bumblebee (version 0.1.0)

estimate_prob_group_pairing_and_linked: estimate_prob_group_pairing_and_linked Estimates joint probability of linkage

Description

This function computes the joint probability that a pair of pathogen sequences is from a specific population group pairing and linked.

Usage

estimate_prob_group_pairing_and_linked(
  df_counts_and_p_hat,
  individuals_population_in,
  ...
)
# S3 method for default
estimate_prob_group_pairing_and_linked(
  df_counts_and_p_hat,
  individuals_population_in,
  verbose_output = FALSE,
  ...
)

Arguments

df_counts_and_p_hat

A data.frame returned by function: estimate_p_hat()

individuals_population_in

A numeric vector of the estimated number of individuals per population group

…

Further arguments.

verbose_output

A boolean value to display intermediate output. (Default is FALSE)

Value

Returns a data.frame containing:

• H1_group, Name of population group 1

• H2_group, Name of population group 2

• number_hosts_sampled_group_1, Number of individuals sampled from population group 1

• number_hosts_sampled_group_2, Number of individuals sampled from population group 2

• number_hosts_population_group_1, Estimated number of individuals in population group 1

• number_hosts_population_group_2, Estimated number of individuals in population group 2

• max_possible_pairs_in_sample, Number of distinct possible transmission pairs between individuals sampled from population groups 1 and 2

• max_possible_pairs_in_population, Number of distinct possible transmission pairs between individuals in population groups 1 and 2

• num_linked_pairs_observed, Number of observed directed transmission pairs between samples from population groups 1 and 2

• p_hat, Probability that pathogen sequences from two individuals randomly sampled from their respective population groups are linked

• prob_group_pairing_and_linked, Probability that a pair of pathogen sequences is from a specific population group pairing and is linked

Methods (by class)

• default: Estimates joint probability of linkage

Details

For a population group pairing \((u,v)\), the joint probability that a pair is from groups \((u,v)\) and is linked is computed as

$$(N_uv / N_choose_2) * p_hat_uv ,$$

where,

• N_uv = N_u * N_v: maximum distinct possible \((u,v)\) pairs in population

• p_hat_uv: probability of linkage between two individuals randomly sampled from groups \(u\) and \(v\)

• N choose 2 or (N * (N - 1))/2 : all distinct possible pairs in population.

See bumblebee website for more details https://magosil86.github.io/bumblebee/.

References

1. Magosi LE, et al., Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial <U+2013> BCPP/ Ya Tsie trial. To submit for publication, 2021.

2. Carnegie, N.B., et al., Linkage of viral sequences among HIV-infected village residents in Botswana: estimation of linkage rates in the presence of missing data. PLoS Computational Biology, 2014. 10(1): p. e1003430.

See Also

See estimate_p_hat to prepare input data to estimate prob_group_pairing_and_linked

Examples

# NOT RUN {
library(bumblebee)
library(dplyr)

# Estimate joint probability that a pair is from a specific group pairing and linked

# We shall use the data of HIV transmissions within and between intervention and control
# communities in the BCPP/Ya Tsie HIV prevention trial. To learn more about the data 
# ?counts_hiv_transmission_pairs and ?sampling_frequency


# Load and view data
#
# The input data comprises counts of observed directed HIV transmission pairs 
# within and between intervention and control communities in the BCPP/Ya Tsie 
# trial, sampling information and the probability of linkage between individuals
# sampled from intervention and control communities (i.e. \code{p_hat})
#
# See ?estimate_p_hat() for details on estimating p_hat
results_estimate_p_hat <- estimated_hiv_transmission_flows[, c(1:10)]

results_estimate_p_hat

# Estimate prob_group_pairing_and_linked
results_prob_group_pairing_and_linked <- estimate_prob_group_pairing_and_linked(
    df_counts_and_p_hat = results_estimate_p_hat, 
    individuals_population_in = sampling_frequency$number_population)

# View results
results_prob_group_pairing_and_linked

# }