A scoping review of importation and predictive models related to vector-borne diseases, pathogens, reservoirs, or vectors (1999–2016)

Abstract

Background As globalization and climate change progress, the expansion and introduction of vectorborne diseases (VBD) from endemic regions to non-endemic regions is expected to occur. Mathematical and statistical models can be useful in predicting when and where these changes in distribution may happen. Our objective was to conduct a scoping review to identify and characterize predictive and importation models related to vector-borne diseases that exist in the global literature. Methods A literature search was conducted to identify publications published between 1999 and 2016 from five scientific databases using relevant keywords. All publications had to be in English or French, and include a predictive or importation model on VBDs, pathogens, reservoirs and/or vectors. Relevance screening and data characterization were performed by two reviewers using pretested forms. The data were analyzed using descriptive statistics. Results The search initially identified 19 710 unique articles, reports, and conference abstracts. This was reduced to 428 relevant documents after relevance screening and data charting. About half of the models used mathematical techniques, and the remainder were statistical. Most of the models were predictive (87%), rather than importation (5%). The most commonly investigated diseases were malaria and dengue fever. Around 12% of the publications did not report all the parameters used in their model. Only 29% of the models incorporated the impacts of climate change.

Outbreaks of Vector-Borne and Zoonotic Diseases Are Associated With Changes in Forest Cover and Oil Palm Expansion at Global Scale

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A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK

“…A recent review by Sadeghieh et al [15] found that current approaches to understanding and predicting VBD risk are typically focused on predicting risk in existing endemic zones (88% of VBD models in 1996-2016) rather than forecasting transmission risk in new regions. This disparity is likely because the ecology and epidemiology of disease systems may be poorly understood for epidemic zones.…”

Remote Sensing Applications in Disease Mapping

“…In such cases, spatial regression models had often been parameterized using data variables derived from remotely sensed products. As a result, there had been an unprecedented upsurge in the utilization of environmental variables and proxies derived from remote sensing in statistical analysis models attempting to map diseases such as vector-borne diseases, including malaria, dengue fever, chikungunya fever, zika virus, and leishmaniasis inter alia [22]. Studies mapping the spatial distribution of such vector-borne diseases had often applied remotely sensed data products as environmental proxies by approximating either environmental conditions or land use and land cover features.…”