  Mechanistic models fit to streaming surveillance data are critical to
understanding the transmission dynamics of an outbreak as it unfolds in
real-time. However, transmission model parameter estimation can be imprecise,
and sometimes even impossible, because surveillance data are noisy and not
informative about all aspects of the mechanistic model. To partially overcome
this obstacle, Bayesian models have been proposed to integrate multiple
surveillance data streams. We devised a modeling framework for integrating
SARS-CoV-2 diagnostics test and mortality time series data, as well as
seroprevalence data from cross-sectional studies, and tested the importance of
individual data streams for both inference and forecasting. Importantly, our
model for incidence data accounts for changes in the total number of tests
performed. We model the transmission rate, infection-to-fatality ratio, and a
parameter controlling a functional relationship between the true case incidence
and the fraction of positive tests as time-varying quantities and estimate
changes of these parameters nonparametrically. We compare our base model
against modified versions which do not use diagnostics test counts or
seroprevalence data to demonstrate the utility of including these often unused
data streams. We apply our Bayesian data integration method to COVID-19
surveillance data collected in Orange County, California between March 2020 and
February 2021 and find that 32--72\% of the Orange County residents experienced
SARS-CoV-2 infection by mid-January, 2021. Despite this high number of
infections, our results suggest that the abrupt end of the winter surge in
January 2021 was due to both behavioral changes and a high level of accumulated
natural immunity.
