Dimitrov Nedialko, Goll Sebastian, Hupert Nathaniel, Pourbohloul Babak, Meyers Lauren Ancel
University of Texas at Austin, USA.
PLoS Curr. 2009 Nov 4;1:RRN1127. doi: 10.1371/currents.rrn1127.
Public health agencies across the globe are working to mitigate the impact of the 2009 pandemic caused by swine-origin influenza A (H1N1) virus. Prior to the large-scale distribution of an effective vaccine, the primary modes of control have included careful surveillance, social distancing and hygiene measures, strategic school closures, other community measures, and the prudent use of antiviral medications to prevent infection (prophylaxis) or reduce the severity and duration of symptoms (treatment). Here, we use mathematical models to determine the optimal geo-temporal tactics for distributing the U.S. strategic national stockpile of antivirals for treatment of infected cases during the early stages of a pandemic, prior to the wide availability of vaccines.We present a versatile optimization method for efficiently searching large sets of public health intervention strategies, and apply it to evaluating tactics for distributing antiviral medications from the U.S. Strategic National Stockpile (SNS). We implemented the algorithm on a network model of H1N1 transmission within and among U.S. cities to project the epidemiological impacts of antiviral stockpile distribution schedules and priorities. The resulting optimized strategies critically depend on the rates of antiviral uptake and wastage (through misallocation or loss). And while a surprisingly simple pro rata distribution schedule is competitive with the optimized strategies across a wide range of uptake and wastage, other equally simple policies perform poorly.Even as vaccination campaigns get underway worldwide, antiviral medications continue to play a critical in reducing H1N1-associated morbidity and mortality. If efforts are made to increase the fraction of cases treated promptly with antivirals above current levels, our model suggests that optimal use of the antiviral component of the Strategic National Stockpile may appreciably slow the transmission of H1N1 during fall 2009, thereby improving the impact of targeted vaccination. A more aggressive optimized antiviral strategy of this type may prove critical to mitigating future flu pandemics, but may increase the risk of antiviral resistance.
全球各地的公共卫生机构都在努力减轻由甲型H1N1猪流感病毒引发的2009年大流行的影响。在有效疫苗大规模分发之前,主要的控制措施包括密切监测、社交距离和卫生措施、战略性学校关闭、其他社区措施以及谨慎使用抗病毒药物以预防感染(预防用药)或减轻症状的严重程度和持续时间(治疗用药)。在此,我们使用数学模型来确定在大流行早期、疫苗广泛可得之前,美国战略国家储备抗病毒药物用于治疗感染病例的最佳地理时间策略。我们提出了一种通用的优化方法,用于高效搜索大量的公共卫生干预策略,并将其应用于评估从美国战略国家储备(SNS)分发抗病毒药物的策略。我们在美国城市内部和之间的H1N1传播网络模型上实施了该算法,以预测抗病毒药物储备分发时间表和优先级的流行病学影响。由此产生的优化策略严重依赖于抗病毒药物的摄取率和浪费率(因分配不当或损失)。虽然一个令人惊讶的简单按比例分配时间表在广泛的摄取和浪费范围内与优化策略具有竞争力,但其他同样简单的政策表现不佳。即使全球范围内的疫苗接种运动正在进行,抗病毒药物在降低与H1N1相关的发病率和死亡率方面仍继续发挥关键作用。如果努力将及时接受抗病毒药物治疗的病例比例提高到当前水平之上,我们的模型表明,战略国家储备中抗病毒药物成分的最佳使用可能会在2009年秋季显著减缓H1N1的传播,从而提高针对性疫苗接种的效果。这种更积极的优化抗病毒策略可能对减轻未来流感大流行至关重要,但可能会增加抗病毒耐药性的风险。