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10.1017/ice.2020.464 http://scihub22266oqcxt.onion/10.1017/ice.2020.464 32928337!8160497!32928337     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Infect+Control+Hosp+Epidemiol 2021 ; 42 (6): 653-658 Nephropedia Template TP {{tp|p=32928337|t=2021. Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity |pdf=|usr=}}{{32928337}} gab.com Text Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity JO: Infect Control Hosp Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32928337 #FOAvip BACKGROUND: The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. OBJECTIVE: We describe methods used by a university hospital to forecast case loads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty. Twit Text FOAVip Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity ????Infect Control Hosp Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32928337 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32928337 #FOAvip English Wikipedia <ref>{{Cite pmid|32928337}}</ref> Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity #MMPMID32928337 Donker T; Burkin FM; Wolkewitz M; Haverkamp C; Christoffel D; Kappert O; Hammer T; Busch HJ; Biever P; Kalbhenn J; Burkle H; Kern WV; Wenz F; Grundmann H Infect Control Hosp Epidemiol 2021[Jun]; 42 (6): 653-658 PMID32928337show ga BACKGROUND: The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. OBJECTIVE: We describe methods used by a university hospital to forecast case loads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty. |*Hospital Bed Capacity[MESH] |COVID-19/*epidemiology/prevention & control[MESH] |Cross Infection/prevention & control[MESH] |Forecasting[MESH] |Germany/epidemiology[MESH] |Hospitals, University/*organization & administration/statistics & numerical data[MESH] |Humans[MESH] |Incidence[MESH] |Models, Statistical[MESH]Navigating hospitals safely through the COVID-19 epidemic tide: Predic(epmc).pdf DeepDyve Pubget Overpricing