Species Distribution Modelling of Aedes aegypti in two dengue‐endemic regions of Pakistan

Abstract

Abstractobjectives Statistical tools are effectively used to determine the distribution of mosquitoes and to make ecological inferences about the vector-borne disease dynamics. In this study, we utilised species distribution models to understand spatial patterns of Aedes aegypti in two dengue-prevalent regions of Pakistan, Lahore and Swat. Species distribution models can potentially indicate the probability of suitability of Ae. aegypti once introduced to new regions like Swat, where invasion of this species is a recent phenomenon.methods The distribution of Ae. aegypti was determined by applying the MaxEnt algorithm on a set of potential environmental factors and species sample records. The ecological dependency of species on each environmental variable was analysed using response curves. We quantified the statistical performance of the models based on accuracy assessment and spatial predictions.results Our results suggest that Ae. aegypti is widely distributed in Lahore. Human population density and urban infrastructure are primarily responsible for greater probability of mosquito occurrence in this region. In Swat, Ae. aegypti has clumped distribution, where urban patches provide refuge to the species in an otherwise hostile heterogeneous environment and road networks are assumed to have facilitated in passive-mediated dispersal of species.conclusions In Pakistan, Ae. aegypti is expanding its range northwards; this could be associated with rapid urbanisation, trade and travel. The main implication of this expansion is that more people are at risk of dengue fever in the northern highlands of Pakistan.

Geographic shifts in the bioclimatic suitability for Aedes aegypti under climate change scenarios in Colombia

“…MaxEnt was chosen because it is one of the most frequently and accurate methods of predicting the distribution of species (Elith et al, 2006;Phillips et al, 2006;Wisz et al, 2008;Ortega-Huerta and Peterson, 2008;Tognelli et al, 2009;Gomes et al, 2018) and is one of the most-used to predict the geographic distribution of insects, such as A. aegypti (Cardoso-Leite et al, 2014; Kraemer et al, 2015;Fatima et al, 2016;Khan et al, 2016;Alaniz et al, 2017). Besides, it is one of the models that presents the best prediction capabilities when different sample sizes are used, and even with sizes less than 25 occurrence data (Pearson et al, 2007;Phillips and Dudík, 2008;Wisz et al, 2008).…”

Environmental risk modelling and potential sand fly vectors of cutaneous leishmaniasis in Chitral district: a leishmanial focal point of mount Tirich Mir, Pakistan

“…MaxEnt is widely used to determine the impact of ecological conditions on the distribution of vector‐borne disease . The input data are the known occurrence points and digital environmental layers.…”

Current status of dengue virus, poliovirus, and chikungunya virus in Pakistan

“…While CHIKV has not yet been identified in other cities, the majority of regions in the country are the breeding sites of A aegypti and A albopictus, both of which are the primary vectors of CHIKV . Therefore, CHIKV poses a potential threat to the health of people living in regions where the vector thrives.…”