Montgomery County APC closing; PHPM ending as well.

From the Montgomery County Advanced Practice Center:

“It is with great sadness that we inform you that as of September, 29, 2012, the Advanced Practice Center (APC) Program will be closing its doors.   After eight years of addressing public health preparedness challenges and showcasing innovative practices, the Montgomery APC has reached the end of its existence and will no longer be creating new tools and resources nor providing technical assistance.   …
In anticipation of the close, the National Association of County and City Health Officials (NACCHO) has started the transition of all existing APC products to the easily accessible APC website (  All the tools and resources created over the life of the APC program will still be available to you-at no cost-through the APC website. ”


So this seems like an appropriate time to end this blog as well.  I encourage those interested in modeling to read the Punk Rock Operations Research blog maintained by Laura McLay; it has a Facebook presence as well.






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.

Talks at INFORMS Charlotte

The INFORMS Charlotte conference starts on November 13.
The following talks are related to public health preparedness.
For more information, search the conference program.

Sunday, November 13

Early Detection and Control of Potential Pandemics, Shengpeng Jin, Suraj Alexander, University of Louisville.

Impact of Decision Times and Response Capabilities on Mitigating a Bioterrorism Attack, Jason Middleton, Cheryl Dingus, David Guistino, Jennifer Wightman, Battelle.

Stockpiling Ventilators for Pandemic Influenza, Hsin-Chan Huang, Ozgur Araz, Paul Damien, Lauren Meyers, David Morton, University of Texas at Austin.

Optimizing the Societal Benefits of the Annual Influenza Vaccine, Osman Ozaltin, University of Waterloo; Oleg A. Prokopyev, Mark Roberts, Andrew Schaefer, University of Pittsburgh.

Optimizing Delivery of Antibiotics in Response to an Anthrax Attack, Adam Montjoy, Jeffrey Herrmann, University of Maryland.

Tuesday, November 15

Choosing Among Prevention Interventions for Pandemic Influenza, David Hutton, University of Michigan.

Modeling Community Vulnerability and Medically Fragile Populations for Natural Disaster Preparedness, Joshua Behr, Rafael Diaz, Old Dominion University.

Modeling the Effect of Public Health Resources and Alerting on the Dynamics of Pertussis Spread, Emine Yaylali, Julie Ivy, Reha Uzsoy, North Carolina State University; Erica Samoff, UNC Institute for Public Health.

Approximation Algorithm for the Pediatric Vaccine Stockpiling Problem, Van-Anh Truong, Columbia University.

Making Operating Decisions for a Public Health Emergency Response Network, Kathleen King, John Muckstadt, Cornell University.

Wednesday, November 16

A Multi-agent Model for Optimizing Alerting Decisions in the Case of a Disease Outbreak, Emine Yaylali, Julie Ivy, North Carolina State University.

Vaccine Prioritization for Biological Terrorist Events or Pandemic Response, Fan Yuan, Eva Lee, Georgia Insitute of Technology.

Analysis of Resource Needs for Postal and POD Mass Dispensing, Eva Lee, Chien-Hung Chen, Georgia Institute of Technology.

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

2012 Industrial and Systems Engineering Research Conference

Call for Papers: 2012 Industrial and Systems Engineering Research Conference. Hilton Bonnet Creek, Orlando, Florida, May 19-23, 2012.

Deadline for Abstract Submission is November 11, 2011. Abstract submission site: Conference web site:

The Institute of Industrial Engineers (IIE) is pleased to invite submissions for the 2012 Industrial and Systems Engineering Research Conference (ISERC). The ISERC is the new name for the Industrial Engineering Research Conference (IERC). The ISERC, which is part of the IIE Annual Conference and Expo, is a forum for exchanging knowledge and discoveries in the industrial and systems engineering research community.

The conference includes tracks in healthcare systems, homeland security, operations research, and many other areas. Interested researchers are encouraged to contribute to the conference by submitting an abstract. Authors of accepted abstracts will be invited to present their work in ISERC sessions. See a complete list at the conference program web page.

All presenters are also encouraged to submit a full length paper for the conference proceedings. The papers should contain results that are significant and have archival value to the industrial and systems engineering research community; however, the papers are more limited in scope and length than a full-length journal paper. Full papers have a ten-page limit and will undergo a double-blind peer review process.

