Abstract
Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.
Highlights
Mosquitoes are one of the most notorious and influential insects in the public health field [1]
We investigated the importance of meteorological factors in predicting the occurrence of mosquitoes according to the land use type
We evaluated the relationships between the occurrence patterns of mosquitoes and land use types in two phases
Summary
Mosquitoes are one of the most notorious and influential insects in the public health field [1]. Udevitz et al [12] predicted the occurrence of four mosquito species (Anopheles punctipennis, Culex territans, Aedes atlanticus, and Psorophora ferox) based on physico-chemical factors using stepwise logistic regression; Hales et al [13] predicted the global distribution of dengue fever under current and future climates based on regressions with macroclimatic data; Peterson et al [14] used a machine-learning approach to describe patterns of mosquito occurrence through space and time; Kearney et al [15] predicted climate impacts on the potential range of the dengue fever vector, A. aegypti, based on biophysical models of energy and mass transfer; and Ruiz et al [16] evaluated the impact of temperature and precipitation on West Nile virus infection in Culex species using random forest
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