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10.3934/mbe.2020158 http://scihub22266oqcxt.onion/10.3934/mbe.2020158 32987501!ä!32987501 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32987501&cmd=llinks): Failed to open stream: HTTP request failed! 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Modeling analysis of COVID-19 based on morbidity data in Anhui, China |pdf=|usr=}}{{32987501}} gab.com Text Modeling analysis of COVID-19 based on morbidity data in Anhui, China JO: Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987501 #FOAvip Since the first case of coronavirus disease (COVID-19) in Wuhan Hubei, China, was reported in December 2019, COVID-19 has spread rapidly across the country and overseas. The first case in Anhui, a province of China, was reported on January 10, 2020. In the field of infectious diseases, modeling, evaluating and predicting the rate of disease transmission is very important for epidemic prevention and control. Different intervention measures have been implemented starting from different time nodes in the country and Anhui, the epidemic may be divided into three stages for January 10 to February 11, 2020, namely. We adopted interrupted time series method and develop an SEI/QR model to analyse the data. Our results displayed that the lockdown of Wuhan implemented on January 23, 2020 reduced the contact rate of epidemic transmission in Anhui province by 48.37%, and centralized quarantine management policy for close contacts in Anhui reduced the contact rate by an additional 36.97%. At the same time, the estimated basic reproduction number gradually decreased from the initial 2.9764 to 0.8667 and then to 0.5725. We conclude that the Wuhan lockdown and the centralized quarantine management policy in Anhui played a crucial role in the timely and effective mitigation of the epidemic in Anhui. One merit of this work is the adoption of morbidity data which may reflect the epidemic more accurately and promptly. Our estimated parameters are largely in line with the World Health Organization estimates and previous studies. Twit Text FOAVip Modeling analysis of COVID-19 based on morbidity data in Anhui, China ????Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987501 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32987501 #FOAvip English Wikipedia <ref>{{Cite pmid|32987501}}</ref> Modeling analysis of COVID-19 based on morbidity data in Anhui, China #MMPMID32987501 Tian JJ; Wu JB; Bao YT; Weng XY; Shi L; Liu BB; Yu XY; Qi LX; Liu ZR Math Biosci Eng 2020[Mar]; 17 (4): 2842-2852 PMID32987501show ga Since the first case of coronavirus disease (COVID-19) in Wuhan Hubei, China, was reported in December 2019, COVID-19 has spread rapidly across the country and overseas. The first case in Anhui, a province of China, was reported on January 10, 2020. In the field of infectious diseases, modeling, evaluating and predicting the rate of disease transmission is very important for epidemic prevention and control. Different intervention measures have been implemented starting from different time nodes in the country and Anhui, the epidemic may be divided into three stages for January 10 to February 11, 2020, namely. We adopted interrupted time series method and develop an SEI/QR model to analyse the data. Our results displayed that the lockdown of Wuhan implemented on January 23, 2020 reduced the contact rate of epidemic transmission in Anhui province by 48.37%, and centralized quarantine management policy for close contacts in Anhui reduced the contact rate by an additional 36.97%. At the same time, the estimated basic reproduction number gradually decreased from the initial 2.9764 to 0.8667 and then to 0.5725. We conclude that the Wuhan lockdown and the centralized quarantine management policy in Anhui played a crucial role in the timely and effective mitigation of the epidemic in Anhui. One merit of this work is the adoption of morbidity data which may reflect the epidemic more accurately and promptly. Our estimated parameters are largely in line with the World Health Organization estimates and previous studies. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |*Pandemics/prevention & control/statistics & numerical data[MESH] |Basic Reproduction Number/statistics & numerical data[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Computer Simulation[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Humans[MESH] |Interrupted Time Series Analysis/statistics & numerical data[MESH] |Markov Chains[MESH] |Mathematical Concepts[MESH] |Monte Carlo Method[MESH] |Morbidity/trends[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH] |Quarantine/statistics & numerical data[MESH]Modeling analysis of COVID-19 based on morbidity data in Anhui, China (epmc).pdf DeepDyve Pubget Overpricing