ABSTRACT
SARS-CoV-2 Omicron has become the predominant variant globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to cater for different settings. To ensure public health decision-makers can easily consider different public health interventions (PHIs) over a wide range of scenarios, we propose a generalized multinomial probabilistic model of airborne infection to systematically capture group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs, with assumptions made on social distancing and contact duration, and estimate infectivity over short time-span group gatherings. This study is related to our 2021 work published in Nature Scientific Reports that modelled airborne SARS-CoV-2 infection (Han, Lam, Li, et al., 2021).1 It is differentiated from former works on probabilistic infection modelling in terms of the following: (1) predicting new cases arising from more than one infectious in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although our results reveal that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. Our model is versatile and can flexibly accommodate other scenarios by allowing new factors to be added, to support public health decision-making.
ABSTRACT
Motivated by earlier findings that exposure to daily outdoor PM2.5 (P) may increase the risk of influenza infection, our study examines if immediate exposure to outdoor P will modify the rate of change in the daily number of COVID-19 infections (R), for (1) the high infection provincial capital cities in China and (2) Wuhan, China, using regression modelling. A multiple linear regression model was constructed to model the statistical relationship between P and R in China and in Wuhan, from 1 January to 20 March 2020. We carefully accounted for potential key confounders and addressed collinearity. The causal relationship between P and R, and the interaction effect between key variables were investigated. A causal relationship between P and R across the high infection provincial capital cities in China was established via matching. A higher P resulted in a higher R in China. A 10 {micro}g/m3 increase in P gave a 1.5% increase in R (p < 0.001). An interaction analysis between P and absolute humidity (AH) showed a statistically significant negative relationship between P x AH and R (p < 0.05). When AH was $ 5.8 g/m3, a higher P and AH gave a higher R. In contrast, when AH [≥] 5.8 g/m3, the effect of a higher P was counteracted by the effect of a higher AH, resulting in a lower R. Given that P can exacerbate R, we recommend the installation of air purifiers and better air ventilation to reduce the effect of P on R. Further, given the increasing discussions/observations that COVID-19 can be airborne, we highly recommend the wearing of surgical masks to keep one from contracting COVID-19 via the viral-particulate transmission pathway.
