预测 COVID-19 大流行之前和期间的药品供应链中断。

IF 3 3区 医学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Risk Analysis Pub Date : 2024-08-30 DOI:10.1111/risa.17453
Andrea C Hupman, Juan Zhang, Haitao Li
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引用次数: 0

摘要

药品供应链(PSC)的中断会对患者产生负面影响,因此需要对其进行预测,以改善风险缓解效果。虽然已经提出了数据分析和机器学习方法来支持概率特征描述,以便为决策和风险缓解策略提供信息,但以前还没有描述过它们在 PSC 中的应用。此外,目前还不清楚这些模型在 COVID-19 大流行等代表深度不确定性的突发事件中的表现如何。本文研究了在 COVID-19 大流行之前和期间使用数据驱动模型预测 PSC 中断的情况。利用财富 500 强药房福利管理公司药房供应链部门的非专利药数据,我们开发了基于天真贝叶斯算法的预测模型,这些模型可预测特定供应商或特定产品在下一个时间段是否会出现供应中断。我们发现,尽管大流行前情况稳定,但在大流行期间,几乎所有与产品供应中断相关的变量的关系都发生了统计意义上的重大变化。我们提供的结果显示了预测模型的灵敏度、特异性和误报率是如何随着 COVID-19 大流行的发生而变化的,并显示了定期更新模型的有益效果。结果表明,保持模型灵敏度比保持特异性和假阳性率更具挑战性。这些结果提供了有关大流行病对 PSC 内部风险预测影响的独特见解,并为风险分析人员更好地理解突发事件和深度不确定性如何影响预测模型提供了启示。
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Predicting pharmaceutical supply chain disruptions before and during the COVID-19 pandemic.

Disruptions to the pharmaceutical supply chain (PSC) have negative implications for patients, motivating their prediction to improve risk mitigation. Although data analytics and machine learning methods have been proposed to support the characterization of probabilities to inform decisions and risk mitigation strategies, their application in the PSC has not been previously described. Further, it is unclear how well these models perform in the presence of emergent events representing deep uncertainty such as the COVID-19 pandemic. This article examines the use of data-driven models to predict PSC disruptions before and during the COVID-19 pandemic. Using data on generic drugs from the pharmacy supply chain division of a Fortune 500 pharmacy benefit management firm, we have developed predictive models based on the naïve Bayes algorithm, where the models predict whether a specific supplier or whether a specific product will experience a supply disruption in the next time period. We find statistically significant changes in the relationships of nearly all variables associated with product supply disruptions during the pandemic, despite pre-pandemic stability. We present results showing how the sensitivity, specificity, and false positive rate of predictive models changed with the onset of the COVID-19 pandemic and show the beneficial effects of regular model updating. The results show that maintaining model sensitivity is more challenging than maintaining specificity and false positive rates. The results provide unique insight into the pandemic's effect on risk prediction within the PSC and provide insight for risk analysts to better understand how surprise events and deep uncertainty affect predictive models.

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来源期刊
Risk Analysis
Risk Analysis 数学-数学跨学科应用
CiteScore
7.50
自引率
10.50%
发文量
183
审稿时长
4.2 months
期刊介绍: Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include: • Human health and safety risks • Microbial risks • Engineering • Mathematical modeling • Risk characterization • Risk communication • Risk management and decision-making • Risk perception, acceptability, and ethics • Laws and regulatory policy • Ecological risks.
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