Designing Effective and Practical Interventions to Contain Epidemics
Designing Effective and Practical Interventions to Contain Epidemics
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发表时间：
2020
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通讯作者：
Prathyush Sambaturu;B. Adhikari;A. Prakash;S. Venkatramanan;A. Vullikanti
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作者：
Prathyush Sambaturu;B. Adhikari;A. Prakash;S. Venkatramanan;A. Vullikanti
接种疫苗是控制流行病传播的标准公共卫生干预措施。然而，疫苗的供应通常是有限的，因此，需要优化其部署。此外，疫苗是随着时间的推移而生产的，因此战略必须是暂时的。我们研究的问题EpiControl设计疫苗接种策略，在现有的预算限制，以尽量减少疫情的传播。这是一个具有挑战性的随机优化问题。我们设计了一个双准则近似算法，它结合了线性规划的舍入，沿着与样本平均近似技术。我们的方法还提供了经验的近似因子的问题的情况下，相对于最佳的。我们发现，近似因子是显着优于最坏情况下的界限，在实践中，是一个小的常数因子。此外，我们的方法显示出显着更好的性能比所有以前的算法为这个问题。通过额外的修剪技术，我们能够将我们的算法扩展到具有数百万条边的网络。
Vaccination is a standard public health intervention for controlling the spread of epidemics. However, the supply of vaccines is typically limited, and therefore, their deployment needs to be optimized. Further, vaccines are produced over time, so the strategies have to be temporal. We study the problem EpiControl of designing vaccination strategies, within available budget constraints, to minimize the spread of an outbreak. This is a challenging stochastic optimization problem. We design a bicriteria approximation algorithm, which combines a linear programming based rounding, along with the sample average approximation technique. Our approach also provides the empirical approximation factor for the problem instance, relative to the optimum. We find that the approximation factor is significantly better than the worst case bound, and, in practice, is a small constant factor. Further, our method shows significantly better performance than all prior heuristics for this problem. With additional pruning techniques, we are able to scale our algorithm to networks with millions of edges.