相关实验视频

Updated: Jul 7, 2025

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1
12:00

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1

Published on: April 8, 2009

10.3K

在全球和大陆范围内使用物流和戈珀茨数学模型对麻疹感染的比较流行病学评估

Obert Marín-Sánchez1, Pedro Pesantes-Grados2, Luis Pérez-Timaná3

  • 1Departamento Académico de Microbiología Médica, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Av. Carlos Germán Amezaga 375, Lima 15081, Peru.

Vaccines
|December 23, 2023
PubMed
概括

数学模型分析了人类病毒 (HMPX) 流行病. 戈珀茨模型最适合HMPX病例数据,而物流模型准确估计了基本复制数 (R0).

关键词:
戈珀茨函数是指一个函数.基本复制编号基本复制编号一个案例,一个案例.的确定系数.关键时间 关键时间关键时间逻辑回归的逻辑回归数学建模的数学建模的水是的水.

更多相关视频

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 307:35

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 3

Published on: April 13, 2009

8.2K
Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 210:55

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 2

Published on: April 10, 2009

8.1K

相关概念视频

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
195
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
441
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...
43
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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:
131
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
371
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
149