Data from: Estimating the phenology of elk brucellosis transmission with hierarchical models of cause-specific and baseline hazards

Understanding the seasonal timing of disease transmission can lead to more effective control strategies, but the seasonality of transmission is often unknown for pathogens transmitted directly. We inserted vaginal implant transmitters (VITs) in 575 elk (Cervus elaphus canadensis) from 2006 to 2014 to assess when reproductive failures (i.e., abortions or still births) occur, which is the primary transmission route of Brucella abortus, the causative agent of brucellosis in the Greater Yellowstone Ecosystem. Using a survival analysis framework, we developed a Bayesian hierarchical model that simultaneously estimated the total baseline hazard of a reproductive event as well as its 2 mutually exclusive parts (abortions or live births). Approximately, 16% (95% CI = 0.10, 0.23) of the pregnant seropositive elk had reproductive failures, whereas 2% (95% CI = 0.01, 0.04) of the seronegative elk had probable abortions. Reproductive failures could have occurred as early as 13 February and as late as 10 July, peaking from March through May. Model results suggest that less than 5% of likely abortions occurred after 6 June each year and abortions were approximately 5 times more likely in March, April, or May compared to February or June. In western Wyoming, supplemental feeding of elk begins in December and ends during the peak of elk abortions and brucellosis transmission (i.e., Mar and Apr). Years with more snow may enhance elk-to-elk transmission on supplemental feeding areas because elk are artificially aggregated for the majority of the transmission season. Elk-to-cattle transmission will depend on the transmission period relative to the end of the supplemental feeding season, elk seroprevalence, population size, and the amount of commingling. Our statistical approach allowed us to estimate the probability density function of different event types over time, which may be applicable to other cause-specific survival analyses. It is often challenging to assess the cause of death, or in this case whether the reproductive event was an abortion or live birth. Accounting for uncertainty in the event type is an important future addition to our methodological approach.

明晰疾病传播的季节性时间规律，可为制定更高效的防控策略提供支撑，但对于直接传播的病原体而言，其传播季节性往往难以获知。2006年至2014年间，研究团队为575头北美马鹿（*Cervus elaphus canadensis*，俗称麋鹿）植入了阴道植入式发射器（vaginal implant transmitters, VITs），以评估繁殖失败（即流产或死产）的发生时间——这正是大黄石生态系统中布鲁氏菌病的病原菌流产布鲁氏菌（*Brucella abortus*）的主要传播途径。本研究采用生存分析框架，构建了贝叶斯分层模型，可同时估算繁殖事件的总基线风险，以及该事件的两种互斥结局：流产或活产。
约16%的血清学阳性妊娠麋鹿出现繁殖失败（95%置信区间CI=0.10, 0.23），而血清学阴性麋鹿中约有2%（95%CI=0.01, 0.04）疑似发生流产。繁殖失败的发生时间最早可至2月13日，最晚可至7月10日，高发时段为3月至5月。模型结果显示，每年6月6日之后发生的疑似流产占比不足5%；且3、4、5月发生流产的概率约为2月或6月的5倍。
在怀俄明州西部，麋鹿补饲工作始于12月，于麋鹿流产和布鲁氏菌病传播的高峰期（即3月与4月）结束。降雪量更高的年份，补饲区域内的麋鹿间传播风险可能升高，因为在传播季的大部分时段，麋鹿会因人工补饲而聚集。麋鹿向牛的传播风险则取决于传播时段与补饲季结束时间的相对关系、麋鹿血清阳性率、种群规模以及混群程度。
本研究的统计方法可用于估算不同事件类型随时间变化的概率密度函数，该方法或可推广至其他病因特异性生存分析场景。评估死亡原因往往颇具挑战，本研究中则体现为难以判定繁殖事件究竟为流产还是活产。未来可将事件类型的不确定性纳入考量，这将是本研究方法体系的重要改进方向。