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10.5414/CP203824 http://scihub22266oqcxt.onion/10.5414/CP203824 32515729!ä!32515729 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 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 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 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 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 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Clin+Pharmacol+Ther 2020 ; 58 (7): 366-374 Nephropedia Template TP {{tp|p=32515729|t=2020. Evaluation of the COVID-19 pandemic using an algorithm based on the Bateman function: Prediction of disease progression using observational data for the city of Heidelberg, Germany? |pdf=|usr=}}{{32515729}} gab.com Text Evaluation of the COVID-19 pandemic using an algorithm based on the Bateman function: Prediction of disease progression using observational data for the city of Heidelberg, Germany JO: Int J Clin Pharmacol Ther http://www.kidney.de/pget.php?&tw=1&pmid=32515729 #FOAvip The COVID-19 pandemic has been evaluated using an algorithm based on the Bateman function in a modified SIR/SIZ-Model. Prediction of the number of persons carrying the live COVID-19 coronavirus (I) in a susceptible population (S) was achieved using two rate constants describing the rate of increase and decrease in the number of infectious persons on a daily basis. The model was verified using observational data for the city of Heidelberg, Germany. Three hypothetical scenarios, having their counterparts in practice were considered, namely Scenario A - No restrictions on the population; Scenario B - Assumption of a 10-fold higher number of infections than observed; Scenario C - Protective measures introduced only for elderly persons. It could be demonstrated using the model that the lockdown measures introduced prevented a major medical emergency and possibly a near catastrophe in the region. It was further demonstrated that the prospective application of the model can facilitate realtime decisions on pandemic management strategy for the population. This is achieved by curve-fitting for the rate constants, determinants for the number of infectious persons. The calculated maximum numbers of infected and infectious persons daily increased in proportion to the number of persons initially susceptible to the infection. After appearance of the first two infections in Heidelberg, the calculated maximum number of persons carrying live virus was 2,291 at Day 102 (Scenario B), 18,936 infectious persons at Day 139 (Scenario C) and 22,535 infectious at Day 142 (Scenario A). In Scenario A, high values would have persisted for 6 months during which a total of 124,301 persons would have been infected in Heidelberg. The model predicted that the virus would have disappeared within 1 year after being first detected. A disease catastrophe of this magnitude would not be expected provided the rate constant (alpha) for the rate of increase in the number of infectious persons remained lower than the rate constant (beta) for the fall in number of infectious persons. Twit Text FOAVip Evaluation of the COVID-19 pandemic using an algorithm based on the Bateman function: Prediction of disease progression using observational data for the city of Heidelberg, Germany ????Int J Clin Pharmacol Ther http://www.kidney.de/pget.php?&tw=1&pmid=32515729 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32515729 #FOAvip English Wikipedia <ref>{{Cite pmid|32515729}}</ref> Evaluation of the COVID-19 pandemic using an algorithm based on the Bateman function: Prediction of disease progression using observational data for the city of Heidelberg, Germany? #MMPMID32515729 Merle U; Lassmann A; Dressel AR; Braun P Int J Clin Pharmacol Ther 2020[Jul]; 58 (7): 366-374 PMID32515729show ga The COVID-19 pandemic has been evaluated using an algorithm based on the Bateman function in a modified SIR/SIZ-Model. Prediction of the number of persons carrying the live COVID-19 coronavirus (I) in a susceptible population (S) was achieved using two rate constants describing the rate of increase and decrease in the number of infectious persons on a daily basis. The model was verified using observational data for the city of Heidelberg, Germany. Three hypothetical scenarios, having their counterparts in practice were considered, namely Scenario A - No restrictions on the population; Scenario B - Assumption of a 10-fold higher number of infections than observed; Scenario C - Protective measures introduced only for elderly persons. It could be demonstrated using the model that the lockdown measures introduced prevented a major medical emergency and possibly a near catastrophe in the region. It was further demonstrated that the prospective application of the model can facilitate realtime decisions on pandemic management strategy for the population. This is achieved by curve-fitting for the rate constants, determinants for the number of infectious persons. The calculated maximum numbers of infected and infectious persons daily increased in proportion to the number of persons initially susceptible to the infection. After appearance of the first two infections in Heidelberg, the calculated maximum number of persons carrying live virus was 2,291 at Day 102 (Scenario B), 18,936 infectious persons at Day 139 (Scenario C) and 22,535 infectious at Day 142 (Scenario A). In Scenario A, high values would have persisted for 6 months during which a total of 124,301 persons would have been infected in Heidelberg. The model predicted that the virus would have disappeared within 1 year after being first detected. A disease catastrophe of this magnitude would not be expected provided the rate constant (alpha) for the rate of increase in the number of infectious persons remained lower than the rate constant (beta) for the fall in number of infectious persons. |*Algorithms[MESH] |*Models, Statistical[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Disease Progression[MESH] |Forecasting[MESH] |Germany/epidemiology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Prospective Studies[MESH]Evaluation of the COVID-19 pandemic using an algorithm based on the Ba(epmc).pdf DeepDyve Pubget Overpricing