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10.1063/5.0008834 http://scihub22266oqcxt.onion/10.1063/5.0008834 32491888!7241685!32491888     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos 2020 ; 30 (5): 051107 Nephropedia Template TP {{tp|p=32491888|t=2020. Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation |pdf=|usr=}}{{32491888}} gab.com Text Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation JO: Chaos http://www.kidney.de/pget.php?&tw=1&pmid=32491888 #FOAvip Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics. Twit Text FOAVip Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation ????Chaos http://www.kidney.de/pget.php?&tw=1&pmid=32491888 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32491888 #FOAvip English Wikipedia <ref>{{Cite pmid|32491888}}</ref> Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation #MMPMID32491888 Faranda D; Castillo IP; Hulme O; Jezequel A; Lamb JSW; Sato Y; Thompson EL Chaos 2020[May]; 30 (5): 051107 PMID32491888show ga Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics. |*Stochastic Processes[MESH] |Asymptomatic Infections/*epidemiology[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Coronavirus Infections/*epidemiology/*virology[MESH] |Global Health[MESH] |Humans[MESH] |Models, Statistical[MESH] |Nonlinear Dynamics[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/*virology[MESH]Asymptotic estimates of SARS-CoV-2 infection counts and their sensitiv(epmc).pdf DeepDyve Pubget Overpricing