针对印度COVID-19大流行的最佳时间依赖的SUC模型
Youngjin Hwang1, Soobin Kwak1, Jyoti2
1Department of Mathematics, Korea University, Seoul, 02841, State, Republic of Korea.
BMC infectious diseases
|September 28, 2024
概括
本研究引入了一种数值算法,用于估计依赖时间的可感性-未识别感染者-确诊 (tSUC) 模型的最佳流行病参数. 该方法适应性地计算了依赖时间的传染率,用于传染病建模,这对于分析印度COVID-19等流行病至关重要.
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:
- 易受感染-未确诊-确诊 (tSUC) 模型对于了解传染病动态,特别是未确诊病例至关重要.
- 流行病的参数,特别是传播率,由于干预和疾病进展,可能会有很大的变化.
- 准确估计时间依赖的传播率对于有效的流行病应对至关重要.
研究的目的:
- 提出一种新的数值算法,用于在依赖时间的 tSUC 模型中确定最佳的流行病参数.
- 用现实数据,特别是累计确诊病例,以适应性地估计依赖时间的传播率.
- 为了验证算法的性能,准确地建模传染病的传播.
主要方法:
- 开发一个数值算法来估计 tSUC 模型的最佳参数.
- 根据确诊病例数据中识别的线性变化点对时间依赖的传输速率的自适应估计.
- 预处理和平滑印度累计确诊病例数据,以确定关键变化点.
- 改变点之间的传输速率的插曲,并使用最小平方方法将差异最小化.
主要成果:
- 拟议的算法成功计算了 tSUC 模型的最佳时间依赖参数.
- 数字实验验证了算法的准确表示确诊病例动态的能力.
- 该算法为印度的COVID-19流行病提供了可靠的依赖时间的传播率.
结论:
- 开发的数值算法有效地估计了tsuc模型的最佳时间依赖的流行病参数.
- 这种方法为分析传染病传播提供了一种可靠的方法,特别是对于时间变化的速率.
- 计算的依赖时间的传播率可以为公共卫生策略和流行病分析提供信息,以印度的COVID-19数据为例.
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