Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;3(3):ojphi.v3i3.3798.
doi: 10.5210/ojphi.v3i3.3798. Epub 2011 Dec 22.

Probabilistic, Decision-theoretic Disease Surveillance and Control

Michael Wagner  1 , Fuchiang Tsui, Gregory Cooper, Jeremy U Espino, Hendrik Harkema, John Levander, Ricardo Villamarin, Ronald Voorhees, Nicholas Millett, Christopher Keane, Anind Dey, Manik Razdan, Yang Hu, Ming Tsai, Shawn Brown, Bruce Y Lee, Anthony Gallagher, Margaret Potter
Affiliations

Probabilistic, Decision-theoretic Disease Surveillance and Control

Michael Wagner et al. Online J Public Health Inform. 2011.

Abstract

The Pittsburgh Center of Excellence in Public Health Informatics has developed a probabilistic, decision-theoretic system for disease surveillance and control for use in Allegheny County, PA and later in Tarrant County, TX. This paper describes the software components of the system and its knowledge bases. The paper uses influenza surveillance to illustrate how the software components transform data collected by the healthcare system into population level analyses and decision analyses of potential outbreak-control measures.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Information flow in the probabilistic, decision-theoretic disease surveillance system. Legend: light blue rectangles, software components: CDS, Case Detection System; ODS, Outbreak Detection and characterization System; hexagons, knowledge bases; rounded rectangles, data and analyses; thin arrows, user interfaces; large arrows indicate that the components exchange data electronically in the direction indicated; dotted arrow indicates a future connection sending case data collected by nurse investigators from Trisano® to CDS.
Figure 2:
Figure 2:
ODS, most probable models.
Figure 3.
Figure 3.
Distributions of total infected, peak date, incidence on peak day, and posterior distributions for Ro, latent period, and infectious period. ODS used uniform prior probabillity distributions for SEIR model parameters in this analysis. For example, the prior distribution for Ro was uniform over the range 1.1 to 1.9.
Figure 4.
Figure 4.
BioEcon.
Figure 5.
Figure 5.
Apollo Web Service
See this image and copyright information in PMC

References

    1. Zimmer SM, Crevar CJ, Carter DM, Stark JH, Giles BM, Zimmerman RK, et al. Seroprevalence following the second wave of Pandemic 2009 H1N1 influenza in Pittsburgh, PA, USA. PLoS One. 2010;5(7):e11601–e. doi: 10.1371/journal.pone.0011601. - DOI - PMC - PubMed
    1. Burkom HS, Ramac-Thomas L, Babin S, Holtry R, Mnatsakanyan Z, Yund C. An integrated approach for fusion of environmental and human health data for disease surveillance. Statistics in Medicine. 2011;30(5):470–9. doi: 10.1002/sim.3976. - DOI - PubMed
    1. Harvey AC. The Kalman filter and its applications in econometrics and time series analysis. Methods of Operations Research. 1982;44(1):3–18.
    1. Le Strat Y, Carrat F. Monitoring epidemiologic surveillance data using hidden Markov models. Statistics in medicine. 1999;18(24):3463–78. - PubMed
    1. Mnatsakanyan ZR, Burkom HS, Coberly JS, Lombardo JS. Bayesian information fusion networks for biosurveillance applications. Journal of the American Medical Informatics Association. 2009;16(6):855–63. - PMC - PubMed

LinkOut - more resources