Looking Forward by Looking Back: Using Historical Calibration to Improve Forecasts of Human Disease Vector Distributions

Abstract

Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.

Where have all the mosquito nets gone? Spatial modelling reveals mosquito net distributions across Tanzania do not target optimal Anopheles mosquito habitats

“…Despite Maxent’s tendency to be more conservative than other machine-learning techniques [ 43 ], it is one of the most reliable species distribution modelling methods, even with small numbers of species observations [ 44 , 45 ]. However, internal training and testing accuracies evaluate the model’s fit to existing observations and external tests against independent measurements or temporal environmental changes provide stronger tests of model performance [ 46 ].…”

“…For assessment of model accuracy, occurrence records were randomly partitioned into 75 % for model training and 25 % for model testing. Ten replicates were run for each model, using tenfold cross-validation, each with a randomized partitioning of training and testing data, a technique that can reliably test spatial model skill for disease vector distributions [ 46 ]. The habitat suitability raster maps were then averaged to determine the relative probability of suitability per grid cell.…”

Grandeur Alliances: Symbiont Metabolic Integration and Obligate Arthropod Hematophagy

“…Notably, analogous patterns of evolution have also occurred in the rich array of microbial partnerships of insects feeding on other types of restricted diets, such as phloem and xylem sap (reviewed in [19]). The geographic distribution of these and other blood-feeding arthropods is spreading at a historically alarming rate due to a variety of factors including environmental changes, pesticide resistance, globalization and the rise in urban landscapes [22-26]. These insects, as well as other blood-feeding arthropods, pose significant public health challenges because of the pathogens they transmit, the dermatological pathologies caused by bites (including allergic reactions and potential secondary infections with skin-associated pathogens), and the detrimental psychological ramifications associated with infections and/or infestations.…”

Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic

“…Modelling suitable habitats for arthropod vectors of human and animal diseases is a growing application of species distribution modelling (SDM) which helps in elucidating species-environment relationships and therefore can be used to support epidemiological studies or target disease surveillance [ 37 – 40 ]. A variety of methods such as Maximum Entropy (Maxent), Boosted Regression Trees (BRT), and Genetic Algorithm for Rule-set Production (GARP) have been widely explored and implemented to construct SDMs for mosquito vectors over the last decade [ 41 – 48 ].…”