Study protocol for a scoping review of Lyme disease prediction methodologies

In the temperate world, Lyme disease (LD) is the most common vector-borne disease affecting humans. In North America, LD surveillance and research have revealed an increasing territorial expansion of hosts, bacteria and vectors that has accompanied an increasing incidence of the disease in humans. To better understand the factors driving disease spread, predictive models can use current and historical data to predict disease occurrence in populations across time and space. Various prediction methods have been used, including approaches to evaluate prediction accuracy and/or performance and a range of predictors in LD risk prediction research. With this scoping review, we aim to document the different modelling approaches including types of forecasting and/or prediction methods, predictors and approaches to evaluating model performance (eg, accuracy).

This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review guidelines. Electronic databases will be searched via keywords and subject headings (eg, Medical Subject Heading terms). The search will be performed in the following databases: PubMed/MEDLINE, EMBASE, CAB Abstracts, Global Health and SCOPUS. Studies reported in English or French investigating the risk of LD in humans through spatial prediction and temporal forecasting methodologies will be identified and screened. Eligibility criteria will be applied to the list of articles to identify which to retain. Two reviewers will screen titles and abstracts, followed by a full-text screening of the articles' content. Data will be extracted and charted into a standard form, synthesised and interpreted.

This scoping review is based on published literature and does not require ethics approval. Findings will be published in peer-reviewed journals and presented at scientific conferences.