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10.1007/s11538-020-00791-2 http://scihub22266oqcxt.onion/10.1007/s11538-020-00791-2 32816135!7439250!32816135     free     free     free 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       Bull+Math+Biol 2020 ; 82 (9): 114 Nephropedia Template TP {{tp|p=32816135|t=2020. Effects of Latency on Estimates of the COVID-19 Replication Number |pdf=|usr=}}{{32816135}} gab.com Text Effects of Latency on Estimates of the COVID-19 Replication Number JO: Bull Math Biol http://www.kidney.de/pget.php?&tw=1&pmid=32816135 #FOAvip There is continued uncertainty in how long it takes a person infected by the COVID-19 virus to become infectious. In this paper, we quantify how this uncertainty affects estimates of the basic replication number [Formula: see text], and thus estimates of the fraction of the population that would become infected in the absence of effective interventions. The analysis is general, and applies to all SEIR-based models, not only those associated with COVID-19. We find that when modeling a rapidly spreading epidemic, seemingly minor differences in how latency is treated can lead to vastly different estimates of [Formula: see text]. We also derive a simple formula relating the replication number to the fraction of the population that is eventually infected. This formula is robust and applies to all compartmental models whose parameters do not depend on time. Twit Text FOAVip Effects of Latency on Estimates of the COVID-19 Replication Number ????Bull Math Biol http://www.kidney.de/pget.php?&tw=1&pmid=32816135 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32816135 #FOAvip English Wikipedia <ref>{{Cite pmid|32816135}}</ref> Effects of Latency on Estimates of the COVID-19 Replication Number #MMPMID32816135 Sadun L Bull Math Biol 2020[Aug]; 82 (9): 114 PMID32816135show ga There is continued uncertainty in how long it takes a person infected by the COVID-19 virus to become infectious. In this paper, we quantify how this uncertainty affects estimates of the basic replication number [Formula: see text], and thus estimates of the fraction of the population that would become infected in the absence of effective interventions. The analysis is general, and applies to all SEIR-based models, not only those associated with COVID-19. We find that when modeling a rapidly spreading epidemic, seemingly minor differences in how latency is treated can lead to vastly different estimates of [Formula: see text]. We also derive a simple formula relating the replication number to the fraction of the population that is eventually infected. This formula is robust and applies to all compartmental models whose parameters do not depend on time. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |Asymptomatic Infections/epidemiology[MESH] |Basic Reproduction Number/*statistics & numerical data[MESH] |COVID-19[MESH] |Coronavirus Infections/epidemiology/prevention & control/*transmission[MESH] |Humans[MESH] |Immunity, Herd[MESH] |Mathematical Concepts[MESH] |Pandemics/prevention & control/statistics & numerical data[MESH] |Pneumonia, Viral/epidemiology/prevention & control/*transmission[MESH] |SARS-CoV-2[MESH] |Time Factors[MESH]Effects of Latency on Estimates of the COVID-19 Replication Number (epmc).pdf DeepDyve Pubget Overpricing