Skip to content

Advertisement

Volume 11 Supplement 1

Challenges in malaria research

  • Poster presentation
  • Open Access

Standard Deviational Ellipse (SDE) models for malaria surveillance, case study: Sukabumi district-Indonesia, in 2012

  • 1,
  • 2,
  • 3 and
  • 4
Malaria Journal201211 (Suppl 1) :P130

https://doi.org/10.1186/1475-2875-11-S1-P130

  • Published:

Keywords

  • Malaria
  • Global Position System
  • Global Position System
  • Malaria Incidence
  • Malaria Endemic Area

Background

Sukabumi District has been a malaria endemic area for 8 years. In 2004 an outbreak of malaria occurred, and more than 250 positive malaria cases are reported every year. Malaria surveillance data is still in the form of tabular data [1], therefore it is necessary to find models to support the malaria surveillance, based on spatial mapping and analysis with the use of Standard Deviational Ellipse (SDE) models. Malaria distribution maps are a strategy to target resource distribution and to focus the control program [2].

Materials and methods

The research used a cross-sectional study. Data collection through Global Positioning System (GPS) plotting, surveys and interviews based on positive malaria cases 2011-2012 derived from public health centers. Analysis of data using overlay analysis with physical environment variables, spatial statistical analysis and SDE.

Results

The highest malaria incidence occurred at ambient temperature 22-25 degrees Celsius (70%), with an altitude of 500-1000m (71%) in the south hills and mountainous areas; rainfall is 3453-3846mm (50%) in the northern areas, the distance from breeding place less than 500m (84%), and interaction the physical environment with vector enabling an outbreak risk. Mean center of gravity was the center of the distribution of cases was Longitude: 106.602721 and Latitude: -7.118190. The locations of respondents were quite close together; this could mean that malaria was spread evenly due to import cases. The rotation angle of SDE was 58.524624 degrees clockwise and the area of ellipse was 146,109,759 square meters. Standard deviational ellipse as an overview of the standard deviation of the distribution showed that the length of the X axis was 15,936.83 m and the Y axis was 11,673.13 m, the ratio between the X and the Y axis wass equal to 1.3653 (Table 1). Direction of the axis of standard deviation ellipses appeared that the skewed distribution towards the northwest-southeast. Rainfall and temperature anomalies were two of the major environmental factors triggering epidemics in warm semi-arid and high altitude areas [35]. Physical environment of Sukabumi District supported the development and metabolism of vectors. The map provided an initial description of the geographic variation of malaria, and might assist in formulating various methods of intervention [6].
Table 1

Mean Center and SDE

Variable

Sub Variable

X

Y

Mean Center

Minimum

106.487241

-7.232089

 

Maximum

106.7401

-7.018312

 

Mean

106.602721

-7.11819

 

Standard Deviation

0.041497

0.047272

 

Geometric Mean

106.602713

-7.118034

 

Harmonic Mean

106.602705

-7.117879

SDE

SD along new axis

7968.41m

5836.57m

 

axis length

15936.83m

11673.13m

Conclusion

Standard Deviational Ellipse (SDE) models can be used to gain a better understanding of the geographical aspects of the phenomenon and identify the cause of an event, based on specific geographic patterns.

Authors’ Affiliations

(1)
Department of Biostatistics and Population Studies, Universitas Indonesia, Kampus Ul Depok, 16424, Indonesia
(2)
Department of Environmental Health, Universitas Indonesia, Kampus Ul Depok, 16424, Indonesia
(3)
Center For Biostatistics and Health Informatics Studies, Universitas Indonesia, Kampus Ul Depok, 16424, Indonesia
(4)
Center For Biostatistics and Health Informatics Studies, Faculty of Public Health, Universitas Indonesia, Kampus Ul Depok, 16424, Indonesia

References

  1. District Health Office: Reports of malaria cases in 2009-2011. Sukabumi DHO. 2011Google Scholar
  2. Grover-Kopec E, Kawano M, Klaver RW, Blumenthal B, Ceccato P, Connor SJ: An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal. 2005, 4: 6-10.1186/1475-2875-4-6.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Omumbo A, Lyon B, Waweru SM, Connor SJ, Thomson MC: Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate. Malaria Journal. 2011, 10: 12-10.1186/1475-2875-10-12.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Pietro Ceccato, Christelle Vancutsem, Robert Klaver, James Rowland, Stephen J Connor: A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa. Journal of Tropical Medicine. 2012, Article ID 595948, doi:10.1155/2012/595948Google Scholar
  5. Ubydul Haque, Ricardo Magalhães, Heidi Reid, Archie Clements, Syed Ahmed, Akramul Islam, Taro Yamamoto, Rashidul Haque, Gregory Glass: Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malaria Journal. 2010, 9: 120-10.1186/1475-2875-9-120.View ArticleGoogle Scholar
  6. Lawrence Kazembe, Immo Kleinschmidt, Timothy Holtz, Brian Sharp: Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. International Journal of Health Geographics. 2006, 5: 41-10.1186/1476-072X-5-41.View ArticleGoogle Scholar

Copyright

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Please note that comments may be removed without notice if they are flagged by another user or do not comply with our community guidelines.

Advertisement