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Alert Threshold Algorithms and Malaria Epidemic Detection

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...

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Autores principales: Teklehaimanot, Hailay Desta, Schwartz, Joel, Teklehaimanot, Awash, Lipsitch, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323320/
https://www.ncbi.nlm.nih.gov/pubmed/15324541
http://dx.doi.org/10.3201/eid1007.030722
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author Teklehaimanot, Hailay Desta
Schwartz, Joel
Teklehaimanot, Awash
Lipsitch, Marc
author_facet Teklehaimanot, Hailay Desta
Schwartz, Joel
Teklehaimanot, Awash
Lipsitch, Marc
author_sort Teklehaimanot, Hailay Desta
collection PubMed
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.
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spelling pubmed-33233202012-04-17 Alert Threshold Algorithms and Malaria Epidemic Detection Teklehaimanot, Hailay Desta Schwartz, Joel Teklehaimanot, Awash Lipsitch, Marc Emerg Infect Dis Research 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. Centers for Disease Control and Prevention 2004-07 /pmc/articles/PMC3323320/ /pubmed/15324541 http://dx.doi.org/10.3201/eid1007.030722 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Research
Teklehaimanot, Hailay Desta
Schwartz, Joel
Teklehaimanot, Awash
Lipsitch, Marc
Alert Threshold Algorithms and Malaria Epidemic Detection
title Alert Threshold Algorithms and Malaria Epidemic Detection
title_full Alert Threshold Algorithms and Malaria Epidemic Detection
title_fullStr Alert Threshold Algorithms and Malaria Epidemic Detection
title_full_unstemmed Alert Threshold Algorithms and Malaria Epidemic Detection
title_short Alert Threshold Algorithms and Malaria Epidemic Detection
title_sort alert threshold algorithms and malaria epidemic detection
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323320/
https://www.ncbi.nlm.nih.gov/pubmed/15324541
http://dx.doi.org/10.3201/eid1007.030722
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