Finding Evidence for Local Transmission of Contagious Disease in Molecular Epidemiological Datasets

Abstract

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

“…Of course, such information might not be available directly from epidemiological data. In this case, one option might be to use statistical methods along with genetic and epidemiological data to differentiate between local and imported cases (Ypma et al, 2013;Cori et al, 2018).…”

A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies

“…Our study used temporal, spatial and genetic data to detect outbreak clusters, but there is no theoretical limit to the number and nature of data sources, as long as the data can be used to compute a measure of dissimilarity between pairs of cases. Unlike previous approaches [38], our method also exploits preexisting information on the disease, such as its serial interval, spatial kernel, and mutation rate, to define whether cases are part of the same outbreak cluster or not. Our method will therefore be particularly useful to understand transmission patterns for known pathogens, for which the natural history, transmission characteristics and evolutionary rates are well described in the literature.…”

“…Our method will therefore be particularly useful to understand transmission patterns for known pathogens, for which the natural history, transmission characteristics and evolutionary rates are well described in the literature. On the other hand, non-parametric approaches such as that described in Ypma et al [38], which make no assumption about the underlying pathogen, may be better suited to the analysis of data on emerging, poorly characterised pathogens. Note however that the performance of the method described in Ypma et al [38] has only been tested on data simulated using an extremely simple and unrealistic evolutionary model, with at least 80% reported cases.…”

“…On the other hand, non-parametric approaches such as that described in Ypma et al [38], which make no assumption about the underlying pathogen, may be better suited to the analysis of data on emerging, poorly characterised pathogens. Note however that the performance of the method described in Ypma et al [38] has only been tested on data simulated using an extremely simple and unrealistic evolutionary model, with at least 80% reported cases. In contrast, our approach explicitly accounts for underreporting, and was tested on more realistic epidemic scenarios, with reporting rates as low as 10%.…”

“…Unfortunately, despite these connections to well-developed methodological fields, the integration of multiple data sources (e.g. epidemiological and genomic data) to identify outbreak clusters remains in its infancy [38].…”