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.