The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R₀) and the effectiveness of containment measures. We developed an ordinary differential equation-based Susceptible-Exposed-Infected-Recovery (SEIR) model with Bayesian underpinning to estimate the main parameter of R₀ determined by transmission coefficients, incubation period, and the recovery rate. Bayesian Markov Chain Monte Carlo (MCMC) estimation method was used to tackle the parameters of uncertainty resulting from the outbreak of COVID-19 given a small cohort of the cruise ship. The extended stratified SEIR model was also proposed to elucidate the heterogeneity of transmission route by the level of deck with passengers and crews. With the application of the overall model, R₀ was estimated as high as 5.70 (95% credible interval: 4.23–7.79). The entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 63% (95% credible interval: 60–66%) infected passengers. With the deck-specific SEIR model, the heterogeneity of R₀ estimates by each deck was noted. The estimated R₀ figures were 5.18 for passengers (5–14 deck), mainly from the within-deck transmission, and 2.46 for crews (2–4 deck), mainly from the between-deck transmission. Modelling the dynamic of COVID-19 on the cruise ship not only provides an insight into timely evacuation and early isolation and quarantine but also elucidates the relative contributions of different transmission modes on the cruise ship though the deck-stratified SEIR model.

钻石公主号游轮上爆发的 COVID-19 提供了一个前所未有的机会，可以通过基本繁殖数 (R ₀ ) 估计其原始传播能力和遏制措施的有效性。我们开发了一个基于常微分方程的易感暴露感染恢复 (SEIR) 模型，以贝叶斯为基础来估计 R _{0的主要参数}由透射系数、潜伏期和回收率决定。贝叶斯马尔可夫链蒙特卡洛 (MCMC) 估计方法用于解决由于一小群游轮而因 COVID-19 爆发而导致的不确定性参数。还提出了扩展的分层 SEIR 模型，以通过乘客和船员的甲板水平来阐明传播路径的异质性。随着整体模型的应用，R ₀估计高达 5.70（95% 可信区间：4.23–7.79）。没有进行遏制测量的整个流行期约为 47 天，并在指示病例一个月后达到高峰。部分遏制措施减少了 63%（95% 可信区间：60-66%）受感染的乘客。使用特定于甲板的 SEIR 模型，注意到每个甲板的 R _{0估计的异质性。}估计的 R ₀乘客（5-14 层）的数据为 5.18，主要来自甲板内传输，船员（2-4 层）的数据为 2.46，主要来自甲板间传输。对游轮上 COVID-19 的动态进行建模不仅可以深入了解及时疏散和早期隔离检疫，还可以通过甲板分层 SEIR 模型阐明游轮上不同传播模式的相对贡献。