Data from: Distinguishing between reservoir exposure and human-to-human transmission for emerging pathogens using case onset data

Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%–32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.目前，中东呼吸综合征冠状病毒（MERS-CoV）、甲型H5N1流感、甲型H7N9流感等病原体，正持续从动物宿主中引发散发性跨种传播导致的人间感染聚集性病例。由于尚未出现明确的人间流行态势，上述三种感染的基本再生数（R₀）均低于或趋近于1。然而，针对低R₀值的稳健聚集性病例估算方法仍存在迫切需求，以便于在各类新发传染病之间优先调配稀缺资源，并及时检测聚集性病例间及随时间推移出现的显著变化。本研究构建了一种推断性传播模型，可区分人传人的传播信号与直接跨种传播（如来自活禽市场或养殖场）的背景噪声。通过模拟实验证实，即便跨种暴露的时间趋势尚未完全明确，只要已知该传染病的代间隔以及跨种暴露突然下降的时间点（如活禽市场关停日期），本方法即可获得R₀的无偏估计值。将本方法应用于2013年中国境内三起最大规模的甲型H7N9流感暴发数据后，研究结果显示，总体而言有13%（95%可信区间1%~32%）的病例由人传人传播所致。针对三起聚集性病例的R₀估算结果分别为：上海0.19（95%可信区间0.01~0.49）、江苏0.29（95%可信区间0.03~0.73）以及浙江0.03（95%可信区间0.00~0.22）。若能通过在哨点市场开展大规模常规采样等方式，精准估算跨种暴露风险的时间趋势，则可更为精准地估算R₀的亚临界值。若出现R₀>1的同类毒株，本方法可实时提示存在持续人传人的传播态势，并给出明确表征的不确定性区间。