Towards reliable early warning for catastrophic pandemics
The world is demonstrably vulnerable to biological threats. COVID-19 was a disaster, but future pandemics could be far worse. To protect society from future catastrophe, we need reliable early warning for any future pandemic pathogen – including those we have never seen before.
The Nucleic Acid Observatory project, a joint effort of SecureBio and MIT’s Sculpting Evolution group, aims to solve this problem by designing new disease surveillance methods capable of detecting any pandemic threat. We are particularly focused on pathogens that – whether through natural evolution or deliberate engineering – may evade existing or near-future surveillance systems focused on known pathogens of concern.
To achieve this, we're using models and experiments to evaluate the potential of different approaches to large-scale disease monitoring, creating and deploying experimental tools to quantify the sensitivity of monitoring in the field, and developing novel computational approaches to detect potential threats as early and reliably as possible.
Together, we can build a pandemic early warning system for the 21st century. If you share that vision, consider reading more about our research or applying to our open positions.
Our research
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Evaluating biosurveillance approaches
Understanding & comparing large-scale monitoring approaches
Biosurveillance encompasses a wide array of approaches for detecting new disease outbreaks. For the NAO, the challenge is to identify which are the most sensitive, reliable, and cost-effective for pathogen-agnostic early warning. Through epidemiological modeling, data analysis, and experimentation, we are characterizing and comparing the merits of different approaches, with a special focus on municipal and airplane wastewater.
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Disease surveillance benchmarks
Building tools and protocols to quantify biosurveillance performance
Wastewater surveillance needs in-depth evaluation prior to large-scale deployment. The NAO is developing both computational and wet-lab benchmarking tools for this purpose, including a unique library of nucleic-acid tracers. By linking the amount of tracer released to the amount present in a downstream sample, we can comprehensively characterize performance under different conditions.
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Computational threat detection
Designing new methods to detect pathogens in sequencing data
To detect an emerging pathogen in sequencing data, one must reliably distinguish pathogen sequences from a complex and ever-changing microbial background. NAO researchers are developing, refining, and evaluating new computational tools for identifying threatening sequences in complex sequencing data, with a special focus on techniques that identify threats based on their growth pattern over time.