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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.7K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.4K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.1K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.7K

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

First Mosquito-Based Molecular Evidence of Tembusu Virus in Vietnam.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Preparing Europe for imported Andes virus: the role of rapid serology and molecular diagnostics.

The Lancet regional health. Europe·2026
Same author

Severe dengue in hospitalized adults at two tertiary referral hospitals in northern Vietnam: clinical features and outcomes.

Tropical medicine and health·2026
Same author

Metagenomic profiling reveals shared resistome signatures between humans and pigs in Vietnamese smallholder farms.

npj antimicrobials and resistance·2026
Same author

Bundibugyo Ebola: why preparedness still fails at the point of detection.

The Lancet. Infectious diseases·2026
Same author

When rare zoonoses travel: Andes virus, hantavirus cardiopulmonary syndrome, and the preparedness gap.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026

相关实验视频

Updated: Feb 27, 2026

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

7.2K

神经参数校准用于登革热爆发预测.

Hoang Viet Pham1, Khuong Trung Dang Nguyen1, Thirumalaisamy P Velavan2,3,4

  • 1Faculty of Engineering, Vietnamese-German University, Ho Chi Minh City, Vietnam.

PloS one
|February 25, 2026
PubMed
概括

神经参数校准 (NPC) 提供了一个更快,更准确的方法来估计登革热传播模型中的参数. 这种计算方法有助于及时响应公共卫生问题,特别是在资源有限的地区.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

14.4K

相关实验视频

Last Updated: Feb 27, 2026

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

7.2K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

14.4K

科学领域:

  • 流行病学 流行病学
  • 计算生物学 计算生物学
  • 数学建模的数学建模

背景情况:

  • 登革热是热带和亚热带地区的重大公共卫生问题.
  • 病毒和宿主因素之间的复杂相互作用驱动着登革热传播动态.
  • 计算模型,通常使用普通微分方程 (ODEs),对于理解这些动态至关重要.

研究的目的:

  • 评估神经参数校准 (NPC) 用于估计登革热传播的扩展分区模型 (ECM) 中的参数.
  • 将NPC的计算效率和准确性与传统的马尔科夫链蒙特卡洛 (MCMC) 方法进行比较.
  • 通过使用来自南美和东南亚的真实世界登革热监测数据来验证ECM-NPC方法.

主要方法:

  • 开发了一个包括七个ODE的扩展隔间模型 (ECM) 来描述人类和蚊子登革热的传播.
  • 采用神经参数校准 (NPC),利用神经网络来学习模型参数后部分布.
  • 分析了来自三个南美城市和三个东南亚国家的六个登革热监测数据集.

主要成果:

  • 与MCMC相比,NPC表现出明显更快的计算时间 (例如,国家级数据的368s与2998s).
  • 对于城市和国家数据集,NPC实现了与MCMC可比的准确性,平均平方误差 (MSE) 值较低.
  • 结合ECM和NPC的方法在登革热爆发预测方面被证明是有效的.

结论:

  • 扩展隔间模型与神经参数校准的整合提供了准确的登革热爆发预测.
  • 这种方法可以大幅降低计算成本,使其成为公共卫生的实用工具.
  • 对于在资源有限的环境中支持及时干预,ECM-NPC方法特别有价值.