Alert Threshold Algorithms and Malaria Epidemic Detection

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Alert Threshold Algorithms and Malaria Epidemic Detection

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Title: Alert Threshold Algorithms and Malaria Epidemic Detection
Author: Teklehaimanot, Hailay Desta; Teklehaimanot, Awash; Schwartz, Joel David; Lipsitch, Marc

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Citation: Teklehaimanot, Hailay Desta, Joel Schwartz, Awash Teklehaimanot, and Marc Lipsitch. 2004. Alert threshold algorithms and malaria epidemic detection. Emerging Infectious Diseases 10(7): 1220-1226.
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Abstract: We describe a method for comparing the ability of different alert threshold algorithms to detect malaria epidemics and use it with a dataset consisting of weekly malaria cases collected from health facilities in 10 districts of Ethiopia from 1990 to 2000. Four types of alert threshold algorithms are compared: weekly percentile, weekly mean with standard deviation (simple, moving average, and log-transformed case numbers), slide positivity proportion, and slope of weekly cases on log scale. To compare dissimilar alert types on a single scale, a curve was plotted for each type of alert, which showed potentially prevented cases versus number of alerts triggered over 10 years. Simple weekly percentile cutoffs appear to be as good as more complex algorithms for detecting malaria epidemics in Ethiopia. The comparative method developed here may be useful for testing other proposed alert thresholds and for application in other populations.
Published Version: doi:10.3201/eid1007.030722
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323320/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10246864
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