In this section
@article{Geismar:2026:10.1371/journal.pcbi.1014271,
author = {Geismar, C and White, PJ and Cori, A and Jombart, T},
doi = {10.1371/journal.pcbi.1014271},
journal = {PLoS Comput Biol},
title = {A statistical framework for comparing epidemic forests.},
url = {http://dx.doi.org/10.1371/journal.pcbi.1014271},
volume = {22},
year = {2026}
}
TY - JOUR
AB - Inferring who infected whom in an outbreak is essential for characterising transmission dynamics and guiding public health interventions. However, this task is challenging due to limited surveillance data and the complexity of immunological and social interactions. Instead of a single definitive transmission tree, epidemiologists often consider multiple plausible trees forming epidemic forests. Various inference methods and assumptions can yield different epidemic forests, yet no formal test exists to assess whether these differences are statistically significant. We propose such a framework using a chi-square test and permutational multivariate analysis of variance (PERMANOVA). We assessed each method's ability to distinguish simulated epidemic forests generated under different offspring distributions. While both methods achieved perfect specificity for forests with 100+ trees, PERMANOVA consistently outperformed the chi-square test in sensitivity across all epidemic and forest sizes. Implemented in the R package mixtree, we provide the first statistical framework to robustly compare epidemic forests.
AU - Geismar,C
AU - White,PJ
AU - Cori,A
AU - Jombart,T
DO - 10.1371/journal.pcbi.1014271
PY - 2026///
TI - A statistical framework for comparing epidemic forests.
T2 - PLoS Comput Biol
UR - http://dx.doi.org/10.1371/journal.pcbi.1014271
UR - https://www.ncbi.nlm.nih.gov/pubmed/42224365
VL - 22
ER -
For any enquiries related to the Centre please contact:
Scientific Manager
Susannah Fisher
mrc.gida@imperial.ac.uk
External Relationships and Communications Manager
Dr Sabine van Elsland
s.van-elsland@imperial.ac.uk