Introduction: Large, localised outbreaks of COVID-19 have been repeatedly reported in high-density residential institutions. Understanding the transmission dynamics will inform outbreak response and the design of living environments that are more resilient to future outbreaks.
Methods: We developed an individual-based, multilevel transmission dynamics model using case, serology and symptom data from a 60-day cluster randomised trial of prophylaxes in a densely populated foreign worker dormitory in Singapore. Using Bayesian data augmentation, we estimated the basic reproduction number and the contribution that within-room, between-level and across-block transmission made to it, and the prevalence of infection over the study period across different spatial levels. We then simulated the impact of changing the building layouts in terms of floors and blocks on outbreak size.
Results: We found that the basic reproduction number was 2.76 averaged over the different putative prophylaxes, with substantial contributions due to transmission beyond the residents' rooms. By the end of ~60 days of follow up, prevalence was 64.4 % (95 % credible interval 64.2-64.6 %). Future outbreak sizes could feasibly be halved by reducing the density to include additional housing blocks, or taller buildings, while retaining the overall number of men in the complex.
Discussion: The methods discussed can potentially be utilised to estimate transmission dynamics at any high-density accommodation site with the availability of case and serology data. The restructuring of infrastructure to reduce the number of residents per room can dramatically slow down epidemics, and therefore should be considered by policymakers as a long-term intervention.
Keywords: Data augmentation; Migrant workers; Outbreak modelling; SARS-CoV-2; Transmission dynamics.
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