Modeling Pandemic Response for Populations Equipped with Contact-Chain Capable Wearable Devices

Abstract

Extant pandemic detection and prevention strategies are based around contact tracing technologies. In contrast, this manuscript describes a strategy for preemptive pandemic response based on wearable devices with built-in privacy, based on distributed, encrypted and anonymized contact chains. We evaluate such strategy in an agent-based simulation environment that models a wearable-device equipped population in an urban area, comparing it with no response strategy, and with the extant contact-tracing approaches. Our results suggest contact-chaining is an effective way to augment pandemic response, by preemptively isolating persons who are potentially infectious; initial results indicate up to a 23.4% reduction in peak infections compared to the strictest extant approach. Simulations show that this strategy is especially effective during a first wave and can potentially prevent further infection waves. Ongoing work is looking at the privacy issues of such an approach and modeling countermeasures within our simulation framework.