Although this topic is not directly related to public health preparedness, it is interesting that the justification for the new recommendations includes the results of six mathematical models. The modeling is discussed in Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms by Mandelblatt et al. (This article also cites the articles describing the details of each model.)

The mathematical models are similar and all estimate the impact of different screening strategies. The benefits are measured in life-years gained because of averted or delayed breast cancer death due to a screening strategy; the harms include false-positive mammograms, unnecessary biopsies, and overdiagnosis.

Mandelblatt et al. examined 20 different strategies and identified eight “non-dominated” strategies – that is, for each of these eight, there was no other strategy that gave more benefits with fewer overall mammograms. Among these eight, there is a tradeoff: more benefits will necessarily require more mammograms.

The models also estimate that false-positives occur more often in those strategies that include screening for women between the ages of 40 to 49 years.

The practices, developed by different states and local jurisdictions, are organized into the following topics: types of patient care, communication, community disease mitigation, and helping at-risk groups. The practices are also organized by the states that developed them.

The paper Modeling and Optimizing the Public Health Infrastructure for Emergency Response, by Eva K. Lee, Chien-Hung Chen, Ferdinand Pietz, and Bernard Benecke, is a useful overview of the models that this group has developed in collaboration with public health officials around the nation. The RealOpt system can “minimize staff required at various stations in point-of-dispensing (POD) locations to meet throughput requirements, determine the number and locations of the PODs to minimize cost of operations while ensuring acceptable travel times and distances, and help evaluate facility design and flow strategies within a POD to mitigate the spread of disease.”

It is great to see this valuable work getting important recognition by the broader operations research community.

At the University of South Florida, Tapas Das is leading a team with experts in industrial engineering, public health, and medical anthropology to develop surveillance and simulation techniques to build a real-time mitigation strategy for pandemics that can begin optimizing the response to an outbreak as soon as it appears.

The guide includes material on influenza pandemic preparedness in order to apply the principles, and this section has links to influenza-specific resources such as training guides, guidelines, checklists, and similar material for hospitals, alternative care sites, and other health services. The guide also discusses ethical and legal issues.

We have updated the Vaccine Allocation Model to reflect this new data. In Version 3.0, one can set the number of doses per person for each target group separately. Thus, one can specify the number of children 9 and under and their need for 2 shots per child and enter the number of children 10 and over and their need for only 1 shot per child. The population of other target groups can be entered as well.

The Vaccine Allocation Model is intended to help public health officials determine how many persons in different target groups can receive treatment. This software, implemented in a Microsoft Excel workbook, is provided free of charge for use by public health emergency preparedness planners. Go to the model download page to get the model and its user guide.