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10.1016/j.mbs.2020.108391 http://scihub22266oqcxt.onion/10.1016/j.mbs.2020.108391 32497623!7263299!32497623     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Biosci 2020 ; 326 (ä): 108391 Nephropedia Template TP {{tp|p=32497623|t=2020. A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy |pdf=|usr=}}{{32497623}} gab.com Text A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy JO: Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32497623 #FOAvip The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modeling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures. Twit Text FOAVip A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy ????Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32497623 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32497623 #FOAvip English Wikipedia <ref>{{Cite pmid|32497623}}</ref> A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy #MMPMID32497623 Xue L; Jing S; Miller JC; Sun W; Li H; Estrada-Franco JG; Hyman JM; Zhu H Math Biosci 2020[Aug]; 326 (ä): 108391 PMID32497623show ga The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modeling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |*Pandemics/prevention & control/statistics & numerical data[MESH] |Algorithms[MESH] |Basic Reproduction Number/statistics & numerical data[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Computer Simulation[MESH] |Confidence Intervals[MESH] |Contact Tracing/statistics & numerical data[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Epidemics/prevention & control/statistics & numerical data[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Markov Chains[MESH] |Mathematical Concepts[MESH] |Monte Carlo Method[MESH] |Ontario/epidemiology[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH] |Quarantine/statistics & numerical data[MESH]A data-driven network model for the emerging COVID-19 epidemics in Wuh(epmc).pdf DeepDyve Pubget Overpricing