Strategies to Improve Detection of Novel Pandemic Pathogens

Detecting novel pathogens is a significant challenge for biosurveillance systems, in part because they rely on pathogen-specific detection technologies. Decisionmakers planning to improve biosurveillance systems using pathogen agnostic technologies lack evidence on their expected costs and the expected detection performance of alternative surveillance system designs. Investment in improved biosurveillance systems is limited by a lack of clarity on cost versus performance, as well as complex implementation choices.

Approach

This study introduces a model of three biosurveillance strategies, referred to as Syndromic, Wearable, and Environmental. Each of these is a pathogen-agnostic workflow designed to detect novel threats via metagenomic next-generation sequencing (mNGS). The Syndromic and Wearable strategies are initiated by a signal from symptomatic or physiological sensors (respectively), while the Environmental strategy runs continuously and is intensified responsively based on external intelligence from event-based surveillance. First, we parameterize this model to emulate the detection of wildtype SARS-Cov-2. Then, we explore how changes in pathogen characteristics and technology performance change key detection and cost measures. We inform our model with parameters derived from the literature, and publicly available information on fixed, operating, and variable costs of disease surveillance activities.

This study provides insight into the tradeoffs inherent to the design of biosurveillance systems by simulating three alternative strategies. Our modeling approach requires a set of assumptions about the pathogen being detected (i.e., transmissibility and disease progression), the detection technology (i.e., sensitivity of alternative detection systems) and how it would be used in a real-world setting. This simplified setting and treatment of strategies as discrete choices is intended to capture key features of a deployed military population and provide rough cost versus performance analysis as one input for choosing a novel surveillance system. Across all scenarios, we assume the existence of a baseline biomedical capability, including access to standard diagnostics, and basic outbreak investigation procedures. A real-world implementation would build on this existing infrastructure, and likely deploy the strategies considered here in combination rather than atomically.

Key Findings

• The Environmental strategy afforded the largest improvements in detection performance in our base-case scenario, followed by the wearable strategy.
• The superiority of the environmental and wearable strategies over the syndromic strategy degrades in scenarios where the disease has high transmissibility, the proportion of individuals who are asymptomatic is low and when the disease has a fast latent period.

Recommendations

• A limited deployment would allow operating costs to be estimated with more precision, provide an opportunity to collect logistical and operational data, estimate detection performance under real-world conditions, and explore data fusion with the larger biosurveillance enterprise.
• There is significant uncertainty in the real-world performance of next-generation sequencing technologies, which could be greatly reduced via a limited deployment of one of the strategies explored in this study, or a combination thereof.