Estimating the relative probability of direct transmission between infectious disease patients.

ABSTRACT:

Background

Estimating 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 in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.

Methods

Using a subset of cases with detailed genetic and/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 frequencie

SUBMITTER: Leavitt SV 

PROVIDER: S-EPMC7394954 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature