Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/15639
Title: | Regression Model for Predicting Adult Female 'Aedes aegypti' Based on Meteorological Variables: A Case Study of Jeddah, Saudi Arabia | Contributor(s): | Khormi, Hassan (author); Kumar, Lalit (author) ; Elzahrany, Ramze (author) | Publication Date: | 2013 | DOI: | 10.4172/2157-7617.1000168 | Handle Link: | https://hdl.handle.net/1959.11/15639 | Abstract: | Considerable interest exists in confirming that meteorological variables may play determinant roles in dengue vector abundance. The principle vector for dengue is 'Aedes aegypti'. Dengue Fever has been considered the most important vector-borne viral disease in Jeddah, Saudi Arabia, and is susceptible to climate variability. The aim of this study is to describe the association between adult female 'Aedes aegypti' mosquitoes and meteorological variables and to develop models for predicting the mosquito abundance using Pearson's correlation and regression analyses. Our results show that mosquitoes have the highest correlation with temperature at lag 0 time and relative humidity at lag 5 weeks. The highest two correlations were found between the mosquitoes and minimum temperature (r=-0.57) and maximum relative humidity (r=0.46). Two models were created based on the regression analysis results. The first model shows that 86% of mosquito values were within the upper and lower limits of agreement. The second model shows that 94% of the values were within the limits of agreement. The study findings could contribute to the forecasting of mosquito abundance peaks and subsequently guide a plan for mosquito control operations ahead of time that would help to minimize the outbreak of dengue occurrence and prevent the spread of dengue infections. | Publication Type: | Journal Article | Source of Publication: | Journal of Earth Science & Climatic Change, 5(1), p. 1-8 | Publisher: | Omics Publishing Group | Place of Publication: | United States of America | ISSN: | 2157-7617 | Fields of Research (FoR) 2008: | 090903 Geospatial Information Systems 090905 Photogrammetry and Remote Sensing 050206 Environmental Monitoring |
Fields of Research (FoR) 2020: | 401302 Geospatial information systems and geospatial data modelling 401304 Photogrammetry and remote sensing 410599 Pollution and contamination not elsewhere classified |
Socio-Economic Objective (SEO) 2008: | 960411 Control of Pests, Diseases and Exotic Species in Urban and Industrial Environments 960604 Environmental Management Systems 960405 Control of Pests, Diseases and Exotic Species at Regional or Larger Scales |
Socio-Economic Objective (SEO) 2020: | 180204 Control of pests, diseases and exotic species in coastal and estuarine environments 180302 Control of pests, diseases and exotic species in fresh, ground and surface water 180602 Control of pests, diseases and exotic species in terrestrial environments |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Environmental and Rural Science |
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