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首页> 外文期刊>Journal of medical systems >Optimal resource allocation model to mitigate the impact of pandemic influenza: a case study for Turkey.
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Optimal resource allocation model to mitigate the impact of pandemic influenza: a case study for Turkey.

机译:减轻大流行性流感影响的最佳资源分配模型:以土耳其为例。

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Pandemic influenza has been considered as a serious international health risk by many health authorities in the world. In mitigating pandemic influenza, effective allocation of limited health resources also plays a critical role along with effective use of medical prevention and treatment procedures. A national resource allocation program for prevention and treatment must be supported with the right allocation decisions for all regions and population risk groups. In this study, we develop a multi-objective mathematical programming model for optimal resource allocation decisions in a country where a serious risk of pandemic influenza may exist. These resources include monetary budget for antivirals and preventive vaccinations, intensive care unit (ICU) beds, ventilators, and non-intensive care unit (non-ICU) beds. The mathematical model has three objectives: minimization of number of deaths, number of cases and total morbidity days during a pandemic influenza. This model can be used as a decision support tool by decision makers to assess the impact of different scenarios such as attack rates, hospitalization and death ratios. These factors are found to be very influential on the allocation of the total budget among preventive vaccination, antiviral treatment and fixed resources. The data set collected from various sources for Turkey is used and analyzed in detail as a case study.
机译:大流行性流感已被世界上许多卫生当局认为是严重的国际健康风险。在减轻大流行性流感中,有效分配有限的健康资源在有效利用医疗预防和治疗程序方面也起着关键作用。必须为所有地区和人口风险群体制定正确的分配决定,以支持国家预防和治疗资源分配计划。在这项研究中,我们开发了一个多目标数学规划模型,用于在可能存在严重大流行性流感风险的国家/地区进行最佳资源分配决策。这些资源包括用于抗病毒和预防接种,重症监护病房(ICU)病床,呼吸机和非重症监护病房(non-ICU)病床的货币预算。该数学模型具有三个目标:减少大流行性流感期间的死亡人数,病例数和总发病天数。决策者可以将此模型用作决策支持工具,以评估不同情况的影响,例如发作率,住院率和死亡率。发现这些因素对预防性疫苗接种,抗病毒治疗和固定资源之间总预算的分配有很大影响。从土耳其的各种来源收集的数据集将作为案例研究进行详细分析。

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