A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks

  title={A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks},
  author={Ashish Ranjan Hota and Jaydeep Godbole and Philip E. Par'e},
  journal={IEEE Transactions on Network Science and Engineering},
Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, such as the fact that high risk individuals are more likely to undergo testing. We then present two tractable optimization problems to… 

Figures from this paper

Estimation and Distributed Eradication of SIR Epidemics on Networks

This work provides a sufficient condition for the SIR model to converge to the set of healthy states exponentially and proposes a stochastic framework to estimate the system states from observed testing data and provides an analytic expression for the error of the estimation algorithm.

Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks

This work analyzes the susceptible-asymptomatic-infected-recovered (SAIR) epidemic in the framework of activity-driven networks with heterogeneous node degrees and time-varying activation rates, and derives both individual and degree-based mean-field approximations of the exact state evolution.

Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models

Under moderate assumptions on the reproduction number, it is proved that the infection numbers are bounded by supermodular functions in the D-Sir model and the IC-SIR model for large classes of random networks.

Analysis and Estimation of Networked SIR & SEIR Models with Transportation Networks

This paper analyzes the limiting behavior of the models and presents necessary and sufficient conditions for estimating the spreading parameters from data and illustrates these results via simulation.

Assessing the Impact of Continuous Vaccination and Voluntary Isolation on the Dynamics of COVID-19: A Mathematical Optimal Control of SEIR Epidemic Model

In order to study the impact of continuous vaccination and voluntary isolation for the COVID-19, a susceptible-exposed-infected-recovered-quarantine-vaccines (SEIR-QV) model is proposed. A basic

A Networked Competitive Multi-Virus SIR Model: Analysis and Observability

A novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network and proposes an observation model which captures the summation of all the viruses’ infection levels in each node.

On the Efficiency of Decentralized Epidemic Management and Application to Covid-19

A game that allows one to assess the potential loss of efficiency induced by a decentralized control or local management of a global epidemic and quantifies through numerical results the loss induced by decentralization, measured in terms of price of anarchy (PoA) and price of connectedness (PoC).

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review

This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making, and develops a multi-dimensional taxonomy, which categorizes existing works based on multiple dimensions.

Parameter Estimation in Epidemic Spread Networks Using Limited Measurements

The epidemic parameter estimation problem is formulated as an optimization problem, where the goal is to either minimize the total cost spent on collecting measurements, or to optimize the parameter estimates while remaining within a measurement budget.



An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China

We develop a health informatics toolbox that enables public health workers to timely analyze and evaluate the time-course dynamics of the novel coronavirus (COVID-19) infection using the public

Analysis, Estimation, and Validation of Discrete-Time Epidemic Processes

This paper presents several different spread models from the literature and explores their relationships to each other; for one of these processes, a sufficient condition is presented for asymptotic stability of the healthy equilibrium and the condition is necessary and sufficient for uniqueness of thehealthy equilibrium.

Optimal Containment of Epidemics over Temporal Activity-Driven Networks

An adaptive model of epidemic processes is proposed, where the network topology dynamically changes due to both exogenous factors independent of the epidemic dynamics as well as endogenous preventive measures adopted by individuals in response to the state of the infection.

Scenario analysis of non-pharmaceutical interventions on global COVID-19 transmissions

A dynamic panel SIR (DP-SIR) model is introduced to investigate the impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission dynamics with panel data from 9 countries across the globe and suggests that countries may avoid the lockdown policy with imposing school closure, mask wearing and centralized quarantine.

On the dynamics of deterministic epidemic propagation over networks

Can the COVID-19 Epidemic Be Controlled on the Basis of Daily Test Reports?

  • F. Casella
  • Medicine
    IEEE Control Systems Letters
  • 2021
The analysis shows that suppression strategies can be effective if strong enough and enacted early on and how mitigation strategies can fail because of the combination of delay, unstable dynamics, and uncertainty in the feedback loop.

Forecasting seasonal influenza with a state-space SIR model.

This workfits a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] with a conditionally specified prior that allows it to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data.

Data-Driven Network Resource Allocation for Controlling Spreading Processes

A data-driven robust optimization framework to find the optimal allocation of protection resources to eradicate the viral spread at the fastest possible rate and relax the robust optimization problem into a conic geometric program, recently proposed by Chandrasekaran and Shah.

Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks

Various solved and open problems in the development, analysis, and control of epidemic models are reviewed and presented.

Matrix Iterative Analysis

Matrix Properties and Concepts.- Nonnegative Matrices.- Basic Iterative Methods and Comparison Theorems.- Successive Overrelaxation Iterative Methods.- Semi-Iterative Methods.- Derivation and