The Center for Computational Epidemiology and Response Analysis

Armin R. Mikler, Tamara Schneider-Jimenez, Chetan Tiwari and Marty O’Neill II at the Center for Computational Epidemiology and Response Analysis have developed the RE-PLAN system to create and analyze emergency response plans, including the distribution of Strategic National Stockpile (SNS) assets. Although the software uses maps, the user does not need GIS expertise. Their model also analyzes traffic flow to and from PODS. They have made a video (about 9 minutes long) demonstrating its functionality.

For more information, visit the center web site or contact Dr. Armin R. Mikler at

Public Health and Healthcare Surveillance and Response

IIE Transactions Call for Papers
Special issue on “Public Health and Healthcare Surveillance and Response”
Guest Editors: Wei Jiang, Lianjie Shu, and Kwok-L. Tsui

The objective of public health surveillance is to systematically
collect, analyze, and interpret public health data (chronic or
infectious diseases) in order to understand trends, detect changes in
disease incidence and death rates, and plan, implement and evaluate
public health practices. Numerous organizations such as CDC, WHO, etc.
as well as private companies such as Google Inc. have collected and
published health related data in a regular basis nowadays, especially
during the period of H1N1 influenza in 2009-2010. Recently, studies
have been conducted to develop methods and algorithms for health
surveillance and disease outbreak detection based on these macro- and
micro-level health datasets. It is well recognized that disease
outbreaks or unanticipated healthcare inefficiency can be effectively
mitigated or avoided by enacting effective healthcare standardization,
quality management, and surveillance systems. As a result, the public
health and healthcare system can be significantly improved through
timely medical mitigation (such as vaccination or targeted groups for
antiviral prescriptions), non-medical mitigation (such as school
closings or quarantine), as well as other quality improvement

Two major tasks in public health surveillance are to quickly detect an
adverse health event and to promptly respond to the event. The earlier
an increase in the incidence rate can be detected, the earlier
preventive actions can be taken before further severe health
situations occur such as disease spreading or mutation. Therefore,
quick detection and prevention are beneficial to both individuals and
society. Moreover, once an adverse event has occurred, the public
health and healthcare administrators should take advantage of the
surveillance methods and other quantitative tools (such as simulation
and optimization) to manage and respond to the outbreak or epidemic
situations. To address these two issues, statistical methods for
public health surveillance and responses have been widely studied. The
central theme of this Special Issue is to understand the requirements
and opportunities in healthcare and public health surveillance and to
encourage the applications of statistical methods in complex
healthcare systems. The purpose is to show the state-of-the-art
research and applications in health surveillance and response by
bringing together researchers from various research fields to address
the significant advancement, expose the unsolved challenges, and
provide visions for future research and development.

* Subject Coverage

We are particularly interested in the research results in the
following two categories: (i) temporal, spatial, and spatiotemporal
surveillance methodologies in public health; and (ii) applications of
quantitative methodologies such as optimization, simulation, and
quality control to healthcare and public health surveillance and
response. Topics to be covered include, but not limited to the

  • Data collection systems in healthcare applications
  • Healthcare system modeling and forecasting including call centers
  • Time study in hospital management
  • Hospital workforce management
  • Emergency room management
  • Temporal surveillance methods in health care applications
  • Spatial surveillance methods for cluster detection in health care applications
  • Spatiotemporal surveillance methods for detecting emerging clusters
  • Disease-related research such as outbreak detection and disease risk estimation and monitoring
  • Performance merits for surveillance methods in healthcare applications
  • Healthcare response to disease outbreaks and pandemic

Notes for Intending Authors

All papers are to be submitted through Please select “Special Issue” under Manuscript Category of your submission. All manuscripts must be prepared according to the IIE Transactions
publication guidelines.

Important Dates

December 1, 2011: Intent to submit (optional)
March 1, 2012: Paper submission deadline
September 1, 2012: Completion of the first round review
January 1, 2013: Completion of the second round review
March 1, 2013: Final manuscripts due
September 1 2013: Tentative publication date

Editor’s notes

You may communicate with any of the Guest Editors on any aspect of the
Special Issue as follows:

Prof. Wei Jiang
Shanghai Jiaotong University, China

Prof. Lianjie Shu
University of Macau, Macau

Prof. Kwok-Leung Tsui
Georgia Institute of Technology, USA

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.