Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
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...
8.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

533
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:
533
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
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 +...
8.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
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...
3.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

204
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
204

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Excess Mortality Estimation.

Annual review of statistics and its application·2026
Same author

Direct-Assisted Bayesian Unit-level Modeling for Small Area Estimation of Rare Event Prevalence.

Journal of survey statistics and methodology·2026
Same author

Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data.

Journal of survey statistics and methodology·2026
Same author

Small Area Estimation of Education Levels in Low- and Middle-Income Countries.

The annals of applied statistics·2026
Same author

BARTSIMP: Flexible spatial covariate modeling and prediction using Bayesian Additive Regression Trees.

Spatial and spatio-temporal epidemiology·2025
Same author

Strengthening evidence for text-based telehealth in post-operative care: A pragmatic study of the reach and effectiveness of two-way, text-based follow-up after voluntary medical male circumcision in South Africa.

PloS one·2025

Related Experiment Video

Updated: Sep 11, 2025

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

10.8K

SPACE-TIME SMOOTHING MODELS FOR SUBNATIONAL MEASLES ROUTINE IMMUNIZATION COVERAGE ESTIMATION WITH COMPLEX SURVEY

Tracy Qi Dong1, Jon Wakefield2

  • 1Department of Biostatistics, University of Washington.

The Annals of Applied Statistics
|August 13, 2025
PubMed
Summary

Accurate measurement of routine immunization coverage is vital for controlling measles in high-burden countries. This study introduces a space-time smoothing model to estimate measles-containing vaccine first dose (MCV1) coverage using complex survey data.

Keywords:
Bayesian smoothingmeasles vaccinationroutine immunizationsupplementary immunization activitysurvey sampling

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

315
Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

8.8K

Related Experiment Videos

Last Updated: Sep 11, 2025

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

10.8K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

315
Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

8.8K

Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Measles vaccination coverage remains high in low- and middle-income countries despite global advances.
  • Routine immunization (RI) and supplementary immunization activities (SIAs) are key strategies for measles control.
  • Accurate subnational measurement of RI-specific coverage is essential for effective program implementation.

Purpose of the Study:

  • To develop a space-time smoothing model for estimating routine immunization-specific coverage of the first dose of measles-containing vaccines (MCV1) at the subnational level.
  • To apply the model to estimate MCV1 coverage in Nigeria using complex survey data from multiple national surveys.
  • To account for the impact of SIAs on overall MCV1 coverage within the estimation model.

Main Methods:

  • Development of a space-time smoothing model utilizing complex survey data.
  • Incorporation of data from multiple national surveys (Demographic and Health Surveys, Multiple Indicator Cluster Surveys, National Nutrition and Health Surveys) conducted between 2003 and 2018 in Nigeria.
  • Integration of information from the World Health Organization's SIA calendar and accounting for SIA impact on MCV1 coverage.

Main Results:

  • The developed model provides robust estimates of subnational RI-specific MCV1 coverage.
  • The model can analyze data from diverse surveys with varying data collection schemes.
  • Coverage estimates are generated with uncertainty reflecting different sampling designs, efficiently implemented using integrated nested Laplace approximation (INLA).

Conclusions:

  • The space-time smoothing model offers a valuable tool for accurately assessing subnational routine immunization coverage for MCV1.
  • This method enhances the understanding of vaccination coverage dynamics, crucial for targeted public health interventions in high-burden settings.
  • The model's ability to integrate multiple data sources and account for SIAs improves the precision of measles control program evaluations.