Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data

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:

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Distributions to Estimate Population Parameter

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...

Biostatistics: Overview

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

Estimating Population Standard Deviation

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...

Causality in Epidemiology

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

You might also read

Related Articles

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

Sort by

Same author

Large language models instantiate evolutionarily robust strategies of cooperation.

PNAS nexus·2026

Same author

Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.

Frontiers in immunology·2026

Same author

Structural hormesis in protein aggregation: A minimal mechanistic model.

Journal of the Royal Society, Interface·2026

Same author

Phase II Trial of Vemurafenib and Sorafenib Combination in Advanced <i>KRAS</i>-Mutated Metastatic Pancreatic Cancer.

Journal of immunotherapy and precision oncology·2026

Same author

Embryonic stem cell-derived extracellular vesicles delay cellular senescence by inhibiting oxidative stress.

The Journal of biological chemistry·2025

Same author

Using PyBioNetFit to Leverage Qualitative and Quantitative Data in Biological Model Parameterization and Uncertainty Quantification.

ArXiv·2025

Same journal

Correction: Bulatov et al. Camelpox Virus in Western Kazakhstan: Assessment of the Role of Local Fauna as Reservoirs of Infection. <i>Viruses</i> 2024, <i>16</i>, 1626.

Viruses·2026

Same journal

Correction: Franco et al. Whole Blood Volume-Based Absolute Quantification of HTLV-1 Proviral Load: A Comparative Method Evaluation Study. <i>Viruses</i> 2026, <i>18</i>, 580.

Viruses·2026

Same journal

Correction: Medkour et al. Adenovirus Infections in African Humans and Wild Non-Human Primates: Great Diversity and Cross-Species Transmission. <i>Viruses</i> 2020, <i>12</i>, 657.

Viruses·2026

Same journal

Burden of Malaria and Dengue Across Global, Asian, and Chinese Populations Based on GBD 2021 Data: A Quantitative Assessment of Importation Risks to China.

Viruses·2026

Same journal

First Report of <i>Orthonairovirus songlingense</i> in <i>Haemaphysalis concinna</i> Ticks from Russia.

Viruses·2026

Same journal

Epidemiological and Virological Characteristics of H9N2 Avian Influenza Virus in Jiangsu Province, China, 2024.

Viruses·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 5, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States.

Abhishek Mallela¹, Jacob Neumann², Ely F Miller²

¹Department of Mathematics, University of California, Davis, CA 95616, USA.

|January 22, 2022

Summary

This summary is machine-generated.

Calculating the basic reproduction number (R0) helps estimate herd immunity thresholds for COVID-19. State-level R0 estimates show significant variation, indicating herd immunity is not yet achieved in any US state.

Keywords:

Bayesian inference basic reproduction number coronavirus disease 2019 (COVID-19)herd immunity mathematical model

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Related Experiment Videos

Last Updated: Oct 5, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Area of Science:

Epidemiology
Infectious Disease Modeling

Background:

COVID-19 remains a threat to non-immune individuals due to ongoing SARS-CoV-2 transmission.
Herd immunity is crucial for controlling sustained disease transmission in a population.

Purpose of the Study:

To estimate state-level basic reproduction numbers (R0) for COVID-19 in the United States.
To determine herd immunity thresholds (HITs) for various US states.
To assess whether any state had achieved herd immunity by September 2021.

Main Methods:

Utilized Bayesian inference to calculate state-specific R0 estimates.
Employed compartmental models and the next-generation matrix approach.
Parameterized models using daily confirmed COVID-19 case reports from January 2020 to June 2020.

Main Results:

State-level R0 estimates varied significantly, from 7.1 in New Jersey to 2.3 in Wyoming.
These R0 estimates characterize ancestral SARS-CoV-2 strains.
No US state had achieved herd immunity by September 20, 2021, even when adjusting for the Delta variant.

Conclusions:

Significant variation in R0 across states complicates the achievement of herd immunity.
Herd immunity is a dynamic threshold influenced by pathogen infectiousness and population immunity.
Ongoing efforts are needed to reach herd immunity and mitigate COVID-19 transmission.