Early detection of malaria in an endemic area: Model development

A malaria epidemic warning system was established in Thailand in 1984 using graphs displaying the median or mean incidence of malaria over the previous five years compiled from malaria surveillance data throughout the country. This reporting mechanism is not timely enough to detect the occurrence of...

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Main Authors: Supawadee Konchom, Pratap Singhasivanon, Jaranit Kaewkungwal, Sirichai Chuprapawan, Krongthong Thimasarn, Chev Kidson, Surapon Yimsamran, Chaiporn Rojanawatsirivet
Other Authors: Thailand Ministry of Public Health
Format: Article
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/23509
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spelling th-mahidol.235092018-08-20T14:08:24Z Early detection of malaria in an endemic area: Model development Supawadee Konchom Pratap Singhasivanon Jaranit Kaewkungwal Sirichai Chuprapawan Krongthong Thimasarn Chev Kidson Surapon Yimsamran Chaiporn Rojanawatsirivet Thailand Ministry of Public Health Mahidol University Roll Back Malaria Mekong SEAMEO-TROPMED Network Bureau of Vector Borne Disease Medicine A malaria epidemic warning system was established in Thailand in 1984 using graphs displaying the median or mean incidence of malaria over the previous five years compiled from malaria surveillance data throughout the country. This reporting mechanism is not timely enough to detect the occurrence of a malaria epidemic which usually occurs at the district level over a short period of time. An alternative method for early detection of a malaria epidemic employing the Poisson model has been proposed. The development of this early malaria epidemic detection model involved 3 steps: model specification, model validation and model testing. The model was based on data collected at the Vector Borne Disease Control Unit (VBDU) Level. The results of model testing reveal the model can detect increasing numbers of cases earlier, one to two weeks prior to reaching their highest peak of transmission. The system was tested using data from Kanchanaburi Province during 2000 to 2001. Results from model testing show the model may be used for monitoring the weekly malaria situation at the district level. The Poisson model was able to detect malaria early in a highly endemic province with a satisfactory level of prediction. As the application is essential for the malaria officers in monitoring of malaria epidemics, this early detection system was introduced into malaria epidemiological work. The model may be helpful in the decision making process, planning and budget allocation for the Malaria Control Program. The software for early malaria detection is currently implemented in several endemic areas throughout Thailand. 2018-08-20T07:08:24Z 2018-08-20T07:08:24Z 2006-11-01 Article Southeast Asian Journal of Tropical Medicine and Public Health. Vol.37, No.6 (2006), 1067-1071 01251562 2-s2.0-33846817238 https://repository.li.mahidol.ac.th/handle/123456789/23509 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33846817238&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Supawadee Konchom
Pratap Singhasivanon
Jaranit Kaewkungwal
Sirichai Chuprapawan
Krongthong Thimasarn
Chev Kidson
Surapon Yimsamran
Chaiporn Rojanawatsirivet
Early detection of malaria in an endemic area: Model development
description A malaria epidemic warning system was established in Thailand in 1984 using graphs displaying the median or mean incidence of malaria over the previous five years compiled from malaria surveillance data throughout the country. This reporting mechanism is not timely enough to detect the occurrence of a malaria epidemic which usually occurs at the district level over a short period of time. An alternative method for early detection of a malaria epidemic employing the Poisson model has been proposed. The development of this early malaria epidemic detection model involved 3 steps: model specification, model validation and model testing. The model was based on data collected at the Vector Borne Disease Control Unit (VBDU) Level. The results of model testing reveal the model can detect increasing numbers of cases earlier, one to two weeks prior to reaching their highest peak of transmission. The system was tested using data from Kanchanaburi Province during 2000 to 2001. Results from model testing show the model may be used for monitoring the weekly malaria situation at the district level. The Poisson model was able to detect malaria early in a highly endemic province with a satisfactory level of prediction. As the application is essential for the malaria officers in monitoring of malaria epidemics, this early detection system was introduced into malaria epidemiological work. The model may be helpful in the decision making process, planning and budget allocation for the Malaria Control Program. The software for early malaria detection is currently implemented in several endemic areas throughout Thailand.
author2 Thailand Ministry of Public Health
author_facet Thailand Ministry of Public Health
Supawadee Konchom
Pratap Singhasivanon
Jaranit Kaewkungwal
Sirichai Chuprapawan
Krongthong Thimasarn
Chev Kidson
Surapon Yimsamran
Chaiporn Rojanawatsirivet
format Article
author Supawadee Konchom
Pratap Singhasivanon
Jaranit Kaewkungwal
Sirichai Chuprapawan
Krongthong Thimasarn
Chev Kidson
Surapon Yimsamran
Chaiporn Rojanawatsirivet
author_sort Supawadee Konchom
title Early detection of malaria in an endemic area: Model development
title_short Early detection of malaria in an endemic area: Model development
title_full Early detection of malaria in an endemic area: Model development
title_fullStr Early detection of malaria in an endemic area: Model development
title_full_unstemmed Early detection of malaria in an endemic area: Model development
title_sort early detection of malaria in an endemic area: model development
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/23509
_version_ 1763493416743731200