一个流行病模型,其时间延迟取决于传染性和疾病持续时间
Masoud Saade1, Samiran Ghosh2, Malay Banerjee2
1Peoples Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, Moscow 117198, Russia.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
我们开发了一种新的流行病学模型,包括分布式康复和死亡率,它比传统模型更接近COVID-19 Omicron变异数据. 这种增强的模型为传染病动态提供了改进的参数估计.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 传染病建模 传染病建模
背景情况:
- 传统的流行病学模型经常使用简化的疾病进展假设.
- 了解分布式康复和死亡率的影响对于准确的流行病预测至关重要.
- 现有的模型可能无法完全捕捉疾病动态的细微差别,特别是在变种出现期间.
研究的目的:
- 提出和分析一种新的流行病学模型,以分布式的恢复和死亡率.
- 调查拟议模型的数学属性,包括解决方案的存在和正性.
- 为了比较新模型与传统的ODE模型在描述COVID-19进展,特别是Omicron变异的表现.
主要方法:
- 构建一个整体差异系统,表示易受,暴露,感染,恢复和死亡的部分.
- 限定案例的推导:一个传统的ODE模型 (统一率) 和一个点wise延迟模型 (三角函数率).
- 数学证明分布式和点wise延迟模型的存在和解决方案的积极性.
- 确定基本的繁殖数和最终的流行病规模.
- 使用COVID-19 Omicron变种数据应用和比较ODE和延迟模型.
主要成果:
- 拟议的模型包括传统的ODE和新的延迟模型作为限制情况.
- 对于延迟模型,解决方案的存在和积极性是数学上确定的.
- 与传统的ODE模型相比,延迟模型对COVID-19 Omicron变种数据的近似性更高,特别是在参数估计方面.
结论:
- 结合分布式康复和死亡率的流行病学模型提供了更精细的疾病动态表现.
- 开发的延迟模型为拟合现实世界流行病数据提供了更高的准确性,例如COVID-19 Omicron变种.
- 这项工作强调了考虑疾病发病率的时间异质性对于改善流行病建模和预测的重要性.
更多相关视频
12:21A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
Published on: September 28, 2022
2.5K
10:11Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
Published on: September 27, 2014
36.3K
相关概念视频
01:18Steps in Outbreak Investigation
152
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152
01:20Infection
8.1K
When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...
8.1K
01:21Causality in Epidemiology
482
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
482
01:26Stages of Infection
57.1K
Stages of infection describe what happens to a susceptible host once a pathogen invades the human body. The stages of infection are incubation, prodromal, illness, stage of decline, and convalescence. The incubation stage is the period from exposure to a pathogen until symptoms start. The infected person is unaware of impending illness as the pathogens grow and multiply within the body. The duration may vary depending on the type of infection. The incubation period of measles averages ten to...
57.1K
01:18Models of Health Promotion and Illness Prevention II
1.6K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
The agent-host-environment model states that disease results...
1.6K
01:14Parametric Survival Analysis: Weibull and Exponential Methods
478
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
478