有限观测下传染病传播网络的参数估计
Parameter Estimation in Epidemic Spread Networks Using Limited Measurements
摘要：
我们研究了估计参数的问题（即，感染率和恢复率）控制网络中流行病的传播。这些参数通常通过测量感染人群随时间的各种特征（如感染和康复个体的数量）来估计。然而，这些测量也会产生一定的成本，这取决于被测试的人群和测试的时间。因此，我们制定的流行病参数估计问题作为一个优化问题，其中的目标是最大限度地减少收集测量花费的总成本，或优化参数估计，同时保持在测量预算。我们表明，这些问题是NP-难解决的一般，然后提出近似算法的性能保证。我们验证我们的算法使用数值例子。
英文摘要：
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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