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10.1111/tbed.13738 http://scihub22266oqcxt.onion/10.1111/tbed.13738 32683774!7405088!32683774     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32683774&cmd=llinks): Failed to open stream: HTTP request failed! 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Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence |pdf=|usr=}}{{32683774}} gab.com Text Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence JO: Transbound Emerg Dis http://www.kidney.de/pget.php?&tw=1&pmid=32683774 #FOAvip Event-based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID-19) pandemic. A retrospective study was conducted to evaluate the capacity of three event-based surveillance (EBS) systems (ProMED, HealthMap and PADI-web) to detect early COVID-19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID-19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to 'pneumonia symptoms' and 'mystery illness'. Once COVID-19 was identified, the vocabulary changed to virus family and specific COVID-19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks. Twit Text FOAVip Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence ????Transbound Emerg Dis http://www.kidney.de/pget.php?&tw=1&pmid=32683774 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32683774 #FOAvip English Wikipedia <ref>{{Cite pmid|32683774}}</ref> Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence #MMPMID32683774 Valentin S; Mercier A; Lancelot R; Roche M; Arsevska E Transbound Emerg Dis 2021[May]; 68 (3): 981-986 PMID32683774show ga Event-based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID-19) pandemic. A retrospective study was conducted to evaluate the capacity of three event-based surveillance (EBS) systems (ProMED, HealthMap and PADI-web) to detect early COVID-19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID-19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to 'pneumonia symptoms' and 'mystery illness'. Once COVID-19 was identified, the vocabulary changed to virus family and specific COVID-19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks. |*SARS-CoV-2[MESH] |Animals[MESH] |COVID-19/*diagnosis/*epidemiology[MESH] |China/epidemiology[MESH] |Communicable Diseases, Emerging/*diagnosis/*epidemiology[MESH] |Humans[MESH] |Population Surveillance[MESH]Monitoring online media reports for early detection of unknown disease(epmc).pdf DeepDyve Pubget Overpricing