Journal
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
Volume 60, Issue 2, Pages S49-S74Publisher
SIAM PUBLICATIONS
DOI: 10.1137/20M1377801
Keywords
epidemic spread networks; parameter estimation; optimization algorithms
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Funding
- National Science Foundation [NSF-CMMI 1635014, NSF-ECCS 2032258]
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The study focuses on the estimation of parameters governing the spread of epidemics in networks. The problem is formulated as an optimization problem, and approximation algorithms are proposed to provide solutions.
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.
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