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

Prevalence and Incidence01:08

Prevalence and Incidence

1.4K
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...
1.4K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

428
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:
428
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

202
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...
202
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

1.9K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
1.9K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

823
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:
823
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.6K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Food Insecurity Is Associated with Increased Risk of Ischemic Stroke.

American journal of preventive medicine·2026
Same author

Total Sugars Misreporting in Self-Reported 24-Hour Recalls in the Study of Latinos: Nutrition and Physical Activity Assessment Study.

The Journal of nutrition·2026
Same author

Trends in the Incidence of Early- and Late-Onset Dementia in 2011-2019: A Nationwide Population-Based Study.

Dementia and geriatric cognitive disorders·2026
Same author

Differential Association Between Surrounding Greenness and Mortality in Individuals With Coronary Heart Disease.

JACC. Advances·2026
Same author

Eosinophilia and Risk of Thrombosis and Mortality in Hospitalized Patients: A Retrospective Cohort Study.

Life (Basel, Switzerland)·2026
Same author

The association between Post-COVID syndrome and self-stigma among the adult Israeli population during the COVID-19 pandemic.

BMC public health·2026

Related Experiment Video

Updated: Dec 25, 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

10.9K

A modified Prevalence Incidence Analysis Model method may improve disease prevalence prediction.

Ilya Novikov1, Liraz Olmer1, Lital Keinan-Boker2

  • 1Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer 5265601, Israel.

Journal of Clinical Epidemiology
|March 24, 2020
PubMed
Summary

A modified approach for the Prevalence Incidence Analysis Model improves disease forecasting accuracy. This method enhances predictions for future prevalence, offering a potentially more reliable tool for public health planning.

Keywords:
Cancer prevalenceCancer survivorsEpidemiologyIsraelStatistical forecastingStatistical projection

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Dec 25, 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

10.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Area of Science:

  • Epidemiology and Public Health
  • Biostatistics
  • Cancer Research

Background:

  • The Prevalence Incidence Analysis Model (PIAM) is a standard method for disease prevalence prediction.
  • Accurate disease forecasting is crucial for effective public health resource allocation and planning.

Purpose of the Study:

  • To propose and evaluate a modified approach for selecting the PIAM.
  • To compare the predictive performance of the modified PIAM against the standard method.

Main Methods:

  • The modified approach selects a PIAM based on its success in predicting prevalence over recent years (Y years) using historical data.
  • This selected model is then used to forecast prevalence for a future period (another Y years).
  • An "alignment" adjustment is applied using the most recent known prevalence data.

Main Results:

  • The modified approach demonstrated equal or superior predictive performance compared to the standard method in forecasting cancer prevalence in Israel.
  • Cancer prevalence in Israel is projected to increase from 10,000 cases annually to 12,000 by 2020, reaching 380,000 by 2024.
  • The forecast indicates a gradually accelerating rate of increase in cancer prevalence.

Conclusions:

  • The modified PIAM approach shows promise for improving disease prevalence forecasting.
  • Further methodological research is recommended to refine cancer prevalence forecasting techniques.
  • This enhanced forecasting may aid in better planning for cancer care and resource allocation.