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)orcid ; 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
Field of Research (FOR): 090903 Geospatial Information Systems
090905 Photogrammetry and Remote Sensing
050206 Environmental Monitoring
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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