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

Retrovirus Life Cycles01:10

Retrovirus Life Cycles

48.8K
Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
48.8K
Hazard Rate01:11

Hazard Rate

311
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
311
Relative Risk01:12

Relative Risk

1.5K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.5K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

288
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
288
Hazard Ratio01:12

Hazard Ratio

435
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
435
Size and Structure of Viral Genomes01:26

Size and Structure of Viral Genomes

497
Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
497

You might also read

Related Articles

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

Sort by
Same author

Cost-effectiveness of leveraging long-acting injectable cabotegravir to expand PrEP coverage among MSM in two contrasting North American cities.

Journal of the International AIDS Society·2026
Same author

Block-pulse integrodifference equations.

Journal of mathematical biology·2023
Same author

Population-level impact of expanding PrEP coverage by offering long-acting injectable PrEP to MSM in three high-resource settings: a model comparison analysis.

Journal of the International AIDS Society·2023
Same author

Estimating the impact of HIV PrEP regimens containing long-acting injectable cabotegravir or daily oral tenofovir disoproxil fumarate/emtricitabine among men who have sex with men in the United States: a mathematical modelling study for HPTN 083.

Lancet regional health. Americas·2023
Same author

Estimating benefits of using on-demand oral prep by MSM: A comparative modeling study of the US and Thailand.

EClinicalMedicine·2023
Same author

Optimal reduced-mixing for an SIS infectious-disease model.

Journal of biological dynamics·2022

Related Experiment Video

Updated: Dec 7, 2025

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies
07:10

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies

Published on: January 7, 2019

16.1K

The dynamics of a simple, risk-structured HIV model.

Mark Kot1, Dobromir T Dimitrov2

  • 1Department of Applied Mathematics, Box 353925, University of Washington, Seattle, WA 98195-3925, USA.

Mathematical Biosciences and Engineering : MBE
|September 29, 2020
PubMed
Summary
This summary is machine-generated.

Modeling disease spread requires understanding risk. This study shows that accurately representing population risk structures is crucial for predicting disease dynamics and stability, as different structures yield different outcomes.

Keywords:
basic reproduction numbermulti-compartment disease modelseparable mixing

More Related Videos

Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models
09:54

Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models

Published on: December 3, 2019

10.3K
A Rat Model of EcoHIV Brain Infection
08:48

A Rat Model of EcoHIV Brain Infection

Published on: January 21, 2021

3.6K

Related Experiment Videos

Last Updated: Dec 7, 2025

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies
07:10

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies

Published on: January 7, 2019

16.1K
Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models
09:54

Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models

Published on: December 3, 2019

10.3K
A Rat Model of EcoHIV Brain Infection
08:48

A Rat Model of EcoHIV Brain Infection

Published on: January 21, 2021

3.6K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • Many infectious diseases exhibit heterogeneous risk within populations.
  • Accurate modeling of disease transmission is essential for public health interventions.
  • Previous models often simplify the complex nature of risk stratification.

Purpose of the Study:

  • To develop and analyze an infectious disease model incorporating demography, mass-action incidence, multiple risk classes, and separable mixing.
  • To investigate how different risk structures impact disease dynamics, equilibria, and stability.
  • To provide insights into the importance of correctly specifying risk heterogeneity in epidemiological models.

Main Methods:

  • Developed a general mathematical model for infectious disease spread with arbitrary risk classes.
  • Analyzed two specific examples with varying transmission coefficient properties (mean and variance).
  • Determined disease-free and endemic equilibria, calculated the basic reproduction number, and analyzed eigenvalue spectra for stability.

Main Results:

  • The basic reproduction number consistently decreased as the number of risk classes increased in both examples.
  • In both examples, the endemic equilibrium, when it existed, was found to be asymptotically stable.
  • Different risk structures, even with similar average properties, led to distinct disease dynamics.

Conclusions:

  • The structure of population risk significantly influences infectious disease dynamics.
  • Correctly modeling risk heterogeneity is vital for accurate predictions of disease spread and control.
  • Simplified assumptions about risk can lead to misleading epidemiological outcomes.