Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

You might also read

Related Articles

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

Sort by

Same author

Internal Factors and the Transition to Full-Time Nurse Faculty Role: An Integrative Review of Literature.

Nursing education perspectives·2026

Same author

The Hard Lessons and Shifting Modeling Trends of COVID-19 Dynamics: Multiresolution Modeling Approach.

Bulletin of mathematical biology·2021

Same author

Building Community-Based Approaches to Systemic Reform in Mathematical Biology Education.

Bulletin of mathematical biology·2020

Same author

Paying Our Dues: The Role of Professional Societies in the Evolution of Mathematical Biology Education.

Bulletin of mathematical biology·2020

Same author

Stability analysis of a prey refuge predator-prey model with Allee effects.

Journal of biosciences·2019

Same author

Adapting Pediatric Clinical Experiences to a Changing Health Care Environment.

Nurse educator·2016

Search research articles

Related Experiment Video

Updated: Feb 16, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Published on: November 1, 2017

Model selection for integrated pest management with stochasticity.

Olcay Akman¹, Timothy D Comar², Daniel Hrozencik³

¹Department of Mathematics, Illinois State University, Normal, IL 61790, USA.

Journal of Theoretical Biology

|December 16, 2017

Summary

This summary is machine-generated.

This study unifies stochastic and mixture models for integrated pest management, enhancing flexibility in predicting environmental impacts. Results show permanence is preserved in stochastic models, offering robust pesticide efficacy optimization.

Keywords:

Birth pulse Impulsive differential equations Integrated pest management Mixture model Permanence Stochasticity

More Related Videos

Bioassays for Monitoring Insecticide Resistance

Bioassays for Monitoring Insecticide Resistance

Published on: December 30, 2010

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Published on: September 28, 2021

Related Experiment Videos

Last Updated: Feb 16, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Published on: November 1, 2017

Bioassays for Monitoring Insecticide Resistance

Bioassays for Monitoring Insecticide Resistance

Published on: December 30, 2010

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Published on: September 28, 2021

Area of Science:

Mathematical Biology
Ecology
Environmental Science

Background:

Previous integrated pest management (IPM) models by Song and Xiang (2006) considered periodically varying climate.
Subsequent studies introduced stochasticity (Akman et al., 2013) and mixture models (Akman et al., 2014) to account for environmental variability.
A need exists for a unified model combining stochastic and mixture components for greater flexibility in IPM systems.

Purpose of the Study:

To unify stochastic and mixture model components for a more flexible IPM approach.
To determine conditions for permanence in the deterministic mixture model.
To optimize pesticide efficacy in a stochastic model by minimizing variance and assessing robustness.

Main Methods:

Developing a unified mathematical model integrating stochasticity and mixture birth-pulse terms.
Analyzing the deterministic mixture model for permanence conditions.
Investigating the stochastic model to find the optimal mixing parameter for minimizing pesticide efficacy variance.
Conducting sensitivity analysis and numerical simulations.

Main Results:

Conditions for the permanence of solutions in the deterministic mixture model were established.
An optimal mixing parameter was identified for the stochastic model, minimizing pesticide efficacy variance.
Sensitivity analysis confirmed the robustness of the optimized pesticide efficacy.
Numerical simulations demonstrated that permanence can be maintained in the stochastic IPM model.

Conclusions:

The unified model offers enhanced flexibility for analyzing environmental impacts on IPM systems.
Deterministic model results provide valuable insights into the behavior of the stochastic counterpart.
The optimization technique ensures robust and effective pesticide application strategies.