A mass casualty event places a huge burden on local hospitals. How much of a burden? The AHRQ Hospital Surge Model provides hospitals and public health officials with a way to predict the hospital resources required.
The model is a web-based tool that requires answering two questions: (1) what type of biological, chemical, nuclear, or radiological event is it? (2) how many casualties are expected?
From this data, it predicts (for each day of the event) the number of arrivals, patients, discharges, and deaths as well as the number of resources (such as specialized staff, consumables, medication) needed.
This summer I had the great fortune to work with two undergraduate industrial engineering students (one from Virginia Tech, the other from the University of San Diego) on the medication distribution problem. (The students were participating in an REU site here at Institute for Systems Research.)
This is the problem of planning how to distribute medication from an RSS to multiple PODs while material continues to arrive (from the SNS and VMI) to the RSS. The goal is to find a plan that uses a fixed number of vehicles and maximizes the slack of the deliveries (as a hedge or buffer against uncertainties). We developed a two-stage routing and scheduling approach and tested it on a realistic scenario from the state of Maryland. For routing vehicles we took advantage of the TourSolver software available through the CDC. We also considered the impact of using a local distribution center as an intermediate point between the RSS and the PODs. Our technical report is now available online at http://hdl.handle.net/1903/8417.
The paper’s results for this particular scenario are not meant to prove whether LDCs are useful. The goal is to provide a methodology for creating medication distribution plans and evaluating their slack as one component in deciding which plan is best for a particular area. I would love to hear from anyone who would like to apply this approach to their situation.
Rachel Abbey (Montgomery County, Maryland, Public Health Services) sent me a link to an article at Government Health IT about computer tools developed at the University of Minnesota’s National Center for Food Protection and Defense.
The Consequence Management System will be used to predict and track food contamination and relies upon data from food processors and distributors.
The second tool is the Food and Agriculture Sector Criticality Assessment Tool. This is a spreadsheet that models different food supply chains, including seafood, grain, and frozen pizza, among others. The center’s website also has a video tutorial on how to use this tool.
The folks at the Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon have been developing BioWar, which is designed to help policymakers evaluate different alternatives for responding to a smallpox attack. BioWar combines
state-of-the-art computational models of social networks, communication media, disease models, demographically accurate agent models, wind dispersion models, and a diagnostic error model into a single integrated model of the impact of an attack on a city.
To use BioWar, you will need a computer running Fedora Core 3, a Linux-based operating system.