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:

Overview of Biostatistics in Health Sciences

Overview of Biostatistics in Health Sciences

Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...

Study Designs in Epidemiology

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

Introduction to Epidemiology

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

You might also read

Related Articles

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

Sort by

Same author

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.

Journal of medical Internet research·2026

Same author

Predicting Suicidal Ideation Among Native American High Schoolers in California.

Archives of suicide research : official journal of the International Academy for Suicide Research·2025

Same author

A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial.

JMIR research protocols·2020

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A Guide to Constructing Indigenous Statistical Spaces for Prevention Science Research.

Valentín Quiroz de la Sierra¹

¹Center for Indigenous Health, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, 415 N. Washington St., Baltimore, MD, 21231, USA. vsierra4@jh.edu.

Prevention Science : the Official Journal of the Society for Prevention Research

|May 8, 2026

Summary

The Indigenous Computational Approach (ICA) integrates Indigenous Research Methodologies with artificial intelligence (AI) for prevention science. This framework supports Indigenous Data Sovereignty and community well-being in AI-driven research.

Keywords:

Artificial intelligence Deaths of despair Indigenous Data Sovereignty Indigenous Research Methodologies Prevention science

More Related Videos

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Related Experiment Videos

Last Updated: May 9, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Area of Science:

Computational Social Science
Indigenous Studies
Public Health

Background:

Artificial intelligence (AI) is increasingly used in Indigenous deaths of despair research.
Challenges include epistemological misalignment, technical limitations, and ethical concerns.
Integrating Indigenous Research Methodologies is crucial for Indigenous Data Sovereignty.

Purpose of the Study:

To introduce the Indigenous Computational Approach (ICA) as a structured protocol.
To operationalize Indigenous Research Methodologies within computational workflows.
To support ethical and effective AI application in Indigenous prevention science.

Main Methods:

The ICA aligns four components: Researcher Standpoint, Indigenous Theoretical Frameworks, AI Data Analysis Technique, and Dissemination and Indigenous Governance.
A case study applied ICA to suicide risk modeling using lasso logistic regression on the California Healthy Kids Survey.
The protocol includes operational steps and an ICA Checklist for replicability.

Main Results:

The case study successfully applied ICA to an Indigenous subsample (n=2609).
A lasso logistic regression model identified 10 key features for suicide risk.
The model showed strong discrimination (AUC=0.87) and acceptable calibration (Brier score=0.10).

Conclusions:

The ICA restructures AI model design, validation, and deployment for prevention science.
It prioritizes Indigenous self-determination and community-defined well-being.
The ICA offers a replicable protocol for AI-powered research in Indigenous contexts.