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. A Beta-Dirichlet switching state-space transmission model is proposed 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 the proposed model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, the proposed switching state-space transmission model quantifies the reduction of transmission rate following interventions. The proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating 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，而疾病传播的复杂动态往往阻碍了这一过程。本文提出了一种Beta-Dirichlet切换状态空间传播模型，可同时追踪疾病的潜在动态并评估干预措施的有效性。随时间推移，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导致的传播率变化的时间和幅度。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间演化。通过模拟研究验证了所提模型与估计方法的有效性。在实际应用中，针对不列颠哥伦比亚省的COVID-19疫情，所提出的切换状态空间传播模型量化了干预措施后传播率的降低程度。该模型为研究疾病传播期间的潜在动态、评估干预措施有效性提供了有力工具，可助力公共卫生政策的制定。