A pandemic early warning system decision analysis concept utilizing a distributed network of air samplers via electrostatic air precipitation

Abstract

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 aeroso-lized 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 man-uscript presents a strategy and a model for the development of an airborne pandemic early warning system using aerosol sampling.