Deep Spatial Q-Learning for Infectious Disease Control
Deep Spatial Q-Learning for Infectious Disease Control
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用于传染病控制的深度空间 Q 学习
DOI：
10.1007/s13253-023-00551-4
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Fang, Ethan X.
中科院分区：
文献类型：
--
作者：
Liu, Zhishuai;Clifton, Jesse;Laber, Eric B.;Drake, John;Fang, Ethan X.
传染病是世界各地人道主义和经济危机的一个原因。在发展中地区，严重的流行病可能导致医疗基础设施崩溃，甚至导致受影响国家的失败。2013-2015年西非爆发的埃博拉病毒病就是这种流行病的一个例子。这次疫情的经济、基础设施和人力成本为研究适应性治疗战略提供了强大的动力，这些战略根据流行病的演变分配资源。我们正式适应性管理的一个新出现的传染病蔓延的一组位置作为一个治疗方案，映射到一个子集的位置确定为高优先级的治疗流行病的最新信息。在这种情况下，最佳治疗方案被定义为最大化预先指定的累积效用度量的期望，例如，相对于基线干预策略，无病个体的数量或发病率或死亡率的估计降低。由于疾病动态在暴发初期并不清楚，因此必须在线估计最佳治疗方案，即，随着数据的积累，因此，有效的估计算法必须平衡选择导致信息增益的干预，从而改进模型，并使干预在当前估计模型下看起来是最佳的。我们开发了一种新型的无模型算法，用于在线管理在有限位置集合和不确定或无限时间范围内传播的传染病。所提出的算法平衡的探索和开发使用的半参数变量的汤普森采样。我们还介绍了一个基于图神经网络的估计，以提高这类算法的性能。模拟，包括那些模仿2013-2015年埃博拉疫情的传播，表明一个适应性的治疗策略有可能显着降低死亡率相对于蟾蜍感染管理strategies.补充材料随本文出现在网上.
Infectious diseases are a cause of humanitarian and economic crises across the world. In developing regions, a severe epidemic can result in the collapse of healthcare infrastructure or even the failure of an affected state. The most recent 2013–2015 outbreak of Ebola virus disease in West Africa is an example of such an epidemic. The economic, infrastructural, and human costs of this outbreak provide strong motivation for the examination of adaptive treatment strategies that allocate resources in response to and anticipation of the evolution of an epidemic. We formalize adaptive management of an emerging infectious disease spreading across a set of locations as a treatment regime that maps up-to-date information on the epidemic to a subset of locations identified as high-priority for treatment. An optimal treatment regime in this context is defined as maximizing the expectation of a pre-specified cumulative utility measure, e.g., the number of disease-free individuals or the estimated reduction in morbidity or mortality relative to a baseline intervention strategy. Because the disease dynamics are not known at the beginning of an outbreak, an optimal treatment regime must be estimated online, i.e., as data accumulate; thus, an effective estimation algorithm must balance choosing interventions that lead to information gain and thereby model improvement with interventions that appear to be optimal under the current estimated model. We develop a novel model-free algorithm for the online management of an infectious disease spreading over a finite set of locations and an indefinite or infinite time horizon. The proposed algorithm balances exploration and exploitation using a semi-parametric variant of Thompson sampling. We also introduce a graph neural network-based estimator in order to improve the performance of this class of algorithms. Simulations, including those mimicking the spread of the 2013–2015 Ebola outbreak, suggest that an adaptive treatment strategy has the potential to significantly reduce mortality relative toad hocmanagement strategies.Supplementary materials accompanying this paper appear online.
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DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
168.9
作者：
Lozano, Rafael;Naghavi, Mohsen;Murray, Christopher J. L.
通讯作者：
Murray, Christopher J. L.
DOI：
10.2139/ssrn.3980837
发表时间：
2021
期刊：
ArXiv
影响因子：
--
作者：
S. Saghafian
通讯作者：
S. Saghafian
DOI：
10.1080/01621459.2020.1768100
发表时间：
2020-06-26
影响因子：
3.7
作者：
Forastiere, Laura;Airoldi, Edoardo M.;Mealli, Fabrizia
通讯作者：
Mealli, Fabrizia
影响因子：
2.7
作者：
Zhang B;Tsiatis AA;Laber EB;Davidian M
通讯作者：
Davidian M