The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. We propose a Beta-Dirichlet switching state-space transmission model to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As time evolves, the switching mechanism introduced in the susceptible-exposed-infected-recovered (SEIR) model is able to capture the timing and magnitude of changes in the transmission rate due to the effectiveness of control measures. The implementation of this model is based on a particle Markov Chain Monte Carlo algorithm, which can estimate the time evolution of SEIR states, switching states, and high-dimensional parameters efficiently. The efficacy of our model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, it indicates approximately a 66.6\% reduction of transmission rate following interventions such as distancing, closures and vaccination. Our proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating of the effectiveness of interventions during the spread of the disease.

翻译：传染病的有效控制依赖于对干预措施影响的准确评估，而这种评估常因疾病传播的复杂动态而受阻。我们提出一种Beta-Dirichlet切换状态空间传播模型，以同时追踪疾病的潜在动态并评估干预措施的有效性。随着时间推移，在易感-暴露-感染-康复（SEIR）模型中引入的切换机制能够捕捉因控制措施有效性而导致的传播率变化的时机与幅度。该模型的实现基于粒子马尔可夫链蒙特卡洛算法，可高效估计SEIR状态、切换状态及高维参数的时变过程。通过模拟研究验证了模型及估计方法的有效性。在不列颠哥伦比亚省COVID-19疫情的实际应用中，该模型表明在社交距离、封控及疫苗接种等干预措施实施后，传播率降低了约66.6%。我们提出的模型为研究疾病传播期间的潜在动态并评估干预措施的有效性提供了一种有力工具，可为公共卫生政策提供参考依据。