This repository contains a Python package that can be used to estimate changes in the time-varying reproduction number from time series of cases. The statistical model is from the field of Bayesian nonparametrics, and results from using this package are given in Creswell et al., 2022, "A Bayesian nonparametric method for detecting rapid changes in disease transmission", Journal of Theoretical Biology.
Local copies of the package files are installable via pip:
pip install -e .See the examples directory for a simple notebook performing inference.
The results repository (https://github.com/SABS-R3-Epidemiology/epicluster-results) contains multiple examples illustrating the full functionality of the package.
The model of change points is based on:
[1] Martínez, A. F., & Mena, R. H. (2014). On a nonparametric change point detection model in Markovian regimes. Bayesian Analysis, 9(4), 823-858.
The epidemiological model is based on:
[2] 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.
[3] Creswell R, Augustin D, Bouros I, Farm HJ, Miao S, Ahern A, Robinson M, Lemenuel-Diot A, Gavaghan DJ, Lambert B, Thompson RN (2022). Heterogeneity in the onwards tranmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number. Philosophical Transactions of the Royal Society A (forthcoming).