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Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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 + error bound)
The...
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...
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...
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 condition at a...
Coronavirus01:29

Coronavirus

Coronaviruses, including the severe acute respiratory syndrome coronavirus (SARS-CoV), are enveloped viruses characterized by their single-stranded, positive-sense RNA genome and helical nucleocapsid structure. The hallmark of these viruses is their club-shaped spike (S) glycoproteins that protrude from the viral envelope, facilitating attachment to host cells. Typically, coronaviruses infect the upper respiratory tract, often causing mild or asymptomatic disease. However, certain strains like...

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Detection of SARS-CoV-2 Neutralizing Antibodies using High-Throughput Fluorescent Imaging of Pseudovirus Infection
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Estimating SARS-CoV-2 seroprevalence.

Samuel P Rosin1, Bonnie E Shook-Sa1, Stephen R Cole2

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|December 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical methods to accurately estimate COVID-19 (severe acute respiratory syndrome coronavirus 2) seroprevalence, addressing errors in antibody tests and sampling biases for reliable public health guidance.

Keywords:
COVID-19diagnostic testsestimating equationsseroepidemiologic studiesstandardization

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Seroprevalence studies are crucial for public health responses to pandemics like COVID-19.
  • Existing seroprevalence surveys face challenges from inaccurate diagnostic tests and biased sampling methods.
  • Accurate estimation of SARS-CoV-2 antibody prevalence is essential for understanding pandemic spread.

Purpose of the Study:

  • To develop and evaluate statistical estimators for seroprevalence that correct for assay misclassification error and selection bias.
  • To provide reliable methods for estimating the proportion of the population with SARS-CoV-2 antibodies.
  • To compare the performance of proposed estimators through simulations and real-world data.

Main Methods:

  • Utilized non-parametric and parametric statistical estimators for seroprevalence.
  • Incorporated validation data to adjust for serologic assay misclassification.
  • Employed covariate-defined strata to address non-probability sampling biases.
  • Derived consistent variance estimators for the proposed methods.

Main Results:

  • The proposed seroprevalence estimators were demonstrated to be consistent and asymptotically normal.
  • Simulation studies showed the estimators performed well across various scenarios.
  • Applied methods to estimate SARS-CoV-2 seroprevalence in New York City, Belgium, and North Carolina.

Conclusions:

  • The developed statistical methods offer a robust approach to estimating seroprevalence, accounting for common sources of error.
  • These improved estimators can enhance the accuracy of public health surveillance for infectious diseases.
  • Accurate seroprevalence data are vital for informing effective pandemic control strategies.