Biosurveillance has evolved beyond biodefense, emphasizing a comprehensive network to detect and counter biological threats.
With the COVID-19 pandemic and the recent H5N1 outbreak in dairy cows, biosurveillance has become a term that is now comfortably in the national repertoire. Like so many terms, biosurveillance has become a bit of an umbrella. Traditionally we used it more so in reference to biodefense efforts in which the focus was a pathogen early warning system against biological threats that could be nefarious.
Public health surveillance though, isn’t a novel concept, nor one that has roots solely in biodefense efforts. I emphasize this as over the years, and especially since the pandemic, many terms have seen a broader application. Biosurveillance now increasingly means a larger network of “program, policies, procedures, workforce, and technology” that encompasses human, animal, plant, and environmental health through monitory to identify potential threats.1
We’ve been employing a surveillance strategy as a mechanism for prevention, response, and resilience to infectious disease threats for a while though. It’s likely you’ve heard of this as public health surveillance, which according to the World Health Organization (WHO), it is “the continuous and systematic collection, orderly consolidation and evaluation of pertinent data with prompt dissemination of results to those who need to know, particularly those who are in a position to take action.”2
Surveillance networks have ranged from wastewater to utilization of clinical and public health laboratory systems to syndromic surveillance, etc. Different approaches allow us to be as specific as possible or even pathogen agnostic, which means that we differ from traditional approaches that look for a pre-determined list of diseases, but rather use metagenomic next generation sequencing (mNGS) to identify novel or unexpected pathogens. For example, sentinel surveillance is a strategic approach where efforts target specific populations, such as airports or high-risk populations like long-term care facilities or medical providers, whereas syndromic surveillance focuses the symptoms of patients in emergency departments and clinical settings, but pre and post diagnosis, which can help public health agencies identify and monitor trends as an early warning system. In truth, we could spend chapters discussing the kinds of biosurveillance strategies, methods for pathogen early warning systems, and all the ways we’ve invested in these collaborative efforts. From routine surveillance of arboviruses 3 in mosquitoes to partnering with clinical labs,4 and collaboration with veterinary services to monitor animal health, there’s a range of efforts underway. As previously mentioned though, it’s not just about the strategy of who to test and what to test for, but the larger network that ensures these strategies work, through data, laboratory practices, and ultimately, how they guide decisions. This is where the new CDC Center for Forecasting and Outbreak Analytics (CFA) 5 has been developed.
Biosurveillance traditionally referred to biodefense efforts, specifically pathogen early warning systems against nefarious biological threats.
Unlike traditional approaches that focus on predetermined diseases, mNGS allows for pathogen-agnostic identification. It detects novel or unexpected pathogens.
The CDC Center for Forecasting and Outbreak Analytics (CFA) plays a crucial role in biosurveillance.
All this talk of biosurveillance though, brings us back to two key issues—first, the US, like so many countries, is heavily investing in enhancing our capacity to detect diseases, both known and unknown, faster and more broadly. These efforts can be seen in the CDC’s launching of the National Wastewater Surveillance (NWSS)6 back in 2020, serving as an early warning network that “can detect small changes as a signal for early action.”
Addressing gaps in our pathogen early warning systems though will take time as more organizations, labs, and agencies are coordinating to build these efforts out, not only locally, but on an international scale. Secondly, the current H5N1 outbreak is a prime example of how much work we have to do. From stressed and disjointed data systems7 to a novel situation that amplifies a trend in US health response—we tend to try and apply the lessons from the last outbreak to the one we’re facing. In this case though, we’re facing not a novel pathogen, but a uniquely impacted industry—dairy production.
Coordination testing across farmers, dairy workers, and unifying data streams to understand both transmission and herd cases, are all proving difficult. Despite the painful and recent lessons of gaps in our surveillance and testing infrastructure during the COVID-19 pandemic, we’re facing similar issues with H5N1. All of this reiterates the importance of investing in sustainable and agile biosurveillance systems—that we can coordinate across human, animal, insect, and environmental networks, to identify not only a pre-determined list of diseases, but alo detect novel variants and pathogens. As we are three months into the H5N1 outbreak in the United States, I urge you to think about how surveillance is playing into our response..or lack there of?
