Finding optimal vaccination strategies for pandemic influenza using genetic algorithms
Finding optimal vaccination strategies for pandemic influenza using genetic algorithms
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DOI：
10.1016/j.jtbi.2004.11.032
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发表时间：
2005-05-21
影响因子：
2
通讯作者：
Halloran, ME
中科院分区：
文献类型：
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作者：
Patel, R;Longini, IM;Halloran, ME
在发生大流行性流感时，可能只有有限的疫苗供应。我们使用随机流行病模拟，遗传算法（GA）和随机突变爬山（RMHC），以找到最佳的疫苗分布，以尽量减少疾病或死亡人数的人口，疫苗数量有限。由于流行病过程的非线性、复杂性和随机性，不可能用数学方法求解最优疫苗分配。然而，我们使用GA和RMHC找到接近最优的疫苗分布。我们模拟了一种流感大流行，其年龄特异性疾病发作率与1957-1958年由甲型流感（H2 N2）引起的亚洲大流行相似，并且分布与1968-1969年由甲型流感（H3 N2）引起的香港大流行相似。我们找到了最佳的疫苗分布，剂量的数量是有限的范围内的10-90%的人口。虽然GA和RMHC在寻找最佳疫苗分布方面工作得很好，但GA比RMHC更有效。我们发现，GA和RMHC找到的最佳疫苗分布是高达84%的有效性比随机大规模接种疫苗的中间范围内的疫苗可用性。遗传算法可推广到其他传染病和种群结构的随机模型参数优化。(c)2005爱思唯尔有限公司保留所有权利。
In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures. (c) 2005 Elsevier Ltd. All rights reserved.