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To control the spread of infectious disease it is important to quantify the impact of interventions and factors such as: age, sex, socio-economic status and geographical location in shaping patterns of transmission.
The Bumblebee package uses counts of directed transmission pairs identified between samples from population groups of interest to estimate the flow of transmissions within and between those population groups accounting for sampling heterogeneity.
Counts of observed directed transmission pairs can be obtained from deep-sequence phylogenetic data (via phyloscanner) or known epidemiological contacts.
Note: Deep-sequence data is also commonly referred to as high-throughput or next-generation sequence data.
Quantifying transmission patterns of HIV, the virus that causes AIDS, in the context of HIV prevention initiatives such as universal test-and-treat.
To learn more see: Magosi LE, et al., Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial. To submit for publication, 2021.
Quantifying transmission patterns of SARS-COV-2, the virus that causes COVID-19, in the presence of heterogeneous vaccine uptake.
We shall use the data of HIV transmissions within and between intervention and control
communities in the BCPP/Ya Tsie HIV prevention trial.
The BCPP / Ya Tsie study was a pair-matched community-randomized trial involving
30 communities in Botswana to test the effect of a universal HIV test-and-treat
intervention in efficiently reducing the occurrence of new HIV infections at the
population level.
To learn more about the data:
# Counts of directed HIV transmission pairs identified between samples from
# intervention and control communities.
?counts_hiv_transmission_pairs,
# Estimated number of individuals with HIV in intervention and control
# communities and the number of individuals sampled from each.
?sampling_frequency
# Estimated transmission flows or relative probability of transmission
# within and between population groups adjusted for variable sampling
# among the population groups.
# Note: The `theta_hat` variable denotes estimated transmission flows.
?estimated_hiv_transmission_flows
Magosi LE, et al., Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial. To submit for publication, 2021.
We shall use the estimate_transmission_flows_and_ci() function to estimate transmission
flows and corresponding confidence intervals within and between intervention and control
communities of the BCPP / Ya Tsie trial. See ?estimate_transmission_flows_and_ci() to
learn more about the function.
The estimate_transmission_flows_and_ci() function
requires the following inputs for analysis:
A character vector of population groups/strata (e.g. communities, age-groups, genders or trial arms) between which to estimate transmission flows.
A numeric vector indicating the number of individuals sampled per population group
A numeric vector of the estimated number of individuals per population group
A data.frame of counts of directed transmission pairs identified between samples from population groups of interest.
# Load libraries ------------------------------------------------
library(bumblebee) # for estimating transmission flows
library(dplyr) # for manipulating data.frames
# Estimate transmission flows and confidence intervals --------------------------
# 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
# View counts of observed directed HIV transmissions within and between
# intervention and control communities (n = 82)
counts_hiv_transmission_pairs
# View the estimated number of individuals with HIV in intervention and control
# communities and the number of individuals sampled from each
sampling_frequency
# Estimate transmission flows within and between intervention and control communities
# accounting for variable sampling among population groups.
# Basic output
results_estimate_transmission_flows_and_ci <- estimate_transmission_flows_and_ci(
group_in = sampling_frequency$population_group,
individuals_sampled_in = sampling_frequency$number_sampled,
individuals_population_in = sampling_frequency$number_population,
linkage_counts_in = counts_hiv_transmission_pairs)
# View results
results_estimate_transmission_flows_and_ci
# Retrieve dataset of estimated transmission flows
dframe <- results_estimate_transmission_flows_and_ci$flows_dataset
The theta_hat variable denotes estimated proportions of HIV transmissions in
the trial population within and between intervention and control communities.
There was substantial sexual mixing between intervention and control communities.
Transmissions into intervention communities from control communities were three
times more common than the reverse, compatible with a benefit from the universal
HIV test-and-treat intervention.
?estimate_transmission_flows_and_ci() for a description of all the output variablesFurther to estimating transmission flows, the bumblebee package provides estimates for:
p_hat, the probability of linkage between pathogen sequences from two individuals randomly sampled from their respective population groups
p_group_pairing_linked, the joint probability that a pair of pathogen sequences is from a specific population group pairing and linked
c_hat, the probability of clustering, more precisely, the probability that a pathogen sequence from one population group links with at least one pathogen sequence from another population group
and confidence intervals for the following methods:
Goodman with a continuity correction (useful for small samples)
Sison-Glaz
Queensbury-Hurst
# Estimate transmission flows and confidence intervals: Detailed output -----------------
# Detailed output
results_estimate_transmission_flows_and_ci_detailed <- estimate_transmission_flows_and_ci(
group_in = sampling_frequency$population_group,
individuals_sampled_in = sampling_frequency$number_sampled,
individuals_population_in = sampling_frequency$number_population,
linkage_counts_in = counts_hiv_transmission_pairs,
detailed_report = TRUE,
verbose_output = TRUE)
# View results
results_estimate_transmission_flows_and_ci_detailed
# Retrieve dataset of estimated transmission flows
dframe <- results_estimate_transmission_flows_and_ci_detailed$flows_dataset
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.