{"title":"预测 COVID-19 大流行之前和期间的药品供应链中断。","authors":"Andrea C Hupman, Juan Zhang, Haitao Li","doi":"10.1111/risa.17453","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting pharmaceutical supply chain disruptions before and during the COVID-19 pandemic.\",\"authors\":\"Andrea C Hupman, Juan Zhang, Haitao Li\",\"doi\":\"10.1111/risa.17453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21472,\"journal\":{\"name\":\"Risk Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/risa.17453\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/risa.17453","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.