Modeling bill of rights

The December, 2011, issue of Industrial Engineer includes an article by David Sturrock about how to conduct a successful simulation project. He includes a “bill of rights” for modelers. Some of the highlights:

    Stakeholders must be accessible, cooperative, and involved periodically to resolve issues.
    Don’t criticize the modeler for unexpected or undesirable results.
    If a stakeholder “knows” the right answer beforehand, there is no point to the project. Respect objectivity.

He also lists a stakeholder bill of rights, which includes the following items:

    The modeler will help stakeholders find the right problems and evaluate proposed solutions.
    All but the simplest projects will have a prototype.
    The model will have enough detail to address the stated objectives.
    Project results will be summarized and expressed in a form and terminology useful to stakeholders.
    The model will be documented internally and externally.

Mitigating an Influenza Pandemic in Real Time

Faculty at the University of South Florida has developed a simulation-based tool to find the optimal way to mitigate the impact of an influenza pandemic across multiple regions. It considers multiple mitigation strategies, including vaccination, antivirals, voluntary quarantines, and social distancing. A key feature is that it can be used in real-time as a pandemic progresses. From the article:

(a) the model is capable of re-allocating resources remaining from the previous allocations and thus achieves a more efficient resource utilization; (b) the model incorporates the costs of the resources and aims to allocate a total available budget, as opposed to allocating available quantities of individual resources, which vary in their relative cost and effectiveness.

Snapshot of the decision-aid simulation GUI

Snapshot of the decision-aid simulation GUI

The complete citation is Andrés Uribe-Sánchez, Alex Savachkin, Alfredo Santana, Diana Prieto-Santa and Tapas K. Das, A predictive decision-aid methodology for dynamic mitigation of influenza pandemics, OR Spectrum, Volume 33, Number 3, 751-786, DOI: 10.1007/s00291-011-0249-0

Transparent Models

The July/August 2011 issue of Medical Decision Making is devoted to simulation modeling.  The models are mostly about diseases, including colorectal cancer. 

David F. Ransohoff, Michael Pignone, and Louise B. Russell have an article about using models to make policy. They stress the need for transparency in the following process:

1. Evidence is gathered systematically, evaluated for quality, and selected on the basis of strength for use in quantitative analysis.
2. Analysis of the evidence is conducted quantitatively, often through modeling, to show the likely outcomes of different intervention strategies.
3. Using the results of steps 1 and 2, guidelines groups then decide among different strategies, based on the decision makers’ values and decision thresholds.

They argue that neutral, experienced, professional model-builders are increasingly doing steps 1 and 2 (but not step 3), and modeling experts are also collaborating to understand the differences between their models, which increases transparency and helps guidelines groups explain their recommendations.

The complete citation is David F. Ransohoff, Michael Pignone, and Louise B. Russell, Using Models to Make Policy: An Inflection Point? Med Decis Making July/August 2011 31: 527-529, doi:10.1177/0272989X11412079

Two papers in the issue are freely available:

Clarifying Differences in Natural History between Models of Screening: The Case of Colorectal Cancer” describes a measure for comparing models of screening.

Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models: A Practical Guide” describes the process of representing scientific uncertainty in a model.

Simulating a Pandemic

The May-June 2011 issue of Interfaces has an article by Dionne M. Aleman, Theodorus G. Wibisono, and Brian Schwartz (all from Toronto, Ontario) that describes a agent based simulation model of an influenza pandemic in greater Toronto (which has a population of almost 5 million people).  The model is used to evaluate the impact of mitigation strategies on the number of people infected.  The model estimates the extent to which an outbreak is less severe as more infected persons stay home.

The citation is Dionne M. Aleman, Theodorus G. Wibisono, and Brian Schwartz, A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak, INTERFACES 2011 41: 301-315.

Modeling and Simulation for Emergency Management and Health Care Systems

This week NIST is hosting a workshop on modeling and simulation for homeland security (sponsored by DHS), and they were handing out copies of a report from a previous workshop: Modeling and Simulation for Emergency Management and Health Care Systems: Workshop Summary.

The summary has a variety of short articles discussing the need for modeling and simulation and articles describing some of the work that has been done.