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10.3760/cma.j.cn112338-20200216-00107 http://scihub22266oqcxt.onion/10.3760/cma.j.cn112338-20200216-00107 32129581!ä!32129581 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Zhonghua+Liu+Xing+Bing+Xue+Za+Zhi 2020 ; 41 (4): 480-484 Nephropedia Template TP {{tp|p=32129581|t=2020. Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak |pdf=|usr=}}{{32129581}} gab.com Text Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak JO: Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32129581 #FOAvip Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes. Twit Text FOAVip Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak ????Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32129581 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32129581 #FOAvip English Wikipedia <ref>{{Cite pmid|32129581}}</ref> Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak #MMPMID32129581 Tang SY; Xiao YN; Peng ZH; Shen HB Zhonghua Liu Xing Bing Xue Za Zhi 2020[Apr]; 41 (4): 480-484 PMID32129581show ga Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes. |*Models, Statistical[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*epidemiology/*prevention & control/transmission[MESH] |Humans[MESH] |Pandemics/*prevention & control[MESH] |Pneumonia, Viral/*epidemiology/*prevention & control/transmission[MESH] |Risk Assessment[MESH] Prediction modeling with data fusion and prevention strategy analysis(epmc).pdf DeepDyve Pubget Overpricing