Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.
Travel Med Infect Dis. 2018 May-Jun;23:80-86. doi: 10.1016/j.tmaid.2018.04.004. Epub 2018 Apr 10.
In an influenza pandemic, arrival times of cases are a proxy of the epidemic size and disease transmissibility. Because of intense surveillance of travelers from infected countries, detection is more rapid and complete than on local surveillance. Travel information can provide a more reliable estimation of transmission parameters.
We developed an Approximate Bayesian Computation algorithm to estimate the basic reproduction number (R) in addition to the reporting rate and unobserved epidemic start time, utilizing travel, and routine surveillance data in an influenza pandemic. A simulation was conducted to assess the sampling uncertainty. The estimation approach was further applied to the 2009 influenza A/H1N1 pandemic in Mexico as a case study.
In the simulations, we showed that the estimation approach was valid and reliable in different simulation settings. We also found estimates of R and the reporting rate to be 1.37 (95% Credible Interval [CI]: 1.26-1.42) and 4.9% (95% CI: 0.1%-18%), respectively, in the 2009 influenza pandemic in Mexico, which were robust to variations in the fixed parameters. The estimated R was consistent with that in the literature.
This method is useful for officials to obtain reliable estimates of disease transmissibility for strategic planning. We suggest that improvements to the flow of reporting for confirmed cases among patients arriving at different countries are required.
在流感大流行期间，病例的到达时间是流行规模和疾病传染性的一个代理指标。由于对来自感染国家的旅行者进行了密集监测，因此检测比本地监测更迅速和完整。旅行信息可以提供更可靠的传播参数估计。
我们开发了一种近似贝叶斯计算算法，除了报告率和未观察到的流行开始时间外，还利用旅行和常规监测数据来估计基本繁殖数 (R)。进行了一项模拟以评估抽样不确定性。该估计方法进一步应用于墨西哥的 2009 年甲型 H1N1 流感大流行作为案例研究。
在模拟中，我们表明该估计方法在不同的模拟设置下是有效和可靠的。我们还发现，在墨西哥 2009 年的流感大流行中，R 和报告率的估计值分别为 1.37（95%可信区间[CI]：1.26-1.42）和 4.9%（95% CI：0.1%-18%），这在固定参数的变化下是稳健的。估计的 R 与文献中的值一致。
该方法可用于官员为战略规划获得可靠的疾病传播估计值。我们建议需要改进到达不同国家的确诊病例报告流程。
