Abstract
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.
Highlights
Infectious disease outbreaks are a recurring threat to humans and animals, with potentially disastrous impacts on human health, economy, and biodiversity
We show that our method has excellent potential for optimally detecting outbreak clusters
151 dogs infected with rabies were reported in Bangui, the capital of the Central African Republic between the 6th January 2003 and the 6th March 2012
Summary
Infectious disease outbreaks are a recurring threat to humans and animals, with potentially disastrous impacts on human health, economy, and biodiversity. Major epidemics such as the 2009 influenza pandemic [1], the emergence of the Middle-East Respiratory Syndrome (MERS, [2,3]) and the West-African Ebola virus disease outbreak (EVD, [4,5]) have re-emphasized the need for assessing outbreaks at an early stage. Rapid identification of clusters of cases linked by transmission and subsequent intervention remain our best chance of containing, or at least mitigating disease epidemics. Early assessment of the relative contributions of local transmission versus case importation is essential for designing appropriate intervention strategies. At a country level, local transmissions and importations of cases from other countries will require different control measures, e.g. social distancing and prevention versus border closing [8,9]. In the case of zoonotic infections, it is crucial to identify the extent to which within-species transmission and spill-over from the reservoir (i.e. cross-species transmission) contribute to the observed incidence, as illustrated in the case of avian influenza [10,11], bovine tuberculosis [12], or MERS [3,8,9]
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