Vector-borne diseases such as Dengue (DEN), Chikungunya (CHIK), Zika (ZIK), and Japanese

encephalitis (JE) are rising public health challenges in Southeast Asia. Aedes aegypti and Ae.

albopictus are the main vectors of Dengue virus (DENV), (CHIKV), and (ZIKV), while Culex

species are the main vectors of Japanese encephalitis virus (JEV). The emergence and

re-emergence of these diseases have posed significant public health challenges in Southeast

Asia, including Cambodia. Through the extensive efforts of entomologists in sampling larvae

across various ecosystems in Cambodia, a comprehensive database has been developed. This

database can now be utilized to create ecological models and map suitable habitats throughout

the country by integrating Geographic Information Systems (GIS), remote sensing technologies,

and statistical modeling. Such maps of Aedes and Culex mosquitoes are much awaited in

Cambodia, not only by the teams of entomologists working on these data but also by the health

services in charge of monitoring vector-borne diseases and those responsible for controlling

these mosquitoes.

This study seeks to develop ecological models of suitable habitats for the main mosquito

species, Ae. aegypti, Ae. albopictus, and C. quinquefasciatus that transmit viruses in Cambodia.

Satellite-based data describing the Land use / Land cover (LULC), topographic data, and

climate data will be prepared using QGIS software as the spatial variables to characterize the

ecological characteristics of the species. The study will describe the entomological data

collected according to different protocols (capture methods, choice of sites, number of sites, and

overnight stays), in order to determine the appropriate modeling methods for describing the

potential habitats of the main vector species. Presence-absence or abundance models will be

tested based on the biomod2 and other relevant packages in R statistical software. Finally, a

critical look should be taken at the various models' variability, to see which are the most

appropriate according to data type and spatial scale.