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10.1073/pnas.2010398117 http://scihub22266oqcxt.onion/10.1073/pnas.2010398117 32839315!7502715!32839315     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (37): 22684-22689 Nephropedia Template TP {{tp|p=32839315|t=2020. A network-based explanation of why most COVID-19 infection curves are linear |pdf=|usr=}}{{32839315}} gab.com Text A network-based explanation of why most COVID-19 infection curves are linear JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32839315 #FOAvip Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, [Formula: see text], below which linear growth and low infection prevalence must occur. Above [Formula: see text] traditional epidemiological dynamics take place, e.g., as in susceptible-infected-recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find [Formula: see text] Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero. Twit Text FOAVip A network-based explanation of why most COVID-19 infection curves are linear ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32839315 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32839315 #FOAvip English Wikipedia <ref>{{Cite pmid|32839315}}</ref> A network-based explanation of why most COVID-19 infection curves are linear #MMPMID32839315 Thurner S; Klimek P; Hanel R Proc Natl Acad Sci U S A 2020[Sep]; 117 (37): 22684-22689 PMID32839315show ga Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, [Formula: see text], below which linear growth and low infection prevalence must occur. Above [Formula: see text] traditional epidemiological dynamics take place, e.g., as in susceptible-infected-recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find [Formula: see text] Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero. |*Models, Statistical[MESH] |Basic Reproduction Number[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Humans[MESH] |Pandemics/prevention & control[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH]A network-based explanation of why most COVID-19 infection curves are (epmc).pdf DeepDyve Pubget Overpricing