Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
127
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
87
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

228
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
228
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

207
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:
207
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

112
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
112

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Data-driven modeling of imported malaria in Morocco and the impact of population migration.

Mathematical biosciences and engineering : MBE·2026
Same author

Fear effect on the mobility of individuals in a spatially heterogeneous environment: a delayed diffusive SPIR epidemic model.

Scientific reports·2025
Same author

Dynamics and asymptotic profiles of a local-nonlocal dispersal SIR epidemic model with spatial heterogeneity.

Infectious Disease Modelling·2025
Same author

A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities.

Infectious Disease Modelling·2024
Same author

Optimal control and stability analysis of an age-structured SEIRV model with imperfect vaccination.

Mathematical biosciences and engineering : MBE·2023
Same author

Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps.

Infectious Disease Modelling·2022

相关实验视频

Updated: Sep 13, 2025

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection
08:14

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection

Published on: October 6, 2015

10.4K

使用数据驱动方法分析疟疾的数学模型.

Adithya Rajnarayanan1, Manoj Kumar1, Abdessamad Tridane2

  • 1School of Engineering and Science, Indian Institute of Technology Madras Zanzibar, PO Box 394, Bweleo, Zanzibar, Urban West, 71215, Tanzania.

Scientific reports
|July 27, 2025
PubMed
概括

这项研究使用温度和海拔高度等环境因素来模拟疟疾传播. 它引入了一种新的基于物理的机器学习方法,用于更好的预测和实时风险评估.

关键词:
分区模型是一个分区模型.数据驱动的方法数据驱动的方法动态模式分解分解疟疾模型 疟疾模型神经网络的神经网络的神经网络

更多相关视频

Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages
08:23

Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages

Published on: May 30, 2019

11.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.9K

相关实验视频

Last Updated: Sep 13, 2025

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection
08:14

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection

Published on: October 6, 2015

10.4K
Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages
08:23

Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages

Published on: May 30, 2019

11.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.9K

科学领域:

  • 流行病学 流行病学
  • 数学建模的数学建模
  • 环境科学 环境科学

背景情况:

  • 疟疾是全球主要的健康负担,每年导致数百万病例和死亡.
  • 了解疾病传播动态对于有效的公共卫生干预至关重要.

研究的目的:

  • 开发一种用于建模疟疾传播动态的新型框架.
  • 将环境因素 (温度,高度) 整合到一个分区SIR-SI模型中.
  • 为了提高疟疾传播模型的现实性和预测准确性.

主要方法:

  • 开发了一个新的传输功能,结合了温度和高度的依赖性.
  • 进行了稳定状态分析,以确定疾病平衡的稳定性标准.
  • 使用人工神经网络 (ANN),循环神经网络 (RNN) 和物理信息神经网络 (PINN) 的比较学习框架进行参数估计.
  • 实现了动态模式分解 (DMD),以创建数据驱动的传输风险指数.

主要成果:

  • 建立了无病和特有平衡的稳定性标准.
  • 通过嵌入流行病学动态,物理信息神经网络 (PINNs) 展示了优越的预测性能.
  • 从感染数据中使用DMD推导出一种新的,可解释的传播风险指数.

结论:

  • 新的框架通过整合环境因素来增强疟疾传播建模的现实性.
  • 使用PINNs进行物理约束参数推断显著提高了预测准确性.
  • 数据驱动的传播风险指数为实时疟疾风险评估提供了有价值的工具.