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Comparative Study
. 2004 Jul;10(7):1220-6.
doi: 10.3201/eid1007.030722.

Alert threshold algorithms and malaria epidemic detection

Hailay Desta Teklehaimanot  1 , Joel Schwatrz, Awash Teklehaimanot, Marc Lipsitch
Affiliations
Comparative Study

Alert threshold algorithms and malaria epidemic detection

Hailay Desta Teklehaimanot et al. Emerg Infect Dis. 2004 Jul.

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.

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Figures

Figure 1
Figure 1
Method for calculating potentially preventable cases (PPC) by using weekly mean. PPC is obtained from cases in excess of the weekly mean with an 8-week window.
Figure 2
Figure 2
Percent of potentially preventable cases (PPC) by number of alerts per year for different algorithms. (A) and (B) were obtained from cases in excess of the weekly mean with window of effectiveness of 8 and 24 weeks, respectively. (C) and (D) were obtained from cases in excess of the weekly mean minus one SD for window of 8 and 24 weeks, respectively. The scale of y-axis is higher for (B) and (D) because they are based on 24 weeks of PPC (based on the random alert, the %PPC for the 24-week window is three times that of the 8-week window of effectiveness).
Figure 3
Figure 3
Percent of potentially preventable cases (PPC) obtained using weekly and monthly data with an 8-week window.
Figure A1
Figure A1
Time series of normalized weekly average daily malaria cases for 10 districts. Years are according to the Ethiopian calendar, in which year y begins on September 11 of year y+7 in the Western calendar.
Figure A2
Figure A2
Percent potentially preventable cases (PPC) by number of alerts per year from all districts for each alert threshold algorithm.
See this image and copyright information in PMC

References

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