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 Experiment Videos

Estimation and prediction with HIV-treatment interruption data.

B M Adams1, H T Banks, M Davidian

  • 1Center for Research in Scientific Computation, North Carolina State University, Box 8205, Raleigh, NC 27695-8205, USA.

Bulletin of Mathematical Biology
|January 11, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Inferring porosity from frequency dependent attenuation in cortical bone mimicking porous media.

IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium·2024
Same author

Using ultrasonic attenuation in cortical bone to infer distributions on pore size.

Applied mathematical modelling·2024
Same author

A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder.

Bulletin of mathematical biology·2022
Same author

THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

Communications in applied analysis·2022
Same author

Beyond body size-new traits for new heights in trait-based modelling of predator-prey dynamics.

PloS one·2022
Same author

Declines in Pregnancies among U.S. Adolescents from 2007 to 2017: Behavioral Contributors to the Trend.

Journal of pediatric and adolescent gynecology·2022
Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026
Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026
See all related articles

This study models HIV patient data during treatment interruptions using a nonlinear dynamical model. The approach accurately predicts patient outcomes by estimating key parameters from limited data.

Area of Science:

  • Mathematical modeling
  • Virology
  • Clinical data analysis

Background:

  • HIV treatment interruptions can lead to viral rebound and disease progression.
  • Understanding individual patient responses to treatment changes is crucial for effective management.
  • Longitudinal data provides insights into disease dynamics over time.

Purpose of the Study:

  • To develop and validate a mathematical model for analyzing longitudinal HIV patient data during treatment interruptions.
  • To estimate dynamic parameters of HIV disease progression using a combination of statistical and inverse problem techniques.
  • To assess the predictive power of the model using limited patient data.

Main Methods:

  • Utilized a nonlinear dynamical mathematical model to fit individual patient data.

Related Experiment Videos

  • Integrated a statistically-based censored data method with inverse problem techniques.
  • Estimated dynamic parameters by analyzing longitudinal clinical data from HIV patients undergoing treatment interruptions.
  • Validated the model's predictive capabilities by comparing simulations using half versus full data sets.

    • Main Results:

    • The developed mathematical model successfully fitted individual patient data.
    • The combined statistical and inverse problem approach effectively estimated dynamic parameters.
    • Simulations based on partial data demonstrated strong predictive capabilities, comparable to using full data sets.
    • This highlights the model's efficiency in parameter estimation and prediction.

    Conclusions:

    • The nonlinear dynamical model, combined with censored data methods and inverse problem techniques, provides a robust framework for analyzing HIV patient data during treatment interruptions.
    • The approach demonstrates significant predictive power, even when using only half of the available longitudinal observations.
    • This method offers a valuable tool for personalized medicine in HIV management, enabling accurate predictions with potentially less data.