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10.1073/pnas.2011548118 http://scihub22266oqcxt.onion/10.1073/pnas.2011548118 33361331!7812760!33361331     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2021 ; 118 (2): ä Nephropedia Template TP {{tp|p=33361331|t=2021. Forward-looking serial intervals correctly link epidemic growth to reproduction numbers |pdf=|usr=}}{{33361331}} gab.com Text Forward-looking serial intervals correctly link epidemic growth to reproduction numbers JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33361331 #FOAvip The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19. Twit Text FOAVip Forward-looking serial intervals correctly link epidemic growth to reproduction numbers ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33361331 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33361331 #FOAvip English Wikipedia <ref>{{Cite pmid|33361331}}</ref> Forward-looking serial intervals correctly link epidemic growth to reproduction numbers #MMPMID33361331 Park SW; Sun K; Champredon D; Li M; Bolker BM; Earn DJD; Weitz JS; Grenfell BT; Dushoff J Proc Natl Acad Sci U S A 2021[Jan]; 118 (2): ä PMID33361331show ga The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19. |*Basic Reproduction Number[MESH] |*Models, Theoretical[MESH] |COVID-19/*epidemiology[MESH] |China/epidemiology[MESH]Forward-looking serial intervals correctly link epidemic growth to rep(epmc).pdf DeepDyve Pubget Overpricing