Calculating confidence intervals of the cell-culture-positivity curve (in Fig 3C)

RK Ruian Ke
PM Pamela P. Martinez
RS Rebecca L. Smith
LG Laura L. Gibson
AM Agha Mirza
MC Madison Conte
NG Nicholas Gallagher
CL Chun Huai Luo
JJ Junko Jarrett
RZ Ruifeng Zhou
AC Abigail Conte
TL Tongyu Liu
MF Mireille Farjo
KW Kimberly K.O. Walden
GR Gloria Rendon
CF Christopher J. Fields
LW Leyi Wang
RF Richard Fredrickson
DE Darci C. Edmonson
MB Melinda E. Baughman
KC Karen K. Chiu
HC Hannah Choi
KS Kevin R. Scardina
SB Shannon Bradley
SG Stacy L. Gloss
CR Crystal Reinhart
JY Jagadeesh Yedetore
JQ Jessica Quicksall
AO Alyssa N. Owens
JB John Broach
BB Bruce Barton
PL Peter Lazar
WH William J. Heetderks
MR Matthew L. Robinson
HM Heba H. Mostafa
YM Yukari C. Manabe
AP Andrew Pekosz
DM David D. McManus
CB Christopher B. Brooke
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Similar as the procedures performed for predicting the confidence intervals of viral genome load trajectories, we randomly sampled 5000 sets of parameter combinations from the distribution specified by the best-fit population parameters of the best model, i.e. the saturation model assuming Km only has a fixed effect (Table S8). More specifically, we sampled parameters from a log-normal distribution for J and h, with their means and standard deviations at the best-fit values. Using the parameter combinations, we generated curves of probability of cell-culture positivity at CN values ranging between 10 and 40. The median and the 5th and 95th quantile of viral genome loads at each CN values are reported.

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