NSF Award
2427070
Infectious airborne pathogens and their spread have caused increased incidences of outbreaks and pandemics, which challenge our society with severe impacts on public health, economies, and social well-being. Controlling airborne pathogens is challenging because they are transmitted easily via respiratory aerosols, spread quickly over large distances, and can mutate to become more infectious and morbid in a short time. Reactive approaches such as treating illnesses or responding to outbreaks after they occur are usually ineffective and costly. In contrast, proactively addressing airborne pathogens is of paramount importance for minimizing the likelihood of outbreaks and safeguarding public health. A proactive approach in combating airborne diseases necessitates early detection of target pathogens and effective communication to ensure prompt collective action within communities. However, it remains extremely challenging to achieve rapid, accurate, scalable, and cost-effective on-site detection of airborne pathogens. Meanwhile, along with the deployment of the pathogen sensing technologies, there are important questions to address on how to communicate potential risks and mitigation options to communities. To address these challenges, this SCC-PG project brings together a highly interdisciplinary team of researchers and uses community-based participatory research to engage a broad range of community partners. Success of this SCC-PG project will provide innovative and proactive solutions in response to the grand challenge of public health protection. Such a proactive system will also foster a culture of prevention and preparedness to better handle future community health threats.<br/><br/>The research will significantly advance science and technology in designing, constructing, modeling and deploying a novel biosensing system timely in-field airborne pathogen monitoring. The intellectual contributions are three-fold. First, the project will create an intelligent system for automated, real-time, and multiplex monitoring of critical airborne pathogens. In addition, an intelligent automated system to obtain time-resolved measurements of infectious aerosols in-field will be established. Second, the project will create a novel data-driven quality-aware deep learning approach which not only effectively generates inference and reconstruction results but also provides rigorous accuracy quantification and interpretation. Third, the project will develop a new framework of ethical dialogue to provide collaborative spaces that will lead to greater involvement and empowerment of the public with the focus on effective risk communication and mitigation.<br/><br/>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.
