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10.1016/j.ijid.2019.10.011 http://scihub22266oqcxt.onion/10.1016/j.ijid.2019.10.011 31629079!7110621!31629079     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Infect+Dis 2019 ; 89 (ä): 146-153 Nephropedia Template TP {{tp|p=31629079|t=2019. Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?|pdf=|usr=}}{{31629079}} gab.com Text Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=31629079 #FOAvip BACKGROUND: Epidemic intelligence (EI) for emerging infections is the process of identifying key information on emerging infectious diseases and specific incidents. Automated web-based infectious disease surveillance technologies are available; however, human input is still needed to review, validate, and interpret these sources. In this study, entries captured by Public Health England's (PHE) manual event-based EI system were examined to inform future intelligence gathering activities. METHODS: A descriptive analysis of unique events captured in a database between 2013 and 2017 was conducted. The top five diseases in terms of the number of entries were described in depth to determine the effectiveness of PHE's EI surveillance system compared to other sources. RESULTS: Between 2013 and 2017, a total of 22 847 unique entries were added to the database. The top three initial and definitive information sources varied considerably by disease. Ebola entries dominated the database, making up 23.7% of the total, followed by Zika (11.8%), Middle East respiratory syndrome (6.7%), cholera (5.5%), and yellow fever and undiagnosed morbidity (both 3.3%). Initial reports of major outbreaks due to the top five disease agents were picked up through the manual system prior to being publicly reported by official sources. CONCLUSIONS: PHE's manual EI process quickly and accurately detected global public health threats at the earliest stages and allowed for monitoring of events as they evolved. Twit Text FOAVip Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=31629079 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=31629079 #FOAvip English Wikipedia <ref>{{Cite pmid|31629079}}</ref> Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack? #MMPMID31629079 Wilburn J; O'Connor C; Walsh AL; Morgan D Int J Infect Dis 2019[Dec]; 89 (ä): 146-153 PMID31629079show ga BACKGROUND: Epidemic intelligence (EI) for emerging infections is the process of identifying key information on emerging infectious diseases and specific incidents. Automated web-based infectious disease surveillance technologies are available; however, human input is still needed to review, validate, and interpret these sources. In this study, entries captured by Public Health England's (PHE) manual event-based EI system were examined to inform future intelligence gathering activities. METHODS: A descriptive analysis of unique events captured in a database between 2013 and 2017 was conducted. The top five diseases in terms of the number of entries were described in depth to determine the effectiveness of PHE's EI surveillance system compared to other sources. RESULTS: Between 2013 and 2017, a total of 22 847 unique entries were added to the database. The top three initial and definitive information sources varied considerably by disease. Ebola entries dominated the database, making up 23.7% of the total, followed by Zika (11.8%), Middle East respiratory syndrome (6.7%), cholera (5.5%), and yellow fever and undiagnosed morbidity (both 3.3%). Initial reports of major outbreaks due to the top five disease agents were picked up through the manual system prior to being publicly reported by official sources. CONCLUSIONS: PHE's manual EI process quickly and accurately detected global public health threats at the earliest stages and allowed for monitoring of events as they evolved. |Cholera/*epidemiology[MESH] |Communicable Diseases, Emerging/epidemiology[MESH] |Coronavirus Infections/*epidemiology[MESH] |Disease Outbreaks[MESH] |Epidemiological Monitoring[MESH] |Hemorrhagic Fever, Ebola/*epidemiology[MESH] |Humans[MESH] |Intelligence[MESH] |Public Health[MESH] |Yellow Fever/*epidemiology[MESH]Identifying potential emerging threats through epidemic intelligence (epmc).pdf DeepDyve Pubget Overpricing