Mapping the abundance of endemic mosquito-borne diseases vectors in southern Quebec

Abstract

Background Climate change is increasing the dispersion of mosquitoes and the spread of viruses of which some mosquitoes are the main vectors. In Quebec, the surveillance and management of endemic mosquito-borne diseases, such as West Nile virus or Eastern equine encephalitis, could be improved by mapping the areas of risk supporting vector populations. However, there is currently no active tool tailored to Quebec that can predict mosquito population abundances, and we propose, with this work, to help fill this gap. Methods Four species of mosquitos were studied in this project for the period from 2003 to 2016 for the southern part of the province of Quebec: Aedes vexans (VEX), Coquillettidia perturbans (CQP), Culex pipiens-restuans group (CPR) and Ochlerotatus stimulans group (SMG) species. We used a negative binomial regression approach, including a spatial component, to model the abundances of each species or species group as a function of meteorological and land-cover variables. We tested several sets of variables combination, regional and local scale variables for landcover and different lag period for the day of capture for weather variables, to finally select one best model for each species. Results Models selected showed the importance of the spatial component, independently of the environmental variables, at the larger spatial scale. In these models, the most important land-cover predictors that favored CQP and VEX were ‘forest’, and ‘agriculture’ (for VEX only). Land-cover ‘urban’ had negative impact on SMG and CQP. The weather conditions on the trapping day and previous weather conditions summarized over 30 or 90 days were preferred over a shorter period of seven days, suggesting current and long-term previous weather conditions effects on mosquito abundance. Conclusions The strength of the spatial component highlights the difficulties in modelling the abundance of mosquito species and the model selection shows the importance of selecting the right environmental predictors, especially when choosing the temporal and spatial scale of these variables. Climate and landscape variables were important for each species or species group, suggesting it is possible to consider their use in predicting long-term spatial variationsin the abundance of mosquitoes potentially harmful to public health in southern Quebec.

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

“…Climate variables significantly influence mosquito proliferation, thereby increasing the risk of disease emergence [ 56 ]. Mosquito abundance is cumulatively influenced by both seasonality and previous weather conditions [ 14 ]. Furthermore, temperature acts as a critical determinant governing vector and pathogen survival, their geographic distribution, and the transmission dynamics of the disease [ 38 , 57 ].…”

“…Incorporating both spatial and temporal analyses with an AHP enables the capture of certain complex facets that drive VBDs [ 14 , 63 ]. This synthesis offers substantial value in bolstering decision-making processes aimed at enhancing preparedness and formulating adaptative strategies to combat VBDs.…”

“…Nevertheless, modelling VBDs can be challenging due to the complex interactions between the vector, pathogen, hosts, and environmental conditions [ 13 ]. Moreover, vectors can have different requirements in different geographic areas due to localised factors [ 14 – 16 ].…”

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach.

“…Climate variables signi cantly in uence mosquito proliferation, thereby increasing the risk of disease emergence [53]. Mosquito abundance is cumulatively in uenced by both seasonality and previous weather conditions [14]. Furthermore, temperature acts as a critical determinant governing vector and pathogen survival, their geographic distribution, and the transmission dynamics of the disease [36], [54].…”

“…Nevertheless, modelling VBDs can be challenging due to the complex interactions between the vector, pathogen, hosts, and environmental conditions [13]. Moreover, vectors can have different requirements in different geographic areas due to localised factors [14]- [16].…”