bumblebee-package

Bumblebee: Quantify Disease Transmission Within and Between Population Groups

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. package

The estimate_transmission_flows() function

To 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

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/.

Cite the package

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/

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 – 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.
3. Goodman, L. A. On Simultaneous Confidence Intervals for Multinomial Proportions Technometrics, 1965. 7, 247-254.
4. Glaz, J., Sison, C.P. Simultaneous confidence intervals for multinomial proportions. Journal of Statistical Planning and Inference, 1999. 82:251-262.
5. 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.
6. 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.
7. 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.
8. 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 Also

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.

Author(s)

Lerato E. Magosi lmagosi@hsph.harvard.edu or magosil86@gmail.com