Li Peiji, Dai Mengmeng, Wang Yayi, Liu Yingbo
Department of Biostatistics, China Pharmaceutical University, Nanjing, China.
Infect Dis Model. 2025 May 6;10(3):946-959. doi: 10.1016/j.idm.2025.05.002. eCollection 2025 Sep.
Influenza remains a global challenge, imposing a significant burden on society and the economy. Many influenza cases are asymptomatic, leading to greater uncertainty and the under-reporting of cases in influenza transmission and preventing authorities from taking effective control measures. In this study, we propose a Bayesian hierarchical approach to model and correct under-reporting of influenza cases in Hong Kong, incorporating a discrete-time stochastic, Susceptible-Infected-Recovered-Susceptible (DT-SIRS) model that allows transmission rate to vary over time. The incidence of influenza exhibits seasonality. To examine the relationship between meteorological factors and seasonal influenza activity in subtropical areas, five meteorological factors are included in the model. The proposed model explores the effects of meteorological factors on transmission rates and disease detection covariates on under-reporting, and the inclusion of the DT-SIRS model enables more accurate inference regarding true disease counts. The results demonstrate that under-reporting rates of influenza cases vary significantly in different years and epidemic seasons. In conclusion, our method effectively captures the dynamic behavior of the disease, and we can accurately estimate under-reporting and provide new possibilities for early warning of influenza based on meteorological data and routine surveillance data.
流感仍然是一项全球性挑战，给社会和经济带来重大负担。许多流感病例是无症状的，这导致流感传播中的不确定性增加以及病例报告不足，使得当局无法采取有效的控制措施。在本研究中，我们提出一种贝叶斯分层方法来建模和校正香港流感病例的报告不足情况，纳入了一个离散时间随机易感-感染-康复-易感（DT-SIRS）模型，该模型允许传播率随时间变化。流感发病率呈现季节性。为了研究亚热带地区气象因素与季节性流感活动之间的关系，模型中纳入了五个气象因素。所提出的模型探讨了气象因素对传播率的影响以及疾病检测协变量对报告不足的影响，并且纳入DT-SIRS模型能够对真实疾病计数进行更准确的推断。结果表明，流感病例的报告不足率在不同年份和流行季节有显著差异。总之，我们的方法有效地捕捉了疾病的动态行为，并且能够准确估计报告不足情况，并基于气象数据和常规监测数据为流感早期预警提供新的可能性。
