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具有由感染性和疾病持续时间决定的时滞的传染病模型。

An epidemic model with time delays determined by the infectivity and disease durations.

机构信息

Peoples Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, Moscow 117198, Russia.

Department of Mathematics and Statistics, IIT Kanpur, Kanpur 208016, India.

出版信息

Math Biosci Eng. 2023 Jun 2;20(7):12864-12888. doi: 10.3934/mbe.2023574.

Abstract

We propose an epidemiological model with distributed recovery and death rates. It represents an integrodifferential system of equations for susceptible, exposed, infectious, recovered and dead compartments. This model can be reduced to the conventional ODE model under the assumption that recovery and death rates are uniformly distributed in time during disease duration. Another limiting case, where recovery and death rates are given by the delta-function, leads to a new point-wise delay model with two time delays corresponding to the infectivity period and disease duration. Existence and positiveness of solutions for the distributed delay model and point-wise delay model are proved. The basic reproduction number and the final size of the epidemic are determined. Both, the ODE model and the delay models are used to describe COVID-19 epidemic progression. The delay model gives a better approximation of the Omicron data than the conventional ODE model from the point of view of parameter estimation.

摘要

我们提出了一个具有分布式恢复和死亡率的流行病学模型。它代表了易感、暴露、感染、恢复和死亡 compartments 的积分微分方程组。在疾病持续期间,假设恢复和死亡率在时间上均匀分布的情况下,该模型可以简化为传统的 ODE 模型。另一个极限情况是,恢复和死亡率由 delta 函数给出,这导致了一个新的具有两个时滞的点态延迟模型,分别对应于感染期和疾病持续期。证明了分布式延迟模型和点态延迟模型的解的存在性和正定性。确定了基本再生数和流行病的最终规模。从参数估计的角度来看,ODE 模型和延迟模型都被用于描述 COVID-19 疫情的进展。延迟模型比传统的 ODE 模型更能更好地逼近奥密克戎数据。

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