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.

翻译：基于实时监测数据拟合的机制模型，对于理解疫情实时演变中的传播动力学至关重要。然而，由于监测数据存在噪声且无法全面反映机制模型的所有特征，传播模型的参数估计可能不精确，甚至完全不可行。为部分克服这一障碍，研究者提出了整合多源监测数据的贝叶斯模型。我们设计了一个建模框架，整合了SARS-CoV-2诊断检测与死亡时间序列数据，以及来自横断面研究的血清阳性率数据，并检验了各数据源对推断与预测的重要性。值得注意的是，我们的发病率模型考虑了总检测次数的变化。我们将传播率、感染致死比，以及控制真实病例发病率与阳性检测比例函数关系的参数建模为时变量，并通过非参数方法估计这些参数的变化。我们通过对比未使用诊断检测计数或血清阳性率数据的修改版本，验证了整合这些常被忽略数据源的价值。我们将该贝叶斯数据整合方法应用于2020年3月至2021年2月加州橙县收集的COVID-19监测数据，发现截至2021年1月中旬，橙县32%–72%的居民曾感染SARS-CoV-2。尽管感染人数众多，我们的结果表明，2021年1月冬季激增的突然终结，既是行为改变的结果，也归因于累积的自然免疫水平。