This function calculates the probability of detecting the presence of a variant given a sample size and sampling strategy.

vartrack_prob_detect(
  n,
  t = NA,
  p_v1 = NA,
  omega,
  p0_v1 = NA,
  r_v1 = NA,
  c_ratio = 1,
  sampling_freq
)

Arguments

n

sample size (either of cross-section or per timestep)

t

time step number (e.g., days) at which variant should be detected by. Default = NA (either 't' or 'p_v1' should be provided, not both)

p_v1

the desired prevalence to detect a variant by. Default = NA (either 't' or 'p_v1' should be provided, not both)

omega

probability of sequencing (or other characterization) success

p0_v1

initial variant prevalence (# introductions / infected population size)

r_v1

logistic growth rate

c_ratio

coefficient of detection ratio, calculated as the ratio of the coefficients of variant 1 to variant 2. Default = 1 (no bias)

sampling_freq

the sampling frequency (must be either 'xsect' or 'cont')

Value

scalar of detection probability

Author

Shirlee Wohl, Elizabeth C. Lee, Bethany L. DiPrete, and Justin Lessler

Examples

# Cross-sectional sampling
vartrack_prob_detect(p_v1 = 0.02, n = 100, omega = 0.8, c_ratio = 1, sampling_freq = 'xsect')
#> Calculating probability of detection assuming single cross-sectional sample
#> [1] 0.8013511

# Periodic sampling
vartrack_prob_detect(n = 158, t = 30, omega = 0.8, p0_v1 = 1/10000, 
r_v1 = 0.1, c_ratio = 1, sampling_freq = 'cont')
#> Calculating probability of detection assuming periodic sampling
#> [1] 0.9101948