Inferring sources of substandard and falsified products in pharmaceutical supply chains

Eugene Wickett*, Matthew Plumlee, Karen Smilowitz, Souly Phanouvong, Victor Pribluda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Substandard and falsified pharmaceuticals, prevalent in low- and middle-income countries, substantially increase levels of morbidity, mortality and drug resistance. Regulatory agencies combat this problem using post-market surveillance by collecting and testing samples where consumers purchase products. Existing analysis tools for post-market surveillance data focus attention on the locations of positive samples. This article looks to expand such analysis through underutilized supply-chain information to provide inference on sources of substandard and falsified products. We first establish the presence of unidentifiability issues when integrating this supply-chain information with surveillance data. We then develop a Bayesian methodology for evaluating substandard and falsified sources that extracts utility from supply-chain information and mitigates unidentifiability while accounting for multiple sources of uncertainty. Using de-identified surveillance data, we show the proposed methodology to be effective in providing valuable inference.

Original languageEnglish (US)
JournalIISE Transactions
StateAccepted/In press - 2023


  • Bayesian statistics
  • identifiability
  • network inference
  • Substandard and falsified pharmaceuticals

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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