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10.1063/5.0015303 http://scihub22266oqcxt.onion/10.1063/5.0015303 33304642!7709495!33304642     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       AIP+Adv 2020 ; 10 (11): 115023 Nephropedia Template TP {{tp|p=33304642|t=2020. Pandemic infection rates are deterministic but cannot be modeled |pdf=|usr=}}{{33304642}} gab.com Text Pandemic infection rates are deterministic but cannot be modeled JO: AIP Adv http://www.kidney.de/pget.php?&tw=1&pmid=33304642 #FOAvip The covid-19 infection rates for a large number of infections collected from a large number of different sites are well defined with a negligible scatter. The simplest invertible iterated map, exponential growth and decay, emerges from country-wide histograms whenever Tchebychev's inequality is satisfied to within several decimal places. This is one point. Another is that failed covid-19 pandemic model predictions have been reported repeatedly by the news media. Model predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections. When those possibilities are ruled out, then little is left. Under lockdown and social distancing, the rates unfold daily in small but unforeseeable steps, they are algorithmically complex. We can, however, use two days in the daily data, today and any single day in the past (generally yesterday), to make a useful forecast of future infections. No model provides results better than this simple forecast. We analyze the actual doubling times for covid-19 data and compare them with our predicted doubling times. Flattening and peaking are precisely defined. We identify and study the separate effects of social distancing vs recoveries in the daily infection rates. Social distancing can only cause flattening but recoveries are required in order for the active infections to peak and decay. Three models and their predictions are analyzed. Pandemic data for Austria, Germany, Italy, the USA, the UK, Finland, China, Taiwan, and Sweden are discussed. Twit Text FOAVip Pandemic infection rates are deterministic but cannot be modeled ????AIP Adv http://www.kidney.de/pget.php?&tw=1&pmid=33304642 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33304642 #FOAvip English Wikipedia <ref>{{Cite pmid|33304642}}</ref> Pandemic infection rates are deterministic but cannot be modeled #MMPMID33304642 McCauley JL AIP Adv 2020[Nov]; 10 (11): 115023 PMID33304642show ga The covid-19 infection rates for a large number of infections collected from a large number of different sites are well defined with a negligible scatter. The simplest invertible iterated map, exponential growth and decay, emerges from country-wide histograms whenever Tchebychev's inequality is satisfied to within several decimal places. This is one point. Another is that failed covid-19 pandemic model predictions have been reported repeatedly by the news media. Model predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections. When those possibilities are ruled out, then little is left. Under lockdown and social distancing, the rates unfold daily in small but unforeseeable steps, they are algorithmically complex. We can, however, use two days in the daily data, today and any single day in the past (generally yesterday), to make a useful forecast of future infections. No model provides results better than this simple forecast. We analyze the actual doubling times for covid-19 data and compare them with our predicted doubling times. Flattening and peaking are precisely defined. We identify and study the separate effects of social distancing vs recoveries in the daily infection rates. Social distancing can only cause flattening but recoveries are required in order for the active infections to peak and decay. Three models and their predictions are analyzed. Pandemic data for Austria, Germany, Italy, the USA, the UK, Finland, China, Taiwan, and Sweden are discussed. äPandemic infection rates are deterministic but cannot be modeled (epmc).pdf DeepDyve Pubget Overpricing