Exploring the Local Determinants of SARS-CoV-2 Transmission and Control via an Exposure-Based Model.

ABSTRACT

A simulation model was developed aimed at assisting local public health authorities in exploring strategies for the suppression of SARS-CoV-2 transmission. A mechanistic modeling framework is utilized based on the daily airborne exposure of individuals defined in terms of inhaled viruses. Comparison of model outputs and observed data confirms that the model can generate realistic patterns of secondary cases. In the example investigated, the highest risk of being newly infected was among young adults, males, and people living in large households. Among risky occupations are food preparation and serving, personal care and service, sales, and production-related occupations. Results also show a pattern consistent with superspreading with 70% of initial cases who do not transmit at all while 13.4% of primary cases contribute 80% of secondary cases. The impacts of school closure and masking on the synthetic population are very small, but for students, school closure resulted in more time at home and increased secondary cases among them by over 25%. Requiring masks at schools decreased the case count by 80%. We conclude that the simulator can be useful in exploring local intervention scenarios and provides output useful in assessing the confidence that might be placed on its predictions.