相关实验视频

Updated: Jun 23, 2025

Generation, Amplification, and Titration of Recombinant Respiratory Syncytial Viruses
11:48

Generation, Amplification, and Titration of Recombinant Respiratory Syncytial Viruses

Published on: April 4, 2019

15.6K

基于诊断编码的RSV发病率的模型校正,针对美国0-4岁儿童

Sabina O Nduaguba1,2,3, Phuong T Tran3,4, Almut G Winterstein5,6,7

  • 1Department of Pharmaceutical Systems and Policy, College of Pharmacy, West Virginia University, Morgantown, WV, USA.

BMC infectious diseases
|June 21, 2024
PubMed
概括

行政索赔数据低估了呼吸道共囊病毒 (RSV) 下呼吸道感染 (RSV-LRTI). 使用RSV监测调整索赔数据可以提高准确性,但在特定数据无法获得时,建议跨设置建模.

关键词:
医疗保健数据 医疗保健数据疾病的国际分类.国家呼吸道和肠道病毒监测系统在RSV流行病学.呼吸系统同胞性病毒

更多相关视频

An Improved and High Throughput Respiratory Syncytial Virus RSV Micro-neutralization Assay09:14

An Improved and High Throughput Respiratory Syncytial Virus RSV Micro-neutralization Assay

Published on: January 26, 2019

10.8K
Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers09:16

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

Published on: March 14, 2018

10.2K

相关概念视频

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
238
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:
349
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
455
Rous Sarcoma Virus (RSV) and Cancer01:03

Rous Sarcoma Virus (RSV) and Cancer

Rous Sarcoma virus or RSV was discovered by F. Peyton Rous in the year 1911 as a filterable transmissible agent that could cause tumors in chickens. He won a Nobel Prize for this discovery in 1966. His experiments clearly demonstrated that some cancers could be caused by infectious agents and led to the discovery of many more cancer-causing viruses in animals as well as humans.
RSV is a retrovirus that contains two copies of a plus-strand  RNA genome. Its genome consists of four main open...
5.1K
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
150
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
162