对于使用具有突变的易感-感染-恢复模型的疾病,发生宿主切换的罕见事件
Yannick Feld1,2, Alexander K Hartmann1
1Carl von Ossietzky Universität Oldenburg, Institut für Physik, 26111 Oldenburg, Germany.
Physical review. E
|February 20, 2026
概括
这项研究模拟了从动物宿主到人类的病原体进化,使用新奇的罕见事件算法计算疾病出现和流行病严重程度的概率.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数学建模的数学建模
背景情况:
- 人类的疾病出现往往源于动物的水库.
- 以前的模型通常从人类感染开始,忽视了病原体的适应.
- 了解主机切换动态对于疫情准备至关重要.
研究的目的:
- 从数值上研究从动物转移到人类的宿主疾病动态.
- 模拟完整的过程,包括病原体突变和流行病爆发.
- 量化动物传染病溢出事件和疾病严重程度的概率.
主要方法:
- 在人狗网络上的易受感染-恢复 (SIR) 模型.
- 数字大偏差技术,特别是1/t王兰道算法.
- 计算极小的概率 (低至10^-120) 和概率密度函数.
主要成果:
- 成功模拟了从非人类宿主到人类的病原体进化.
- 量化了宿主切换的概率和随后的流行病潜力.
- 获得了人类感染分数和疫情严重程度的完整概率密度函数.
结论:
- 这项研究为分析动物传播疾病的出现提供了一个新的框架.
- 罕见事件算法能够解决风险评估至关重要的极低概率事件.
- 该方法允许全面描述疾病爆发及其严重程度.
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