InfectionTrees swMATH ID: 42443 Software Authors: Gallagher, Shannon K.; Follmann, Dean Description: Branching process models to identify risk factors for infectious disease transmission. Simple branching processes for infectious disease transmission assume all individuals are homogeneous, which means that risk factors that may inhibit or increase transmission are unable to be identified. In this work, we develop a branching process model that allows for identification of risk factors by assuming the probability of onward transmission is determined by the individual’s covariates. Because enumerating the transmission trees is infeasible for large clusters, we develop an algorithm to sample transmission trees to compute approximate maximum likelihood estimates. We then discuss how our model can be extended to account for cases that are undetected but are part of the true transmission tree. We use our method to investigate individual characteristics that are associated with transmission of Tuberculosis using clusters of detected cases in Maryland from 2003 to 2009. We find that later detection within a cluster is associated with an increased probability of onward transmission (OR = 1.41 [95 Homepage: https://skgallagher.github.io/InfectionTrees/articles/getting-started.html Source Code: https://github.com/skgallagher/InfectionTrees Dependencies: R Keywords: branching process; Monte Carlo sampling; transmission trees; unobserved transmission; tuberculosis Related Software: outbreaker2; RcppAlgos; LBFGS-B; Surveillance; R Cited in: 1 Document Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Branching process models to identify risk factors for infectious disease transmission. Zbl 07547630Gallagher, Shannon K.; Follmann, Dean 2022 Cited by 2 Authors 1 Follmann, Dean A. 1 Gallagher, Shannon K. Cited in 1 Serial 1 Journal of Computational and Graphical Statistics Cited in 1 Field 1 Statistics (62-XX) Citations by Year