Alert Threshold Algorithms and Malaria Epidemic Detection

Teklehaimanot, Hailay Desta; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

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Alert Threshold Algorithms and Malaria Epidemic Detection

Jul 2004
By Teklehaimanot, Hailay Desta ; Schwartz, Joel ; Teklehaimanot, Awash ; ...
Source: Emerg Infect Dis. 10(7):1220-1226.

[PDF-173.46 KB]

English

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Details:

Alternative Title:

Emerg Infect Dis
Personal Author:

Teklehaimanot, Hailay Desta ; Schwartz, Joel ; Teklehaimanot, Awash ; Lipsitch, Marc

Teklehaimanot, Hailay Desta ; Schwartz, Joel ; Teklehaimanot, Awash ; Lipsitch, Marc Less -
Description:

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.
Subjects:

[+]

Algorithms Animals Disease Outbreaks Epidemiologic Methods Humans Malaria Models, Biological Research
Source:

Emerg Infect Dis. 10(7):1220-1226.
Document Type:

Journal Article
Place as Subject:

Ethiopia
Location:

Africa
Collection(s):

Emerging Infectious Diseases
Main Document Checksum:

[+]

urn:sha256:74bd3eb7646a3c00d640591273ac5e767fc9768213684d5154fe1d51aefd2841
Download URL:

https://stacks.cdc.gov/view/cdc/14490/cdc_14490_DS1.pdf
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