A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation

The COVID-19 pandemic has highlighted the need for improved airborne infectious disease monitoring capability. A key challenge is to develop a technology that captures pathogens for identification from ambient air. While pathogenic species vary significantly in size and shape, for effective airborne...

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Bibliographic Details
Published in:Applied sciences 2021-06, Vol.11 (11), p.5308
Main Authors: Bango, Joseph J., Agostinelli, Sophia A., Maroney, Makayla, Dziekan, Michael, Deeb, Ruba, Duman, Gazi
Format: Article
Language:English
Subjects:
aerosol
Aerosols
Air sampling
aptamer
Atoms & subatomic particles
Bacteria
Cell culture
charge detection mass spectrometry
COVID-19
Culture media
Decision making
Droplets
Electrodes
electrospray
electrospray mass spectrometer
Electrostatic discharges
Gas flow
High Efficiency Particulate Air (HEPA)
Identification
Infectious diseases
Lasers
Pandemics
Pathogens
Physical characteristics
Prion protein
Prions
Public health
Samplers
Solvents
Spores
Viruses
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Summary:The COVID-19 pandemic has highlighted the need for improved airborne infectious disease monitoring capability. A key challenge is to develop a technology that captures pathogens for identification from ambient air. While pathogenic species vary significantly in size and shape, for effective airborne pathogen detection the target species must be selectively captured from aerosolized droplets. Captured pathogens must then be separated from the remaining aerosolized droplet content and characterized in real-time. While improvements have been made with clinical laboratory automated sorting in culture media based on morphological characteristics of cells, this application has not extended to aerosol samples containing bacteria, viruses, spores, or prions. This manuscript presents a strategy and a model for the development of an airborne pandemic early warning system using aerosol sampling.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11115308