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10.21037/jtd.2020.02.64 http://scihub22266oqcxt.onion/10.21037/jtd.2020.02.64 32274081!7139011!32274081     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Thorac+Dis 2020 ; 12 (3): 165-174 Nephropedia Template TP {{tp|p=32274081|t=2020. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions |pdf=|usr=}}{{32274081}} gab.com Text Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions JO: J Thorac Dis http://www.kidney.de/pget.php?&tw=1&pmid=32274081 #FOAvip BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. METHODS: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. RESULTS: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. CONCLUSIONS: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size. Twit Text FOAVip Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions ????J Thorac Dis http://www.kidney.de/pget.php?&tw=1&pmid=32274081 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32274081 #FOAvip English Wikipedia <ref>{{Cite pmid|32274081}}</ref> Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions #MMPMID32274081 Yang Z; Zeng Z; Wang K; Wong SS; Liang W; Zanin M; Liu P; Cao X; Gao Z; Mai Z; Liang J; Liu X; Li S; Li Y; Ye F; Guan W; Yang Y; Li F; Luo S; Xie Y; Liu B; Wang Z; Zhang S; Wang Y; Zhong N; He J J Thorac Dis 2020[Mar]; 12 (3): 165-174 PMID32274081show ga BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. METHODS: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. RESULTS: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. CONCLUSIONS: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size. äModified SEIR and AI prediction of the epidemics trend of COVID-19 in (epmc).pdf DeepDyve Pubget Overpricing