A climate distribution model of malaria transmission in Sudan
Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was co...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2012-11-01
|
| Series: | Geospatial Health |
| Subjects: | |
| Online Access: | http://www.geospatialhealth.net/index.php/gh/article/view/102 |
| _version_ | 1819266195828768768 |
|---|---|
| author | Mohammed I. Musa Shamarina Shohaimi Nor R. Hashim Isthrinayagy Krishnarajah |
| author_facet | Mohammed I. Musa Shamarina Shohaimi Nor R. Hashim Isthrinayagy Krishnarajah |
| author_sort | Mohammed I. Musa |
| collection | DOAJ |
| description | Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the <em>Anopheles</em> mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate. |
| first_indexed | 2024-12-23T20:57:25Z |
| format | Article |
| id | doaj.art-dc5ac07ca8ab4c0ea9e2087c19f9659f |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-12-23T20:57:25Z |
| publishDate | 2012-11-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-dc5ac07ca8ab4c0ea9e2087c19f9659f2022-12-21T17:31:29ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962012-11-0171273610.4081/gh.2012.102102A climate distribution model of malaria transmission in SudanMohammed I. Musa0Shamarina Shohaimi1Nor R. Hashim2Isthrinayagy Krishnarajah3Department of Biology, Faculty of Science, Putra University, Selangor, Malaysia; Economic and Social Research Bureau, Ministry of Science and Technology, KhartoumDepartment of Biology, Faculty of Science, Putra University, SelangorDepartment of Environmental Sciences, Faculty of Environmental Studies, Putra University, SelangorDepartment of Mathematics, Faculty of Science, Putra University, Serdang, Selangor; Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Putra University, Serdang, SelangorMalaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the <em>Anopheles</em> mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.http://www.geospatialhealth.net/index.php/gh/article/view/102fuzzy logic, climate suitability, malaria rate, geographical information system, mapping, Sudan. |
| spellingShingle | Mohammed I. Musa Shamarina Shohaimi Nor R. Hashim Isthrinayagy Krishnarajah A climate distribution model of malaria transmission in Sudan Geospatial Health fuzzy logic, climate suitability, malaria rate, geographical information system, mapping, Sudan. |
| title | A climate distribution model of malaria transmission in Sudan |
| title_full | A climate distribution model of malaria transmission in Sudan |
| title_fullStr | A climate distribution model of malaria transmission in Sudan |
| title_full_unstemmed | A climate distribution model of malaria transmission in Sudan |
| title_short | A climate distribution model of malaria transmission in Sudan |
| title_sort | climate distribution model of malaria transmission in sudan |
| topic | fuzzy logic, climate suitability, malaria rate, geographical information system, mapping, Sudan. |
| url | http://www.geospatialhealth.net/index.php/gh/article/view/102 |
| work_keys_str_mv | AT mohammedimusa aclimatedistributionmodelofmalariatransmissioninsudan AT shamarinashohaimi aclimatedistributionmodelofmalariatransmissioninsudan AT norrhashim aclimatedistributionmodelofmalariatransmissioninsudan AT isthrinayagykrishnarajah aclimatedistributionmodelofmalariatransmissioninsudan AT mohammedimusa climatedistributionmodelofmalariatransmissioninsudan AT shamarinashohaimi climatedistributionmodelofmalariatransmissioninsudan AT norrhashim climatedistributionmodelofmalariatransmissioninsudan AT isthrinayagykrishnarajah climatedistributionmodelofmalariatransmissioninsudan |