Calculate probability of detecting a variant given a per-timestep sample size assuming periodic sampling

This function calculates the probability of detecting the presence of a variant given a sample size and either a desired maximum time until detection or a desired prevalence by which to detect the variant by. It assumes a periodic sampling strategy, where samples are collected at regular intervals (time steps).

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

Arguments

n

per-timestep (e.g., per day) sample size

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)

Value

scalar of detection probability

See also

Other variant detection functions: vartrack_prob_detect_xsect(), vartrack_prob_detect(), vartrack_samplesize_detect_cont(), vartrack_samplesize_detect_xsect(), vartrack_samplesize_detect()

Other variant tracking functions: vartrack_cod_ratio(), vartrack_prob_detect_xsect(), vartrack_prob_detect(), vartrack_prob_prev_xsect(), vartrack_prob_prev(), vartrack_samplesize_detect_cont(), vartrack_samplesize_detect_xsect(), vartrack_samplesize_detect(), vartrack_samplesize_prev_xsect(), vartrack_samplesize_prev()

Author

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

Examples

vartrack_prob_detect_cont(n = 158, t = 30, omega = 0.8, p0_v1 = 1/10000, r_v1 = 0.1, c_ratio = 1)
#> [1] 0.9101948