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 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.
Konchom
Supawadee
S
Bureau of Vector Borne Disease, Ministry of Public Health, Nonthaburi 11000, Thailand. jiabk@yahoo.com
Singhasivanon
Pratap
P
Kaewkungwal
Jaranit
J
Chuprapawan
Sirichai
S
Thimasarn
Krongthong
K
Kidson
Chev
C
Yimsamran
Surapon
S
Rojanawatsirivet
Chaiporn
C
D000818:Animals; D004199:Disease Vectors; D019353:Endemic Diseases; D006801:Humans; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D012621:Seasons; D012984:Software; D013785:Thailand / Q000453:epidemiology