Confronting data sparsity to identify potential sources of Zika virus spillover infection among primates
Confronting data sparsity to identify potential sources of Zika virus spillover infection among primates
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DOI：
10.1016/j.epidem.2019.01.005
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发表时间：
2019-06-01
期刊：
影响因子：
3.8
通讯作者：
Varshney, Kush R.
中科院分区：
文献类型：
--
作者：
Han, Barbara A.;Majumdar, Subhabrata;Varshney, Kush R.
最近在美洲爆发的寨卡病毒(ZIKV)疫情是现代最大的疫情之一。像其他蚊媒传播的黄病毒一样，ZIKV在灵长类动物中以森林周期循环传播，可以作为向人类溢出感染的蓄水池。确定高山水库对于减少溢出风险至关重要，但对这一人畜共患病和大多数其他人畜共患病的相关监测和生物学数据仍然有限。我们通过将机器学习方法--贝叶斯多标签学习与灵长类特征的多重归因方法相结合来解决这种数据稀疏性问题。由此产生的模型识别出黄病毒阳性的灵长类动物的准确率为82%，并表明构成最大溢出风险的物种也是最适合人类栖息地的物种之一。考虑到描述动物宿主的普遍数据稀疏性，以及在涉及新的或新出现的人畜共患病的场景中数据稀疏性的虚拟保证，我们表明计算方法在从现有数据中提取可操作的推断以支持改进的流行病学反应和预防是有用的。
The recent Zika virus (ZIKV) epidemic in the Americas ranks among the largest outbreaks in modern times. Like other mosquito-borne flaviviruses, ZIKV circulates in sylvatic cycles among primates that can serve as reservoirs of spillover infection to humans. Identifying sylvatic reservoirs is critical to mitigating spillover risk, but relevant surveillance and biological data remain limited for this and most other zoonoses. We confronted this data sparsity by combining a machine learning method, Bayesian multi-label learning, with a multiple imputation method on primate traits. The resulting models distinguished flavivirus-positive primates with 82% accuracy and suggest that species posing the greatest spillover risk are also among the best adapted to human habitations. Given pervasive data sparsity describing animal hosts, and the virtual guarantee of data sparsity in scenarios involving novel or emerging zoonoses, we show that computational methods can be useful in extracting actionable inference from available data to support improved epidemiological response and prevention.