Preventing Infectious Disease in Dynamic Populations Under Uncertainty
Preventing Infectious Disease in Dynamic Populations Under Uncertainty
复制标题
在不确定的情况下预防动态人群的传染病
DOI：
10.1609/aaai.v32i1.11341
复制
发表时间：
2018
影响因子：
2.3
通讯作者：
Milind Tambe
中科院分区：
文献类型：
--
作者：
Bryan Wilder;S. Suen;Milind Tambe
可治疗的传染病是公共卫生面临的重大挑战。外展活动可以鼓励未确诊的患者寻求治疗，但必须仔细定位目标，以最有效地利用有限的资源。我们提出了一种算法，以最优地分配有限的外联资源在人口中的人口群体。该算法采用了一种新的多主体疾病传播模型，该模型既能捕捉潜在的种群动态，又易于优化。我们的算法扩展，具有可证明的保证，到随机设置，我们只有一个分布的参数，如代理之间的接触模式。我们用两个实例来评估我们的算法，其中这种分布是从现实世界的数据中推断出来的：印度的结核病和美国的淋病。与目前的政策相比，我们的算法产生的政策预计每年至少可以避免8000人年的结核病和20000人年的淋病。
Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 person-years of gonorrhea annually compared to current policy.
影响因子：
5.5
作者：
Lee, Bruce Y.;Brown, Shawn T.;Korch, George W.;Cooley, Philip C.;Zimmerman, Richard K.;Wheaton, William D.;Zimmer, Shanta M.;Grefenstette, John J.;Bailey, Rachel R.;Assi, Tina-Marie;Burke, Donald S.
通讯作者：
Burke, Donald S.
影响因子：
20.8
作者：
Luke DA;Stamatakis KA
通讯作者：
Stamatakis KA