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In this paper we create a compartmental, stochastic process model ofSARS-CoV-2 transmission, where the process's mean and variance have distinctdynamics. The model is fit to time series data from Washington from January2020 to March 2021 using a deterministic, biologically-motivated signalprocessing approach, and we show that the model's hidden states, likepopulation prevalence, agree with survey and other estimates. Then, in thepaper's second half, we demonstrate that the same model can be reframed as abranching process with a dynamic degree distribution. This perspective allowsus to generate approximate transmission trees and estimate some higher orderstatistics, like the clustering of cases as outbreaks, which we find to beconsistent with related observations from contact tracing and phylogenetics.

COVID-19 epidemiology as emergent behavior on a dynamic transmission forest