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预测COVID-19的传播使用SIR模型增强了包含隔离和测试的模型

Nikhil Anand¹, A Sabarinath¹, S Geetha¹

¹Vikram Sarabhai Space Centre, Trivandrum, Kerala India.

Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

|April 16, 2024

概括

这项研究引入了修改后的SIR模型,用于预测印度的COVID-19传播,并结合了测试和隔离数据. 该模型表明,喀拉拉邦可以在7月份控制大流行,但由于R0 > 1,印度需要继续封锁.

科学领域:

流行病学流行病学
数学建模的数学建模
传染病的动态传染病的动态

背景情况:

由于COVID-19的流行,印度需要采取诸如全国性封锁等公共卫生干预措施.
分区模型,如SIR模型,对于了解疾病传播至关重要.
标准SIR模型通常假定人口混合均,这可能不反映现实世界的情况.

研究的目的:

提出修改后的SIR模型,对已检测和隔离的感染者进行核算.
克服传统SIR模型的同质混合假设.
分析印度COVID-19封锁的影响,并预测未来的流行病轨迹.

主要方法:

开发了一个修改后的SIR模型,包括测试和隔离率.
利用了2020年4月/5月起的喀拉拉邦和印度的COVID-19病例数据.
公式参数估计作为一个优化问题,使用最小二次成本函数.
采用微分演变优化器来解决优化问题.

主要成果:

修改后的模型通过解决同质混合,为疾病建模提供了更现实的方法.
分析量化了封锁对感染趋势的影响.
模型预测表明,通过干预,到7月初在喀拉拉邦成功控制了流行病.
印度的基本繁殖数 (R0) 仍然高于1,这表明传染风险持续存在.

结论:

修改后的SIR模型为传染病动态提供了更好的预测.
印度各地继续实施封锁措施是有必要的,以控制COVID-19的传播.
有针对性的干预措施可以导致大流行病的控制,克拉拉的预计轨迹就是一个例子.

关键词:

在 COVID-19 疫情中,印度印度印度喀拉拉邦喀拉拉邦喀拉拉邦一个流行病的流行病.隔离隔离隔离隔离隔离隔离隔离模型SIR 模型SIR 测试测试测试测试

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