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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. 

This paper proposes a new formulation for the school bus scheduling problem (SBSP), which optimizes school start times and bus operation times to minimize transportation cost. The goal is to minimize the number of buses to serve all bus routes such that each route arrives in a time window before school starts. We show that introducing context-specific features, common in many school districts, can lead to a new time-indexed integer linear programming (ILP) formulation. Based on a strengthened version of the linear relaxation of the ILP, we develop a dependent randomized rounding algorithm that yields near-optimal solutions for large-scale problem instances. The efficient formulation and solution approach enable quick generation of multiple solutions to facilitate strategic planning, which we demonstrate with data from two public school districts in the United States. We also generalize our methodologies to solve a robust version of the SBSP. 

This paper describes Situational Awareness for Events (SAFE), a comprehensive data visualization system for mass-participation endurance events. Working in partnership with the Bank of America Chicago Marathon and the Chevron Houston Marathon, and their public safety partners, we developed SAFE to enhance logistics, medical preparedness, and response for mass-participation endurance events such as marathons. The system incorporates critical data into a user-friendly dashboard to serve as a centralized source of information during the events. SAFE uses historical and real-time data to provide pre-event and on-site analytics via descriptive, predictive, and prescriptive models. These models help race organizers and relevant stakeholders effectively manage and oversee all participants, monitor the dynamic location of race participants, and manage health and safety resources throughout the event. The system was deployed successfully at the Chicago Marathon (2014–2018), the Shamrock Shuffle (2014–2018), and the Houston Marathon (2016–2018.