Evaluation of the impacts of climate change on disease vectors through ecological niche modelling

Abstract

Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.

Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches

“…Four of the models (Bioclim, Domain, Maxent and GARP) are presence only models and they only need the set of presence records. Bioclim and Domain from DIVA-GIS software, PLOS NEGLECTED TROPICAL DISEASES version 7.1.7 (http://www.diva-gis.org/) were developed by constraining the range of environmental factors [26]. While Maxent and GARP are machine-learning programs, the former conducted by the stand-alone Maxent software, version 3.4.1 [27], and the latter based on biological evolution theory, which also has its stand-alone software, i.e.…”

High-resolution habitat suitability model for Phlebotomus pedifer, the vector of cutaneous leishmaniasis in southwestern Ethiopia

“…Identifying the vector distribution is pivotal for guidance of targeted integrated vector control, because places where P. pedifer occurs are either burdened by CL or vulnerable for a disease outbreak [55]. In this study, we designed a MaxEnt model resulting in a practical, high-resolution map indicating areas suitable for the presence of P. pedifer in ve zones in southwestern Ethiopia.…”

“…Therefore, it is sometimes suggested to perform a eld validation study to evaluate the accuracy of the model [55,[58][59][60][61]. However, it should be taken into account that not all individuals of a species live in optimal conditions, so it is possible to nd the species outside the predicted suitable habitat [62].…”

“…The importance of these climate variables could also indicate that the distribution of the vector could alter when the climate changes [36,55,73]. For Ethiopia, it is predicted that the temperature will rise, and rainfall will become erratic with ood and drought events likely to increase [74].…”

Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain

“…With the introduction of geographic information systems (GIS) and other geospatial tools, there is now growing interest in modelling vector distributions based on climate and environmental drivers for epidemiology studies and follow-up of arthropod vectors [15][16][17]. Statistical models help to determine the relative contribution of drivers to map vector occurrence or predict future vector distributions based on expected climate change [18]. In East Africa, scientists have had ample experience in surveying the bacterium Yersinia pestis, the causative agent of the plague, through GIS and Remote Sensing (RS) procedures [19][20][21][22][23].…”