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

Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches.
Author summary Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new approach which combines different types of data to identify clusters of related cases of an infectious disease. This approach relies on representing each type of data (e.g. temporal, spatial, or genetic) as a graph where nodes are cases, and two nodes are connected if the corresponding cases are closely related for this data. Our method then identifies clusters of cases which likely stem from the same introduction. Furthermore, we can use the size of these clusters to infer transmissibility of the disease and the number of importations of the pathogen into the population. We apply this approach to analyse dog rabies epidemics in Central African Republic. We show that outbreak clusters identified using our method are consistent with structures previously identified by more complex and computationally intensive approaches. Using simulated rabies epidemics, we show that our method has excellent potential for optimally detecting outbreak clusters. We also identify promising areas of research for transforming our method into a routine analysis tool for processing disease surveillance data.