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感染密度引起的额外查和治疗和对COVID-19的影响:建模和成本有效的最佳控制

Sonu Lamba¹, Tanuja Das^1,2, Prashant K Srivastava¹

¹Department of Mathematics, Indian Institute of Technology Patna Bihta - 801106, Patna, Bihar, India.

Infectious Disease Modelling

|April 1, 2024

概括

这项研究开发了一种新的COVID-19模型,以寻找具有成本效益的控制措施. 促进感染密度诱导的额外查 (IDIAS) 是可持续疾病管理的最经济的策略.

科学领域:

流行病学流行病学
数学建模的数学建模
公共卫生公共卫生

背景情况:

COVID-19 管理需要有效和经济高效的战略.
现有的模型可能无法完全捕捉复杂的动态,如再感染和治疗和.
了解最佳的控制措施对于疫情应对至关重要.

研究的目的:

为COVID-19动态引入和分析一种新的SI2HR模型.
调查疾病管理的经济有效的最佳控制策略.
评估不同干预措施的经济效率.

主要方法:

开发了一个新的SI2HR数学模型,包括无症状/症状感染,感染密度诱导的额外查 (IDIAS),治疗和,再感染和免疫力下降.
使用来自香港的真实世界COVID-19数据对模型进行了校准.
通过Pontryagin的最大原则和前后扫描方法,制定并解决了一个最佳控制问题 (OCP).

主要成果:

查,治疗和隔离的延迟增加了基本繁殖数 (R0),促进了流行性.
加强查,治疗和预防干预措施有助于可持续的疾病控制.
通过成本效益分析 (CEA),仅仅促进IDIAS已成为最具成本效益的战略.

结论:

优化查,治疗和隔离率对于减轻疾病负担至关重要.
感染密度诱导的额外查 (IDIAS) 为COVID-19控制提供了一种非常具有成本效益的方法.
这些发现为决策者提供了一个框架,以实施有效的流行病管理策略.

关键词:

在 COVID-19 疫情中,成本效益分析的成本效益分析估计估计估计他们的想法.最佳的控制控制是最好的控制.预测预测预测

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