Biosurveillance Algorithms

Different diseases may share many of the same symptoms. Because diseases have a broad range of
symptoms (many of which overlap), biosurveillance algorithms must be constructed to identify those indicators
that can (individually or in some combination) accurately discriminate the presence or absence of the condition
of interest, properly monitor those indicators, and provide reliable output on each specific disease’s trends.
Matching the process of analyzing the data with the necessary types of data is of utmost importance when
trying to obtain an early identification of a health event with minimum false positives.
select a chronic disease that is of interest to you.
write a 2 pages paper, Consider the best approach/algorithm to monitor the disease or condition you selected.
Determine and explain the number and type of covariates you would include in the algorithm. Finally, explain
the limitations of the algorithm and the implications for public health.
Required Readings
Disease Surveillance: A Public Health Informatics Approach
• Chapter 4, “Alerting Algorithms for Biosurveillance”
Kulldorff, M. (2001). Prospective time periodic geographical disease surveillance using a scan statistic. Journal
of Royal Statistical Society, 164(1), 61–72.
Taubenberger, J. K., & Morens, D. M. (2006, January). 1918 influenza: The mother of all pandemics. Emerging
Infectious Diseases, 12(1), 15–22.
Optional Resources
Brookmeyer, R., & Gail, M. H. (1994). AIDS epidemiology: A quantitative approach. New York, NY: Oxford
University Press.
Note: In particular, read Chapter 7, “Statistical Issues in Surveillance of AIDS Incidence,” found on pages 170 –
188.
Ryan, T. P. (1989). Statistical methods for quality improvement. New York, NY: Wiley.