Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping

Abstract

Asian tiger and yellow fever mosquitoes (Aedes albopictus and Ae. aegypti) are global nuisances and are competent vectors for viruses such as Chikungunya (CHIKV), Dengue (DV), and Zika (ZIKV). This review aims to analyze available spatiotemporal distribution models of Aedes mosquitoes and their influential factors. A combination of five sets of 3–5 keywords were used to retrieve all relevant published models. Five electronic search databases were used: PubMed, MEDLINE, EMBASE, Scopus, and Google Scholar through 17 May 2017. We generated a hierarchical decision tree for article selection. We identified 21 relevant published studies that highlight different combinations of methodologies, models and influential factors. Only a few studies adopted a comprehensive approach highlighting the interaction between environmental, socioeconomic, meteorological and topographic systems. The selected articles showed inconsistent findings in terms of number and type of influential factors affecting the distribution of Aedes vectors, which is most likely attributed to: (i) limited availability of high-resolution data for physical variables, (ii) variation in sampling methods; Aedes feeding and oviposition behavior; (iii) data collinearity and statistical distribution of observed data. This review highlights the need and sets the stage for a rigorous multi-system modeling approach to improve our knowledge about Aedes presence/abundance within their flight range in response to the interaction between environmental, socioeconomic, and meteorological systems.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

“…The authors of Reference [8] have shown that Aedes distributions are currently the widest ever recorded, and are now extensive in all continents, including North America and Europe. Both species have become increasingly capable of exploiting man-made container habitats and human blood meal hosts [9,10], demonstrating their high-level of ecological plasticity and remarkable adaptation to urban settings [11]. The abundance and distribution of Aedes mosquitoes are influenced by climatic, topographic, land use and land cover (LULC) factors [10].…”

“…Both species have become increasingly capable of exploiting man-made container habitats and human blood meal hosts [9,10], demonstrating their high-level of ecological plasticity and remarkable adaptation to urban settings [11]. The abundance and distribution of Aedes mosquitoes are influenced by climatic, topographic, land use and land cover (LULC) factors [10]. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection is not straightforward [12], and it is difficult to identify a minimal entomological threshold for dengue transmission [13].…”

Spatial Distribution of Discarded Vehicle Tires and Their Influence onAedes albopictusandCulex quinquefasciatusPopulations, New Orleans, Louisiana

“…For example, our regression models showed that the distribution range of Ae. albopictus was predicted by a combination of macro-and microhabitat variables such as muddy water quality ( r 2 =25.40) and developed medium intensity areas ( r 2 =22.44) ( Sallam et al 2017b ). These findings were consistent with other researches, which found a link between Ae.…”

A 15 Year Evaluation of West Nile Virus in Wisconsin: Effects on Wildlife and Human Health

“…Regression values, calculated using the PROC REG procedure in SAS 9.4, compose the ecological modeling for WNV and inform the relative effects (positive or negatively associated with disease) and magnitude (weighted values of disease effect) of specific environmental or climatic factors for each specimen group (avian, corvid, equine, or human) submitted for testing. The selection of final explanatory variables was based on overall lowest Akaike information criterion (AIC) values via backward elimination, as this method reduces both model over-fitting and collinearity between variables [35,36]. Data was organized, processed, and visualized using JMP 11.0.0 (SAS Institute, Cary, NC, USA) [27].…”