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10.1371/journal.pone.0233146 http://scihub22266oqcxt.onion/10.1371/journal.pone.0233146 32542041!7295185!32542041     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (6): e0233146 Nephropedia Template TP {{tp|p=32542041|t=2020. A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases |pdf=|usr=}}{{32542041}} gab.com Text A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32542041 #FOAvip As the Coronavirus contagion develops, it is increasingly important to understand the dynamics of the disease. Its severity is best described by two parameters: its ability to spread and its lethality. Here, we combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t0). Using the logistic model with specific parameters (L, k and t0), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We were able to predict the CFR much closer to reality at all stages of the viral outbreak compared to traditional methods. This model can be used far more effectively than current models to estimate the CFR during an outbreak, allowing for better planning. The model can also help us better understand the impact of individual interventions on the CFR. With much better data collection and labeling, we should be able to improve our predictive power even further. Twit Text FOAVip A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32542041 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32542041 #FOAvip English Wikipedia <ref>{{Cite pmid|32542041}}</ref> A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases #MMPMID32542041 Narayanan CS PLoS One 2020[]; 15 (6): e0233146 PMID32542041show ga As the Coronavirus contagion develops, it is increasingly important to understand the dynamics of the disease. Its severity is best described by two parameters: its ability to spread and its lethality. Here, we combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t0). Using the logistic model with specific parameters (L, k and t0), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We were able to predict the CFR much closer to reality at all stages of the viral outbreak compared to traditional methods. This model can be used far more effectively than current models to estimate the CFR during an outbreak, allowing for better planning. The model can also help us better understand the impact of individual interventions on the CFR. With much better data collection and labeling, we should be able to improve our predictive power even further. |*Logistic Models[MESH] |*Models, Biological[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Cohort Studies[MESH] |Computer Simulation[MESH] |Coronavirus Infections/epidemiology/*mortality[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/epidemiology/*mortality[MESH]A novel cohort analysis approach to determining the case fatality rate(epmc).pdf DeepDyve Pubget Overpricing