IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA DECISION MAKING METHOD
Development of an innovative method to enhance the detection of tuberculosis (TB) in Malaysia is the latest agenda of the Ministry of Health. Therefore, a geographical information system (GIS) based index model is proposed as an alternative method for defining potential high-risk areas of local TB c...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Natural Resources Canada
2016
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/36994/1/%28IDENTIFYING_HIGH-RISK_POPULATIONS%29_isprs-archives-XLII-4-W1-9-2016.pdf |
| _version_ | 1797009742314340352 |
|---|---|
| author | Abdul Rasam, A. R. Shariff, N. M. Dony, J. F. |
| author_facet | Abdul Rasam, A. R. Shariff, N. M. Dony, J. F. |
| author_sort | Abdul Rasam, A. R. |
| collection | USM |
| description | Development of an innovative method to enhance the detection of tuberculosis (TB) in Malaysia is the latest agenda of the Ministry of Health. Therefore, a geographical information system (GIS) based index model is proposed as an alternative method for defining potential high-risk areas of local TB cases at Section U19, Shah Alam. It is adopted a spatial multi-criteria decision making (MCDM) method for ranking environmental risk factors of the disease in a standardised five-score scale. Scale 1 and 5 illustrate the lowest and the highest risk of the TB spread respectively, while scale from 3 to 5 is included as a potential risk level. These standardised scale values are then combined with expert normalised weights (0 to 1) to calculate the overall index values and produce a TB ranked map using a GIS overlay analysis and weighted linear combination. It is discovered that 71.43% of the Section is potential as TB high risk areas particularly at urban and densely populated settings. This predictive result is also reliable with the current real cases in 2015 by 76.00% accuracy. A GIS based MCDM method has demonstrated analytical capabilities in targeting high-risk spots and TB surveillance monitoring system of the country, but the result could be strengthened by applying other uncertainty assessment method. |
| first_indexed | 2024-03-06T15:09:53Z |
| format | Article |
| id | usm.eprints-36994 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:09:53Z |
| publishDate | 2016 |
| publisher | Natural Resources Canada |
| record_format | dspace |
| spelling | usm.eprints-369942017-10-10T01:41:21Z http://eprints.usm.my/36994/ IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA DECISION MAKING METHOD Abdul Rasam, A. R. Shariff, N. M. Dony, J. F. LC5800-5808 Distance education. Development of an innovative method to enhance the detection of tuberculosis (TB) in Malaysia is the latest agenda of the Ministry of Health. Therefore, a geographical information system (GIS) based index model is proposed as an alternative method for defining potential high-risk areas of local TB cases at Section U19, Shah Alam. It is adopted a spatial multi-criteria decision making (MCDM) method for ranking environmental risk factors of the disease in a standardised five-score scale. Scale 1 and 5 illustrate the lowest and the highest risk of the TB spread respectively, while scale from 3 to 5 is included as a potential risk level. These standardised scale values are then combined with expert normalised weights (0 to 1) to calculate the overall index values and produce a TB ranked map using a GIS overlay analysis and weighted linear combination. It is discovered that 71.43% of the Section is potential as TB high risk areas particularly at urban and densely populated settings. This predictive result is also reliable with the current real cases in 2015 by 76.00% accuracy. A GIS based MCDM method has demonstrated analytical capabilities in targeting high-risk spots and TB surveillance monitoring system of the country, but the result could be strengthened by applying other uncertainty assessment method. Natural Resources Canada 2016-10 Article PeerReviewed application/pdf en http://eprints.usm.my/36994/1/%28IDENTIFYING_HIGH-RISK_POPULATIONS%29_isprs-archives-XLII-4-W1-9-2016.pdf Abdul Rasam, A. R. and Shariff, N. M. and Dony, J. F. (2016) IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA DECISION MAKING METHOD. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, XLII (4/W1). pp. 9-13. ISSN 1682-1750 https://doi.org/10.5194/isprs-archives-XLII-4-W1-9-2016 |
| spellingShingle | LC5800-5808 Distance education. Abdul Rasam, A. R. Shariff, N. M. Dony, J. F. IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA DECISION MAKING METHOD |
| title | IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA
DECISION MAKING METHOD |
| title_full | IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA
DECISION MAKING METHOD |
| title_fullStr | IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA
DECISION MAKING METHOD |
| title_full_unstemmed | IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA
DECISION MAKING METHOD |
| title_short | IDENTIFYING HIGH-RISK POPULATIONS OF TUBERCULOSIS USING ENVIRONMENTAL FACTORS AND GIS BASED MULTI-CRITERIA
DECISION MAKING METHOD |
| title_sort | identifying high risk populations of tuberculosis using environmental factors and gis based multi criteria decision making method |
| topic | LC5800-5808 Distance education. |
| url | http://eprints.usm.my/36994/1/%28IDENTIFYING_HIGH-RISK_POPULATIONS%29_isprs-archives-XLII-4-W1-9-2016.pdf |
| work_keys_str_mv | AT abdulrasamar identifyinghighriskpopulationsoftuberculosisusingenvironmentalfactorsandgisbasedmulticriteriadecisionmakingmethod AT shariffnm identifyinghighriskpopulationsoftuberculosisusingenvironmentalfactorsandgisbasedmulticriteriadecisionmakingmethod AT donyjf identifyinghighriskpopulationsoftuberculosisusingenvironmentalfactorsandgisbasedmulticriteriadecisionmakingmethod |