Description The estimate_transmission_flows() function Cite the package Author(s) References See Also
Bumblebee 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.
Population groups might include: communities, geographical regions, age-gender groupings or arms of a randomized-clinical trial.
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. See references to learn more about phyloscanner.
estimate_transmission_flows()
functionTo estimate transmission flows, that is, the relative probability of transmission
within and between population groups accounting for variable sampling the among
the population groups the function: estimate_transmission_flows_and_ci()
computes the conditional probability, theta_hat
that a pair of pathogen
sequences is from a specific population group pairing given that the pair is
linked.
For two population groups of interest (u,v) theta_hat
is denoted by
\hat{θ_{ij}} = Pr(pair from groups (i,j) | pair is linked), where i = u,v and j = u,v .
To learn more and try some examples, see documentation of the
estimate_transmission_flows()
function and the bumblebee package
website https://magosil86.github.io/bumblebee/.
Please cite the package using the following reference: Lerato E. Magosi, Marc Lipsitch (2021). Bumblebee: Quantify Disease Transmission Within and Between Population Groups. R package version 0.1.0 https://magosil86.github.io/bumblebee/
Lerato E. Magosi lmagosi@hsph.harvard.edu or magosil86@gmail.com
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.
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.
Goodman, L. A. On Simultaneous Confidence Intervals for Multinomial Proportions Technometrics, 1965. 7, 247-254.
Glaz, J., Sison, C.P. Simultaneous confidence intervals for multinomial proportions. Journal of Statistical Planning and Inference, 1999. 82:251-262.
May, W.L., Johnson, W.D. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software, 2000. 5(6). Paper and code available at https://www.jstatsoft.org/v05/i06.
Ratmann, O., et al., Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis. Nature Communications, 2019. 10(1): p. 1411.
Sison, C.P and Glaz, J. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 1995. 90:366-369.
Wymant, C., et al., PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity. Molecular Biology and Evolution, 2017. 35(3): p. 719-733.
See the following functions for details on estimating transmission flows
and corresponding confidence intervals: estimate_transmission_flows_and_ci
,
estimate_theta_hat
and estimate_multinom_ci
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.