Branching Process Model
In this package, we use branching processes to model the time-dependent reproduction number (the number of cases each infected individual will subsequently cause) of an infectious disease.
All features of our software are described in detail in our full API documentation.
A web app for performing inference for branching process models is included in this package. Instructions for accessing the app are available here.
More details on branching process models and inference can be found in these papers:
References
[1] R. Creswell,† D. Augustin,† I. Bouros,† H. J. Farm,† S. Miao,† A. Ahern,† M. Robinson, A. Lemenuel-Diot, D. J. Gavaghan, B. C. Lambert and R. N. Thompson: “Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number,” Phil. Trans. R. Soc. A. 380: 20210308 (2022).
[2] Cori A, Ferguson NM, Fraser C, Cauchemez S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology 178(9): 1505-12.
[3] Thompson RN, Stockwin JE, van Gaalen RD, Polonsky JA, Kamvar ZN, Demarsh PA, Dahlqwist E, Li S, Miguel E, Jombart T, Lessler J. (2019). Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics 29: 100356.
Installation procedure
One way to install the module is to download the repositiory to your machine of choice and type the following commands in the terminal.
git clone https://github.com/SABS-R3-Epidemiology/branchpro.git
cd ../path/to/the/fileA different method to install this is using pip:
pip install -e .Usage
import branchpro
import numpy as np
# create a simple branching process model with prescribed initial R and serial interval
branchpro.BranchProModel(initial_r=0.5, serial_interval=[0, 0.15, 0.52, 0.3, 0.01])
# create branching process model with local and imported cases with prescribed initial R
# and serial interval
# set imported cases data
libr_model_1 = branchpro.LocImpBranchProModel(
initial_r=2, serial_interval=np.array([1, 2, 3, 2, 1]), epsilon=1)
libr_model_1.set_imported_cases(times=[1, 2.0, 4, 8], cases=[5, 10, 9, 2])
# create the posterior of a branching process model for multiple daily serial intervals
# and incidence data contained in the dataframe df; prior distribution is Gamma with
# parameters alpha and beta (shape, rate)
branchpro.BranchProPosteriorMultSI(
inc_data=df, daily_serial_intervals=[[1, 2], [0, 1]], alpha=1, beta=0.2)More examples on how to use the classes and features included in this repository can be found here.
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.