Significant factors associated with malaria spread in Thailand: a cross-sectional study
Purpose – This paper aims to uncover new factors that influence the spread of malaria. Design/methodology/approach – The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain th...
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| Format: | Article |
| Language: | English |
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College of Public Health Sciences, Chulalongkorn University
2022-04-01
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| Series: | Journal of Health Research |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JHR-11-2020-0575/full/pdf?title=significant-factors-associated-with-malaria-spread-in-thailand-a-cross-sectional-study |
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| author | Patcharaporn Krainara Pongchai Dumrongrojwatthana Pattarasinee Bhattarakosol |
| author_facet | Patcharaporn Krainara Pongchai Dumrongrojwatthana Pattarasinee Bhattarakosol |
| author_sort | Patcharaporn Krainara |
| collection | DOAJ |
| description | Purpose – This paper aims to uncover new factors that influence the spread of malaria. Design/methodology/approach – The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution. Findings – This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree. Originality/value – This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally. |
| first_indexed | 2024-03-12T06:44:23Z |
| format | Article |
| id | doaj.art-a438ca0ac4b744c6b1cf6ea7376a21c9 |
| institution | Directory Open Access Journal |
| issn | 0857-4421 2586-940X |
| language | English |
| last_indexed | 2024-03-12T06:44:23Z |
| publishDate | 2022-04-01 |
| publisher | College of Public Health Sciences, Chulalongkorn University |
| record_format | Article |
| series | Journal of Health Research |
| spelling | doaj.art-a438ca0ac4b744c6b1cf6ea7376a21c92023-09-03T00:42:48ZengCollege of Public Health Sciences, Chulalongkorn UniversityJournal of Health Research0857-44212586-940X2022-04-0136351552310.1108/JHR-11-2020-0575663432Significant factors associated with malaria spread in Thailand: a cross-sectional studyPatcharaporn Krainara0Pongchai Dumrongrojwatthana1Pattarasinee Bhattarakosol2Faculty of Science, Chulalongkorn University, Bangkok, ThailandFaculty of Science, Chulalongkorn University, Bangkok, ThailandMathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, ThailandPurpose – This paper aims to uncover new factors that influence the spread of malaria. Design/methodology/approach – The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution. Findings – This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree. Originality/value – This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.https://www.emerald.com/insight/content/doi/10.1108/JHR-11-2020-0575/full/pdf?title=significant-factors-associated-with-malaria-spread-in-thailand-a-cross-sectional-studymalaria distributionmalaria controllogistic model treerisk factorsrisk modelthailand |
| spellingShingle | Patcharaporn Krainara Pongchai Dumrongrojwatthana Pattarasinee Bhattarakosol Significant factors associated with malaria spread in Thailand: a cross-sectional study Journal of Health Research malaria distribution malaria control logistic model tree risk factors risk model thailand |
| title | Significant factors associated with malaria spread in Thailand: a cross-sectional study |
| title_full | Significant factors associated with malaria spread in Thailand: a cross-sectional study |
| title_fullStr | Significant factors associated with malaria spread in Thailand: a cross-sectional study |
| title_full_unstemmed | Significant factors associated with malaria spread in Thailand: a cross-sectional study |
| title_short | Significant factors associated with malaria spread in Thailand: a cross-sectional study |
| title_sort | significant factors associated with malaria spread in thailand a cross sectional study |
| topic | malaria distribution malaria control logistic model tree risk factors risk model thailand |
| url | https://www.emerald.com/insight/content/doi/10.1108/JHR-11-2020-0575/full/pdf?title=significant-factors-associated-with-malaria-spread-in-thailand-a-cross-sectional-study |
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