Current influenza surveillance practices rely on syndromic surveillance and confirmed influenza case counts, aggregated at the care-provider level and reported to public health agencies. These systems rely on symptomatic individuals seeking medical care, and the intake, diagnosis, aggregation, and reporting steps introduce delays. We propose a novel human sentinel network (HSN) for bio-surveillance, comprising a network of individuals outfitted with wearable sensors capable of detecting illness pre-symptomatically, and rapidly deployed diagnostic tests to confirm or deny infection by specific pathogens; alerts and test results would be aggregated in near-real-time via a cloud-based network. We use an Agent-Based Model to model both the HSN and current surveillance practices to evaluate the potential performance of such a system. We introduce a novel measure of network coverage, characterize the performant trade-space, and perform sensitivity analyses to identify the most critical network characteristics. Results indicate that for a network covering more than ~5% of the population, the HSN can identify the onset of the influenza season 5 – 14 days earlier than current practices.