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10.1088/1478-3975/ab9e59 http://scihub22266oqcxt.onion/10.1088/1478-3975/ab9e59 32554901!8849595!32554901     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Biol 2020 ; 17 (6): 065003 Nephropedia Template TP {{tp|p=32554901|t=2020. Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |pdf=|usr=}}{{32554901}} gab.com Text Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them JO: Phys Biol http://www.kidney.de/pget.php?&tw=1&pmid=32554901 #FOAvip Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the 'CFR' has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual's probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations. Twit Text FOAVip Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them ????Phys Biol http://www.kidney.de/pget.php?&tw=1&pmid=32554901 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32554901 #FOAvip English Wikipedia <ref>{{Cite pmid|32554901}}</ref> Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them #MMPMID32554901 Bottcher L; Xia M; Chou T Phys Biol 2020[Sep]; 17 (6): 065003 PMID32554901show ga Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the 'CFR' has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual's probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations. |COVID-19/*mortality[MESH] |Humans[MESH] |Models, Statistical[MESH] |Pandemics[MESH] |Probability[MESH] |SARS-CoV-2/isolation & purification[MESH]Why case fatality ratios can be misleading: individual- and population(epmc).pdf DeepDyve Pubget Overpricing