Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion
Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion
复制标题
DOI：
10.3390/math11092167
复制
发表时间：
2023-05
期刊：
影响因子：
2.4
通讯作者：
Victoria Chebotaeva;P. Vasquez
中科院分区：
文献类型：
--
作者：
Victoria Chebotaeva;P. Vasquez
我们探讨了交叉扩散动力学在流行病模型中的作用。使用传染病的反应扩散模型，我们明确考虑的情况下，一个类别中的个人将根据其他类别中的个人的浓度移动。也就是说，我们对易感个体远离感染者和传染性个体进行建模。在这里，我们表明，包括这些交叉扩散动力学的结果在一个流行病的发病延迟和增加的传染性个人的总数。这种表示在Erlang SEIR模型中提供了更真实的疾病类别的时空动态，并使我们能够研究由于社会行为导致的空间流动性如何影响流行病的传播。我们发现，有针对性的控制措施，如有针对性的检测、接触者追踪和隔离感染者，可以更有效地减缓传染病的传播，同时最大限度地减少对社会和经济的负面影响。
We explore the effects of cross-diffusion dynamics in epidemiological models. Using reaction–diffusion models of infectious disease, we explicitly consider situations where an individual in a category will move according to the concentration of individuals in other categories. Namely, we model susceptible individuals moving away from infected and infectious individuals. Here, we show that including these cross-diffusion dynamics results in a delay in the onset of an epidemic and an increase in the total number of infectious individuals. This representation provides more realistic spatiotemporal dynamics of the disease classes in an Erlang SEIR model and allows us to study how spatial mobility, due to social behavior, can affect the spread of an epidemic. We found that tailored control measures, such as targeted testing, contact tracing, and isolation of infected individuals, can be more effective in mitigating the spread of infectious diseases while minimizing the negative impact on society and the economy.