Attention Index (BAI) from Baidu website, which reflected public attention to the outbreak. Rumors identified and confirmed by the authorities were collected from Baidu search engine. RESULTS: Both daily posted and forwarded number and BAI for keyword H7N9 increased quickly during the first 3 days of the outbreaks and remained at a high level for 5 days. The total daily posted and forwarded number for H7N9 on Sina microblog peaked at 850,000 on April 3, from zero blogs before March 31, increasing to 97,726 on April 1 and to 370,607 on April 2, and remaining above 500,000 from April 5-8 before declining to 208,524 on April 12. The total daily BAI showed a similar pattern of change to the total daily posted and forwarded number over time from March 31 to April 12. When the outbreak locations spread, especially into other areas of the same province/city and the capital, Beijing, daily posted and forwarded number and BAI increased again to a peak at 368,500 and 116,911, respectively. The median daily BAI during the studied 25 days was significantly higher among the 7 provinces/cities with reported human H7N9 cases than the 2 provinces without any cases (P<.001). So were the median daily posted and forwarded number and daily BAI in each province/city except Anhui province. We retrieved a total of 32 confirmed rumors spread across 19 provinces/cities in China. In all, 84% (27/32) of rumors were disseminated and transmitted by social media. CONCLUSIONS: The first 3 days of an epidemic is a critical period for the authorities to take appropriate action through Internet surveillance to prevent and control the epidemic, including preparation of personnel, technology, and other resources; information release; collection of public opinion and reaction; and clarification, prevention, and control of rumors. Internet surveillance can be used as an efficient and economical tool to prevent and control public health emergencies, such as H7N9 outbreaks.
has issue date	2014-01-17(xsd:dateTime)
bibo:doi	10.2196/jmir.2911
bibo:pmid	24440770
has license	cc-by
schema:url	https://doi.org/10.2196/jmir.2911
resource representing a document's title	Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks
has PubMed Central identifier	PMC3906895
has PubMed identifier	24440770
schema:publication	J Med Internet Res
resource representing a document's body	covid:PMC3906895#body_text
is schema:about of	named entity 'Outbreaks' named entity 'Public Health Emergency' named entity 'PREVENTION' named entity 'EVIDENCE' named entity 'EMERGENCY' named entity 'AVIAN INFLUENZA A' named entity 'H7N9' named entity 'PUBLIC HEALTH' named entity 'EPIDEMIOLOGIC STUDY' named entity 'SURVEILLANCE' named entity 'OUTBREAKS' named entity 'CONTROL' named entity 'DIGITAL' named entity 'INTERNET' named entity 'H7N9' named entity 'Avian Influenza A H7N9' named entity 'public health emergency' named entity 'epidemic' named entity 'Figure 8' named entity 'correlation coefficient' named entity 'June 30, 2012' named entity 'influenza' named entity 'Zhejiang' named entity 'Internet surveillance' named entity 'respiratory symptoms' named entity 'Eastern China' named entity 'epidemic' named entity 'case fatality rate' named entity 'April 17' named entity 'Avian Influenza A H7N9' named entity 'H7N9' named entity 'cough' named entity 'communication software' named entity 'chicken feet' named entity 'virus' named entity 'human infection' named entity 'H7N9' named entity 'Beijing' named entity 'China' named entity 'provinces in China' named entity 'H7N9' named entity 'H7N9' named entity 'Shanghai' named entity 'avian influenza' named entity 'Internet' named entity 'Shandong' named entity 'search engine' named entity 'provinces in China' named entity 'Jiangsu' named entity 'Internet' named entity 'Beijing' named entity 'pickled' named entity 'public health emergency' named entity 'Internet' named entity 'Internet surveillance' named entity 'China' named entity 'epidemic' named entity 'Beijing' named entity 'Shaanxi' named entity 'epidemic' named entity 'April 13' named entity 'Sina microblog' named entity 'April 12' named entity 'H7N9' named entity 'Internet' named entity 'study population' named entity 'March 31' named entity 'Henan' named entity 'Beijing' named entity 'H1N1'

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