Spatial Exposure Notification

Department: Architecture
Research Centre: Healthy High Density Cities Lab;
Project Title: Spatial Exposure Notification
Project Coordinator: Dr. E.H. Schuldenfrei
Commencement Date: June 30, 2022
Completion Date: April 29, 2024
Funding: Supported by a Collaborative Research Fund grant from the Research Grants Council of the Hong Kong Special Administrative Region, China, HKU C7105-21G

The devastating cholera pandemics of the 19th century were solved first by epidemiology, which identified the water-borne pathogen and its spread, and then by a civil effort to re-engineer the water infrastructure of cities. How might city design change as a result of the early 21st century pandemic and how do we couple building configuration and the new infrastructure of pathogen surveillance?

As novel air-borne diseases such as SARS and COVID-19 (C19) overtake us, what form should our 21st century response take? Our solution must be interdisciplinary, integrating new ideas from epidemiology, architecture, and psychology. But even fundamental facts of how influenza propagates remain unobserved — we need new ways of sensing the spread of disease in the built environment.

We propose a new class of low-cost sensor devices that interoperate with the current Google / Apple Exposure Notification (GAEN) framework while fixing deficiencies in its design — which, by neglecting crucial spatial and temporal aspects of contagion, triggers inaccurate contact notifications. We maintain (and repair) existing privacy protections while adding new sources of data through the integration of fixed environmental sensors that let us reconstruct key spatial dimensions of contagion.

Exposure risk estimates are improved by more accurately measuring interactions between environment, proximity, time, location, and airflow. These metrics can enable rapid contact tracing for airborne diseases like influenza and can provide a data collection infrastructure essential for the experimentation required to refine transmission models and develop efficient responses. Such data can also guide modifications to building codes, urban environments and policy for future pandemics. By targeting the requirements to operate within architectural elements such as lightbulbs, our proposed GAEN-based sensor devices can potentially enable a massively scalable solution within existing building infrastructure.

The information our system collects in real time is vital to government decision-making. In the C19 pandemic, governments have struggled to enact real-time regulations for quarantining, social distancing and masking. For new diseases, the nature of transmission (fomite vs airborne), incubation periods and transmission curves will again be initially unknown. Manual contact tracing schemes designed to capture this data fail at scale and therefore near-instantaneous contact tracing becomes necessary to produce rapid exposure notifications to reduce infections. Even in the case of the yearly flu, our proposal for a rapid, dynamically calibrated and trusted exposure notification system — which could simply tell potentially infected persons to wear a mask for a few days — might transform the economic and human costs in ways that cannot be hoped for through radical and exorbitant modifications to existing building HVAC systems.

Project Team

Eric Schuldenfrei (HKU)
George Q. Huang (HKU)
Howard Huang (Nokia Bell Labs)
Marc Downie (University of Chicago)
Yuguo Li (HKU)
Chris Webster (HKU)
John Gallacher (University of Oxford)
John Bacon-Shone (HKU)
Anne SY Cheung (HKU)
Tuan Q. Phan (HKU)
Christian Chan (HKU)
Paul Kaiser (Collaborator)

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