Related Experiment Video
Updated: Aug 7, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.1K
Path integral control of a stochastic multi-risk SIR pandemic model
1Department of Mathematics and Statistics, University of South Alabama, 411 University Boulevard North, Mobile, AL, 36688-0002, USA. ppramanik@southalabama.edu.
Theory in Biosciences = Theorie in Den Biowissenschaften
|March 10, 2023
Summary
This study introduces a novel path integral control method to optimize COVID-19 pandemic control strategies. It aims to minimize social costs by determining optimal lockdown policies using a dynamic SIR model and Bayesian opinion dynamics.
Area of Science:
- * Epidemiology and Public Health
- * Mathematical Modeling and Control Theory
- * Computational Economics and Social Dynamics
Background:
- * The COVID-19 pandemic necessitates sophisticated control strategies balancing public health and socioeconomic costs.
- * Existing models often struggle to incorporate complex factors like fatigue dynamics, stochastic risks, and evolving public opinion on vaccination.
- * A dynamic framework is needed to recursively optimize interventions based on real-time and forward-looking risk assessments.
Purpose of the Study:
- * To develop a recursive health objective function integrating fatigue dynamics and a stochastic Susceptible-Infective-Recovered (SIR) model.
- * To model Bayesian opinion dynamics regarding vaccination within different risk groups.
- * To minimize a policy-maker's social cost by determining optimal lockdown intensity.
Main Methods:
- * Application of Feynman-type path integral control for a recursive formulation.
- * Utilizing a forward-looking stochastic multi-risk SIR model with Bayesian opinion dynamics.
- * Derivation of optimal lockdown intensity from a Wick-rotated Schrödinger-type equation (analogous to HJB equation).
- * Employing dynamic programming tools for analysis and numerical solutions.
Main Results:
- * An optimal lockdown intensity was derived, balancing health objectives and social costs.
- * The path integral control approach provided a computationally feasible method for complex pandemic modeling.
- * The integrated model effectively captured the interplay between disease dynamics, public opinion, and intervention policies.
Conclusions:
- * Path integral control offers a powerful framework for optimizing public health interventions in dynamic, uncertain environments.
- * The study provides a novel approach to pandemic control by incorporating fatigue, risk perception, and opinion dynamics.
- * The methodology facilitates the development of adaptive and effective strategies for managing infectious disease outbreaks.
Keywords:
Bayesian opinion networkFeynman-type path integralsMulti-risk SIR modelPandemic controlStochastic differential equationsMore Related Videos
12:21A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
Published on: September 28, 2022
2.6K
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.7K
Related Concept Videos
01:18Steps in Outbreak Investigation
160
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:
160
01:24PI Controller: Design
395
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
395
01:14Parametric Survival Analysis: Weibull and Exponential Methods
512
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
512
01:27Time and frequency -Domain Interpretation of PI Control
175
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
175
01:21Causality in Epidemiology
568
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...
568
00:59Propagation of Uncertainty from Random Error
752
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
752