Riding the epidemic curve to glory, WGS edition

One reassuring lesson all healthcare epidemiologists learn is that every outbreak will, eventually, come to an end. The trick is to prevent outbreaks in the first place, or to recognize them early enough to intervene effectively. Many outbreaks, though, are recognized as they peak and are entering the “downhill” part of the epidemic curve. Any interventions prescribed by erstwhile epidemiologists are then, in retrospect, credited with helping to contain the outbreak (even if said interventions were completely idiotic). This phenomenon was described by Dr. Alexander Langmuir, the father of the CDC’s Epidemic Intelligence Service, as “riding to glory on the downhill slope of the epidemic curve”.

Now we can add the performance of whole-genome sequencing (WGS) to the list of activities that epidemiologists can “ride to glory” as the key to outbreak control. As WGS becomes faster and more affordable, reports have been published in NEJM, Science Translational Medicine and Lancet Infectious Diseases suggesting that WGS can be the key to real-time or “actionable” information to help contain outbreaks (due to MRSA, KPC-producing K. pneumoniae, and MRSA, respectively). As we’ve pointed out previously, though, it isn’t at all clear that WGS was important to real-time outbreak management, or that WGS is ready for prime-time and coming soon to a hospital near you.

Why? For any technology to see widespread adoption in clinical diagnostic laboratories, there must be sufficient automation (including of the analysis and interpretation of the massive amounts of WGS data), and it must provide a substantial advantage over existing testing approaches.

Which brings us to the more important question: given our crude approaches to outbreak control, how does WGS provide any immediate advantage over other same-day typing methods? Does the added discrimination really make a difference in whether we decide to isolate or cohort patient X, decolonize healthcare worker Y, or close unit Z to new admissions? These questions are particularly pertinent when we don’t yet understand the “within host” variation in genotype and how “within” versus “between” host variation can be applied to determine direction of transmission (or even the fact of transmission). Eli recently sent me a link to this interesting discussion of the Lancet ID report, which addresses some of these issues. 

The bottom line is that we have a relatively few crude tools for outbreak response: enhanced basic practices (e.g. hand hygiene, environmental disinfection), active surveillance, isolation, cohorting (of patients and/or HCWs), decolonization of carriers (both HCWs and patients), removal (temporarily or permanently) of HCWs implicated in transmission, closure of units, mitigation of any identified common sources, etc. The level of discrimination provided by WGS is a great research tool, and will undoubtedly help in retrospectively piecing together the most likely outbreak scenarios--but for now I’m not convinced it has much advantage over other typing methods for guiding real-time outbreak investigation and management.