Parameter Estimation in Epidemic Spread Networks Using Limited Measurements

TL;DR

This paper addresses the challenge of estimating epidemic parameters in networks efficiently by formulating it as an optimization problem that balances measurement costs and estimation accuracy, and proposes approximation algorithms with guarantees.

Contribution

It introduces a novel optimization framework for epidemic parameter estimation under measurement constraints and provides approximation algorithms with theoretical performance guarantees.

Findings

Algorithms achieve near-optimal estimation within budget constraints

Validation through numerical simulations demonstrates effectiveness

NP-hardness of the problem highlights computational challenges

Abstract

We study the problem of estimating the parameters (i.e., infection rate and recovery rate) governing the spread of epidemics in networks. Such parameters are typically estimated by measuring various characteristics (such as the number of infected and recovered individuals) of the infected populations over time. However, these measurements also incur certain costs, depending on the population being tested and the times at which the tests are administered. We thus formulate the epidemic parameter estimation problem 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. We show that these problems are NP-hard to solve in general, and then propose approximation algorithms with performance guarantees. We validate our algorithms using numerical examples.