SNS TourSolver webinar on January 29, 2010

I received a note that the Division of Strategic National Stockpile (DSNS) will be having a webinar about the new and improved SNS TourSolver on Friday, Jan. 29, 2010 at 2 p.m.

According to the note, “This pre-released beta version of the new TourSolver was custom designed for use by DSNS’ state and local partners and is simplified for and focused on SNS routing and distribution needs. Please note: This new version does not require Citrix, and the previous Citrix-based TourSolver will no longer be available.”

Interested parties should contact Rick Pietz at CDC or Bob Roberts at C2Logix.

Planning Medication Distribution

Some public health emergencies could require the quick and efficient distribution of medication and supplies to a large number of people. For instance, to respond to an aerosol anthrax attack, officials will set up and operate Points of Dispensing (PODs) to distribute antibiotics. The medication to be distributed at these PODs must be delivered quickly from a central depot as soon as it arrives.

Planners need a robust plan because there are many uncertainties in medication distribution (including the timing of shipments to the depot, the time needed to load and unload vehicles, travel times, and the demand for medication at each POD). In particular, it is better if the plan calls for delivering medication to PODs much earlier than it is needed. This improves the likelihood that the PODs will open on-time, will not run out of medication during operations, and will dispense medication to the largest number of people in a timely manner.

Our research group recently completed a study on fast algorithms to construct medication distribution plans with slack. Our approach divides the problem into multiple subproblems, including generating a single route, dividing that route into subroutes for each vehicle (“clustering”), scheduling the deliveries, and finding the best delivery quantities. We tested different algorithms on a variety of problem instances based on real-world data. The results show that clustering by route duration generates solutions with more slack than those created by clustering by demand and that these algorithms can generate near-optimal solutions. See the technical report for details.

This work is part of our collaboration with the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response, which is part of the NACCHO Advanced Practice Center program.

Containing an International Influenza Pandemic

The November-December 2009 issue of Operations Research includes the article “Selfish Drug Allocation for Containing an International Influenza Pandemic at the Onset” by Peng Sun, Liu Yang, Francis de Véricourt. (The first two authors are at the Fuqua School of Business at Duke University; de Véricourt is at the European School of Management and Technology in Berlin.)

The article models the spread of an influenza pandemic and discusses how each nation (or a centralized authority like the World Health Organization) should best allocate its stockpile of antiviral drugs to slow down the epidemic. Of course, each nation is acting on its own (and in its own interests), so this can be modeled using game theory. The authors conduct a numerical study of various scenarios, in which sometimes each nations’ selfish decisions are the same as those that a centralized authority would make. In some cases, reducing the total number of infected persons would require a nation to give up its medication to another nation.

Citation: Peng Sun, Liu Yang, Francis de Véricourt, Selfish Drug Allocation for Containing an International Influenza Pandemic at the Onset, Operations Research, Volume 57, Number 6, pages 1320-1332, November-December 2009.

Modeling sessions at the 2010 PHP Summit in Atlanta

The 2010 Public Health Preparedness Summit will be in Atlanta next month. Among the many sessions on the schedule are the following that discuss modeling:

  • Using Computer Models to Plan for the H1N1 Fall Event and Other Health Events, by Jeffrey W. Herrmann (University of Maryland), Rachel Abbey (Montgomery County, Maryland), and William Stephens (Tarrant County, Texas).
  • Modeling the Processing of Mass Fatalities, by CM Wood (CDC).
  • Simulation for Success: Using Models for Preparedness and Response, by Colleen Monahan (University of Illinois at Chicago) and colleagues from Chicago, Minneapolis, and Pittsburgh.
  • Computational Models to Better Understand and Improve Vaccine Delivery, by Bruce Y. Lee, Tina-Marie Assi, and Rachel R. Bailey (University of Pittsburgh).

Some lessons from H1N1

A report that the Trust for America’s Health (TFAH) and the Robert Wood Johnson Foundation (RWJF) released last month concludes that

the H1N1 flu outbreak has exposed serious underlying gaps in the nation’s ability to respond to public health emergencies and that the economic crisis is straining an already fragile public health system.

Read the Ready or Not? Protecting the Public’s Health from Diseases, Disasters, and Bioterrorism report and see how each state performed. See also the post by Richard Hamburg, deputy director of Trust for America’s Health.

Back in June, 2009, the TFAH and RWJF published Pandemic Flu Preparedness: Lessons from the Frontlines, which described the early lessons learned. The June report offered specific recommendations regarding the H1N1 flu pandemic, whereas the new report offers more general recommendations about the structure of public health preparedness.