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10.1111/risa.13730 http://scihub22266oqcxt.onion/10.1111/risa.13730 33864640!8251401!33864640     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33864640&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Risk+Anal 2021 ; 41 (11): 2046-2064 Nephropedia Template TP {{tp|p=33864640|t=2021. Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning |pdf=|usr=}}{{33864640}} gab.com Text Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning JO: Risk Anal http://www.kidney.de/pget.php?&tw=1&pmid=33864640 #FOAvip Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first-hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID-19. Twit Text FOAVip Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning ????Risk Anal http://www.kidney.de/pget.php?&tw=1&pmid=33864640 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33864640 #FOAvip English Wikipedia <ref>{{Cite pmid|33864640}}</ref> Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning #MMPMID33864640 Shi X; Li J; Huang A; Song S; Yang Z Risk Anal 2021[Nov]; 41 (11): 2046-2064 PMID33864640show ga Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first-hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID-19. |*Disease Outbreaks[MESH] |*Fuzzy Logic[MESH] |*Public Health Administration[MESH] |Adult[MESH] |COVID-19/*epidemiology[MESH] |China[MESH] |Epidemics[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Principal Component Analysis[MESH] |Public Health/*methods[MESH] |Risk Assessment/*methods[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reason(epmc).pdf DeepDyve Pubget Overpricing