Microbial pathogen transmission in buildings is an urgent public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in public buildings with minimal disruptions in building functions. With the ultimate goal to develop healthy buildings that minimize risks of infectious diseases, this project will develop smart control strategies for buildings and assistive robots to mitigate pathogen transmission and occupant exposure. New techniques will be developed to monitor and predict pathogen spreading, automate building ventilation, enable intelligent recognition of contaminated objects, and perform precision disinfection to reduce pathogen transmission through air circulation and surface contacts. Findings from this project will also guide occupants and facility managers to develop and implement effective behavioral interventions and hygiene practices. If successful, this research will revolutionize the control of built environments to enable protection against infectious diseases, which will have vast public health and economic benefits to the nation. This project will also create new and unique opportunities to stimulate the academic interests of students and support the development of next-generation workforce adequately equipped with interdisciplinary computing and engineering skills needed to address challenges facing the nation.

The objectives of this research are: 1) Advance understanding of linkages among physical, biological, and social processes that drive the dynamics of human-pathogen interactions in building environments; and 2) Develop a novel cyber-physical system of integrated monitoring, building control, robot adaptation, and human-in-the-loop interactions to reduce the transmission of infectious pathogens. This research pioneers a novel digital-twinning approach that integrates building information modeling, privacy-preserving internet of things sensing, spatiotemporal molecular and metagenomic sequencing, and machine learning to map building-human-pathogen interactions for predicting contamination and infection risks at multiple spatiotemporal scales. New methods will be developed to connect and manage the buildings, occupants, and robots to reduce pathogen burdens. These methods include: model-based and dynamic-data-enabled optimal control of building ventilation; learning algorithms for robotic identification of contaminated spots; adaptive disinfection processes with behavioral considerations; and co-optimization of building operations and robotic disinfection with real-time sensing. In addition, user-centric systems will be developed to analyze contextual information and recommend hygiene practices, organizational operations, and crowd management to prevent disease spreading and maintain functionalities within buildings.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Sankar Basu
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University of Tennessee Knoxville
United States
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