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

656
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:
656
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
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:
1.1K
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.7K
Regression Analysis01:11

Regression Analysis

8.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.7K
Modeling with Differential Equations01:25

Modeling with Differential Equations

133
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
133

You might also read

Related Articles

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

Sort by
Same author

Assessment of CD8<sup>+</sup> T-cell mediated immunity in an influenza A(H3N2) human challenge model in Belgium: a single centre, randomised, double-blind phase 2 study.

The Lancet. Microbe·2024
Same author

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned.

Epidemics·2024
Same author

Evaluation of a panel of therapeutic antibody clinical candidates for efficacy against SARS-CoV-2 in Syrian hamsters.

Antiviral research·2023
Same author

Serial Interval and Incubation Period Estimates of Monkeypox Virus Infection in 12 Jurisdictions, United States, May-August 2022.

Emerging infectious diseases·2023
Same author

Novel modelling approaches to predict the role of antivirals in reducing influenza transmission.

PLoS computational biology·2023
Same author

Reducing Influenza Virus Transmission: The Potential Value of Antiviral Treatment.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2021
Same journal

Spatio-temporal modeling of zoonotic cutaneous leishmaniasis (ZCL) in the Algerian steppe: Epidemiological insights and climatic associations.

Epidemics·2026
Same journal

Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review.

Epidemics·2026
Same journal

Identifying memory mechanisms in Bayesian models of behavioural change during epidemics.

Epidemics·2026
Same journal

Mapping the landscape of individual-based models for respiratory pathogen transmission in the pandemic and post-pandemic era (2020-2024): A systematic review.

Epidemics·2026
Same journal

A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.

Epidemics·2026
Same journal

Modelling serological cross-reactivity to disentangle the dynamics of West Nile and Usutu viruses in an emerging area.

Epidemics·2026
See all related articles

Related Experiment Video

Updated: Mar 5, 2026

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

37.1K

Forecasting Ebola with a regression transmission model.

Jason Asher1

  • 1Leidos Supporting the Department of Health and Human Services (HHS), Biomedical Advanced Research and Development Authority (BARDA), United States.

Epidemics
|March 27, 2017
PubMed
Summary
This summary is machine-generated.

A new stochastic model accurately predicted Ebola outbreak peaks and final size, achieving the lowest error in a forecasting challenge. This flexible model enhances traditional disease modeling for complex epidemic dynamics.

Keywords:
Bayesian inferenceEbolaForecastingMathematical modeling

More Related Videos

Experimental Viral Infection in Adult Mosquitoes by Oral Feeding and Microinjection
08:02

Experimental Viral Infection in Adult Mosquitoes by Oral Feeding and Microinjection

Published on: July 28, 2022

3.1K
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

11.2K

Related Experiment Videos

Last Updated: Mar 5, 2026

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

37.1K
Experimental Viral Infection in Adult Mosquitoes by Oral Feeding and Microinjection
08:02

Experimental Viral Infection in Adult Mosquitoes by Oral Feeding and Microinjection

Published on: July 28, 2022

3.1K
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

11.2K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Ebola virus disease (EVD) outbreaks pose significant public health challenges requiring accurate forecasting.
  • Traditional compartmental models like Susceptible-Infected-Recovered (SIR) have limitations in capturing complex epidemic trajectories.

Purpose of the Study:

  • To develop and validate a novel stochastic model for Ebola transmission forecasting.
  • To assess the model's performance against other participants in the Ebola Forecasting Challenge.
  • To demonstrate the model's capability in predicting key outbreak parameters.

Main Methods:

  • A discrete-time compartmental model was developed incorporating a time-varying reproductive rate.
  • The reproductive rate was modeled as a multiplicative random walk influenced by the number of infectious individuals.
  • The model was applied to generate forecasts for an Ebola outbreak scenario.

Main Results:

  • The developed stochastic model achieved the lowest mean absolute error among all participants in the Ebola Forecasting Challenge.
  • The model successfully predicted the peak incidence, timing of the peak, and the final size of the Ebola outbreak.
  • The model's flexible structure allowed for accurate representation of complex disease dynamics.

Conclusions:

  • The proposed stochastic model offers a simple yet powerful approach for forecasting Ebola transmission.
  • This generalized modeling framework can be adapted for various infectious disease outbreaks with complex dynamics.
  • Accurate forecasting is crucial for effective public health interventions during epidemics.