预测病毒的动物感染潜力:我们在哪里?
Nardus Mollentze1, Daniel G Streicker1
1School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom; MRC-University of Glasgow Centre for Virus Research, G61 1QH, United Kingdom.
Current opinion in virology
|July 29, 2023
概括
预测模型可以帮助识别公共卫生准备的高风险病毒. 改进这些模型需要更好的数据生成和计算算法,以有效地预测病毒的出现.
科学领域:
- 病毒学 病毒学
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 识别和预测高风险病毒的出现对公共卫生至关重要.
- 大规模的病毒发现举措往往被早期干预的潜力证明是合理的.
- 病毒出现的预测建模是一个不断发展的领域,有道德考虑.
研究的目的:
- 审查病毒出现预测模型的现状.
- 根据其数据输入和实用性对预测模型进行分类.
- 评估影响公共卫生风险预测有效性的因素.
主要方法:
- 审查关于病毒发现和预测建模的现有文献.
- 根据数据输入,预测模型被分为三大类.
- 对实验室,现场和临床环境的数据生成需求的分析.
主要成果:
- 确定了病毒发现预测模型的三个主要类别.
- 对新发现的病毒进行分类的模型实用性根据数据输入而有所不同.
- 预测建模的进步取决于计算改进和高效的数据生成.
结论:
- 预测模型为识别高风险病毒和指导公共卫生准备提供了潜力.
- 在各种设置中增强数据生成对于提高模型准确性至关重要.
- 对动物传染病预测的研究为病毒出现因素提供了宝贵的见解.
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