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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

105
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:
105
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

299
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:
299
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
Prevalence and Incidence01:08

Prevalence and Incidence

351
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...
351
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

40
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
40
Causality in Epidemiology01:21

Causality in Epidemiology

291
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...
291

You might also read

Related Articles

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

Sort by
Same author

Toward a new generation of market indicators driven by blockchain transaction networks.

Chaos (Woodbury, N.Y.)·2026
Same author

Bayesian symbolic regression: automated equation discovery from a physicist's perspective.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

[Guideline for the Treatment of Ulcerative Colitis and Crohn's Disease in Adult Patients].

Revista medica de Chile·2026
Same author

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.

Briefings in bioinformatics·2026
Same author

Connections between physics and metabolism in brain functions.

iScience·2026
Same author

Precision cardiovascular medicine: shifting the innovation paradigm.

Frontiers in science·2025
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 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

10.7K

Machine learning mathematical models for incidence estimation during pandemics.

Oscar Fajardo-Fontiveros1, Mattia Mattei2, Giulio Burgio2

  • 1Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia.

Plos Computational Biology
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to estimate infectious disease incidence in real-time using reported cases and testing rates. A single predictive model accurately estimated COVID-19 incidence across multiple countries.

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

145
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

2.0K

Related Experiment Videos

Last Updated: Jun 4, 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

10.7K
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

145
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

2.0K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Machine Learning

Background:

  • Accurate infectious disease incidence data is crucial for epidemic control.
  • Under-reporting is common due to limited testing, especially with asymptomatic cases.
  • Real-time incidence estimation is vital for timely public health interventions.

Purpose of the Study:

  • To develop a machine learning approach for real-time pandemic incidence estimation.
  • To identify parsimonious, closed-form mathematical models for incidence prediction.
  • To validate the model's accuracy using COVID-19 data from multiple countries.

Main Methods:

  • Utilized Bayesian symbolic regression to automatically derive mathematical models.
  • Incorporated reported case counts and overall test rates as input features.
  • Validated models using daily COVID-19 incidence data from nine countries.

Main Results:

  • The machine learning models accurately predicted daily infectious disease incidence.
  • A single, unified model demonstrated superior parsimony and predictive power across diverse countries compared to country-specific models.
  • The approach effectively addresses under-reporting by integrating testing rates.

Conclusions:

  • Machine learning, specifically Bayesian symbolic regression, offers a powerful tool for real-time incidence modeling.
  • A universal model can effectively capture pandemic dynamics across different regions.
  • This method provides a valuable, accurate tool for public health decision-making during epidemics.