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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Measures of Central Tendency02:16

Measures of Central Tendency

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The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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:  
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Updated: Sep 5, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis.

Usman Naseem1, Imran Razzak2, Matloob Khushi1

  • 1School of Computer ScienceThe University of Sydney Ultimo NSW 2006 Australia.

IEEE Transactions on Computational Social Systems
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Early social media data reveals public panic and evolving opinions during the COVID-19 pandemic. Analysis of 90,000 tweets highlights the need for proactive public health communication to combat misinformation.

Keywords:
COVID-19Twitterepidemicmisinformationopinion miningpandemicsentiment analysistext mining

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Area of Science:

  • Public Health
  • Social Media Analysis
  • Computational Social Science

Background:

  • The COVID-19 pandemic triggered widespread social media activity, leading to concerns about mass hysteria and misinformation.
  • Assessing early information dissemination and public opinion shifts on social media is crucial for understanding pandemic responses.

Purpose of the Study:

  • To analyze early information flows and public sentiment regarding COVID-19 on social media.
  • To inform policy decisions on social media moderation and public health communication strategies.

Main Methods:

  • Creation of a large-scale sentiment dataset (COVIDSENTI) with 90,000 COVID-19-related tweets from February-March 2020.
  • Sentiment classification of tweets into positive, negative, and neutral categories using various features and classifiers.

Main Results:

  • Negative opinions significantly influenced public sentiment during the early pandemic.
  • Initial public support for lockdowns observed, with sentiment shifting by mid-March.
  • The COVIDSENTI dataset provides valuable insights into early pandemic public opinion.

Conclusions:

  • A proactive and agile public health presence is essential to counter negative sentiment on social media during pandemics.
  • Effective social media moderation policies are needed to address misinformation.
  • Understanding public opinion dynamics on social media is key to pandemic management.