A generalized multinomial probabilistic model for SARS‐COV‐2 infection prediction and public health intervention assessment in an indoor environment

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.