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:适合流流监测数据的机械模型对于了解突发事件实时发生时的传播动态至关重要。然而,传输模型参数估计可能不准确,有时甚至不可能,因为监测数据吵杂,对机械模型的所有方面没有信息。为部分克服这一障碍,提议了贝叶斯模型,将多个监测数据流结合起来。我们设计了一个模型框架,将SARS-COV-2诊断测试和死亡率时间序列数据以及跨部门研究的血清反应率数据结合起来,并测试了个人数据流对于推断和预测的重要性。重要的是,我们用于记录所进行测试总数变化的频率数据账户的模型。我们模拟了传输率、感染-致命比率以及控制真实案件发生率与正测试比例之间的功能关系参数,我们设计了一个模型,将SAS-COV-2诊断测试测试和死亡率时间序列序列数据整合成一个模型,我们把2021年的诊断测试和血清反应率测试或血清反应数据都比起来,以显示这些经常未使用的数据流的频率变化。我们在2020年1月20-19年的州里卡-加利福尼亚州测测测测测测测测测测度数据中,2月20-加利福尼亚州测测测测测测测测测测测测测结果为该州20-2020年的CRal-2020年测测测测测测测测测测测测测测测点结果。</s>