EpiCURB: Learning to Derive Epidemic Control Policies

被引：1

作者：

Rusu A.C. ^{[1
]}

Farrahi K. ^{[1
]}

Niranjan M. ^{[1
]}

机构：

[1] University of Southampton, Southampton

来源：

IEEE Pervasive Computing | 2024年 / 23卷 / 01期

关键词：

Disease control;

D O I：

10.1109/MPRV.2023.3329546

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The effectiveness of an epidemic control policy relies largely on how much effort is invested in every public health measure. Unfortunately, it is seldom possible to optimally allocate funds to these measures if the isolated effect of each intervention cannot be reliably estimated. We show how this challenge can be overcome by utilizing EpiCURB, a simulation-control framework that enables us to measure the effect of both untargeted and prioritized interventions on the epidemic outcome, where the latter are guided by reinforcement learning routines that effectively rank eligible individuals. © 2002-2012 IEEE.

引用

页码：57 / 62

页数：5

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