Predicting Ebola virus disease risk and the role of African bat birthing
Predicting Ebola virus disease risk and the role of African bat birthing
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DOI：
10.1016/j.epidem.2019.100366
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发表时间：
2019-12-01
期刊：
影响因子：
3.8
通讯作者：
Hayman, David T. S.
中科院分区：
文献类型：
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作者：
Hranac, C. Reed;Marshall, Jonathan C.;Hayman, David T. S.
埃博拉病毒病（EVD）对整个赤道非洲的公共卫生构成威胁。尽管有许多“溢出”事件进入人类和猿类，维修水库和溢出机制知之甚少。有证据表明果蝠在这两种情况下都发挥了作用，但数据仍然很少，而且蝙蝠表现出广泛的生活史特征。在这里，我们池稀疏的数据，并使用一种机械的方法来研究非洲果蝠，molossid蝙蝠，和非molossid microbats的生育周期通知EVD溢出的时空发生。我们创建合奏小生境模型来预测时空变化的蝙蝠分娩和模型爆发的时空泊松点过程。我们预测三个不同的年度生育模式非洲蝙蝠沿着纬度梯度。EVD溢出模型测试，最好的准赤池信息标准（qAIC）和样本预测包括显着的非洲蝙蝠出生相关的条款。时间蝙蝠生育条款适合在最好的模型为人类和动物的爆发与假设的病毒动力学在蝙蝠种群，但纯粹的空间模型也表现良好。我们的最佳模型预测，刚果民主共和国2018年两次EVD疫情发生地点的EVD溢出风险在撒哈拉以南非洲分析的所有25 x 25 km空间单元的前12-35%和0.1%范围内。结果表明，稀疏数据可以用来帮助理解复杂的系统。
Ebola virus disease (EVD) presents a threat to public health throughout equatorial Africa. Despite numerous 'spillover' events into humans and apes, the maintenance reservoirs and mechanism of spillover are poorly understood. Evidence suggests fruit bats play a role in both instances, yet data remain sparse and bats exhibit a wide range of life history traits. Here we pool sparse data and use a mechanistic approach to examine how birthing cycles of African fruit bats, molossid bats, and non-molossid microbats inform the spatio-temporal occurrence of EVD spillover. We create ensemble niche models to predict spatio-temporally varying bat birthing and model outbreaks as spatio-temporal Poisson point processes. We predict three distinct annual birthing patterns among African bats along a latitudinal gradient. Of the EVD spillover models tested, the best by quasiAkaike information criterion (qAIC) and by out of sample prediction included significant African bat birthrelated terms. Temporal bat birthing terms fit in the best models for both human and animal outbreaks were consistent with hypothesized viral dynamics in bat populations, but purely spatial models also performed well. Our best model predicted risk of EVD spillover at locations of the two 2018 EVD outbreaks in the Democratic Republic of the Congo was within the top 12-35% and 0.1% of all 25 x 25 km spatial cells analyzed in sub-Saharan Africa. Results suggest that sparse data can be leveraged to help understand complex systems.