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Study protocol for a scoping review of Lyme disease prediction methodologies
Daniel Szaroz1,2, Manisha Kulkarni3, Claudia Ximena Robayo González4,2
1École de santé publique, Département de médecine sociale et préventive, Université de Montréal, Montreal, Québec, Canada daniel.szaroz@umontreal.ca.
BMJ Open
|May 21, 2024
Summary
This review maps Lyme disease (LD) prediction models, identifying forecasting methods, predictors, and performance evaluations. Understanding these models is crucial for tracking the expanding threat of this common vector-borne disease.
Area of Science:
- Epidemiology
- Public Health
- Infectious Diseases
Background:
- Lyme disease (LD) is the most prevalent human vector-borne illness in temperate regions.
- Increasing incidence and geographic spread of LD in North America are observed.
- Predictive modeling is essential for understanding and forecasting LD dynamics.
Purpose of the Study:
- To conduct a scoping review of modeling approaches for Lyme disease risk prediction.
- To document various forecasting and prediction methods used in LD research.
- To identify common predictors and performance evaluation strategies in LD spatial and temporal models.
Main Methods:
- Adherence to PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines.
- Comprehensive search of major scientific databases (PubMed/MEDLINE, EMBASE, CAB Abstracts, Global Health, SCOPUS).
- Screening of English and French studies on human LD risk using spatial and temporal prediction methodologies.
Main Results:
- Data extraction and synthesis of diverse modeling approaches.
- Identification of key predictors and evaluation metrics for LD risk models.
- Charting of current practices in LD forecasting and spatial prediction.
Conclusions:
- This review provides a comprehensive overview of LD modeling techniques.
- Findings will inform future research and public health strategies for Lyme disease surveillance and control.
- Understanding model variability is key to addressing the expanding Lyme disease burden.
