Estimating the Relative Probability of Direct Transmission between Infectious Disease Patients

Abstract

BackgroundEstimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important to understand infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. MethodsUsing a subset of cases with detailed genetic or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. ResultsIn simulations we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. ConclusionsAll rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/612945 doi: bioRxiv preprint 3 Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

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Estimation of local time-varying reproduction numbers in noisy surveillance data

“…If the survey data is unavailable, using genomic data is a natural alternative. Genomic surveillance has been used to detect transmission clusters and to provide information on the possible source of individual cases [26–31].…”

What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

“…We assume we have WGS data for all cases and define pairs of close genetic relatedness as pairs that differ by fewer than two SNPs. 31,[37][38][39] In sensitivity analyses, we consider other thresholds: fewer than three, four, and five SNPs. For each outbreak, we estimate OR G and OR T with 95% confidence intervals from the contingency table using standard methods.…”

“…Recently, we developed a method that uses probable transmission events defined by WGS and/or contact investigations to estimate the transmission probability between cases using pair-level covariates. 31 The contribution of each covariate to the probabilities represents another proxy for the relationship between covariates and transmission, but one that accounts for the uncertainty of transmission defined by WGS and/or contact investigations using an iterative estimation process. Here, we use simulations to determine how similar the association between covariates and close genetic relatedness is to the true association with transmission.…”