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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
Confidence Intervals01:21

Confidence Intervals

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 confidence...

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Application of Ha-CoV-2 Pseudovirus for Rapid Quantification of SARS-CoV-2 Variants and Neutralizing Antibodies
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Application of Ha-CoV-2 Pseudovirus for Rapid Quantification of SARS-CoV-2 Variants and Neutralizing Antibodies

Published on: September 8, 2023

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.

David J Warne1,2, Abhishek Varghese2, Aidan Brewster1

  • 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

Plos One
|May 26, 2026
PubMed
Summary

Mathematical modeling can detect vaccine hesitancy during epidemics by analyzing case, death, and vaccination data. This approach helps understand drivers of hesitancy and inform public health strategies for better vaccine uptake.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Vaccination programs are crucial for controlling infectious diseases, especially during epidemics.
  • Vaccine hesitancy can significantly slow vaccination uptake, hindering disease control efforts.
  • Identifying vaccine hesitancy and its drivers is essential for effective public health interventions.

Purpose of the Study:

  • To explore the use of mathematical modeling of epidemiological data to detect vaccine hesitancy.
  • To identify potential drivers of vaccine hesitancy during a vaccination roll-out.
  • To develop and test a novel epidemiological model for analyzing community behavior.

Main Methods:

  • Developed a novel susceptible-exposed-infectious-recovered (SEIR) model incorporating behavioral changes.
  • The model accounts for non-pharmaceutical interventions and vaccine uptake influenced by reported data.
  • Utilized a Bayesian approach to analyze simulated data under various vaccine hesitancy scenarios.

Main Results:

  • Individual parameters driving vaccine hesitancy were often unidentifiable.
  • Posterior correlation structures effectively detected the presence of vaccine hesitancy.
  • Insights were gained into the relative influence of factors like vaccine safety concerns and complacency.

Conclusions:

  • Mathematical modeling of epidemiological data is a viable tool for detecting vaccine hesitancy.
  • While specific drivers may be hard to pinpoint, the model can reveal the existence and general influences of hesitancy.
  • The developed methods are generalizable to various infectious diseases, aiding adaptive vaccination strategies.