  The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health,
economy and society worldwide. Emerging strains are making pandemic management
increasingly challenging. There is an urge to collect epidemiological,
clinical, and physiological data to make an informed decision on mitigation
measures. Advances in the Internet of Things (IoT) and edge computing provide
solutions for pandemic management through data collection and intelligent
computation. While existing data-driven architectures attempt to automate
decision-making, they do not capture the multifaceted interaction among
computational models, communication infrastructure, and the generated data. In
this paper, we perform a survey of the existing approaches for pandemic
management, including online data repositories and contact-tracing
applications. We then envision a unified pandemic management architecture that
leverages the IoT and edge computing to automate recommendations on vaccine
distribution, dynamic lockdown, mobility scheduling and pandemic prediction. We
elucidate the flow of data among the layers of the architecture, namely, cloud,
edge and end device layers. Moreover, we address the privacy implications,
threats, regulations, and existing solutions that may be adapted to optimize
the utility of health data with security guarantees. The paper ends with a
lowdown on the limitations of the architecture and research directions to
enhance its practicality.
