Study protocol for a scoping review of Lyme disease prediction methodologies

IntroductionIn 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...

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Published inBMJ open Vol. 14; no. 5; p. e071402
Main Authors Szaroz, Daniel, Kulkarni, Manisha, Robayo González, Claudia Ximena, Zinszer, Kate
Format Journal Article
LanguageEnglish
Published England British Medical Journal Publishing Group 20.05.2024
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Abstract IntroductionIn 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).Methods and analysisThis 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.Ethics and disseminationThis 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.
AbstractList 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).INTRODUCTIONIn 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.METHODS AND ANALYSISThis 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.ETHICS AND DISSEMINATIONThis 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.
IntroductionIn 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).Methods and analysisThis 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.Ethics and disseminationThis 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.
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.
Introduction 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).Methods and analysis 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.Ethics and dissemination 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.
Author Szaroz, Daniel
Kulkarni, Manisha
Robayo González, Claudia Ximena
Zinszer, Kate
AuthorAffiliation 2 Centre de Recherche en Santé Publique (CReSP) , Montréal , Québec , Canada
1 École de santé publique, Département de médecine sociale et préventive , Université de Montréal , Montreal , Québec , Canada
3 School of Epidemiology and Public Health , University of Ottawa Faculty of Medicine , Ottawa , Ontario , Canada
AuthorAffiliation_xml – name: 1 École de santé publique, Département de médecine sociale et préventive , Université de Montréal , Montreal , Québec , Canada
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– name: 2 Centre de Recherche en Santé Publique (CReSP) , Montréal , Québec , Canada
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  surname: Szaroz
  fullname: Szaroz, Daniel
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  surname: Zinszer
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38772589$$D View this record in MEDLINE/PubMed
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Snippet IntroductionIn the temperate world, Lyme disease (LD) is the most common vector-borne disease affecting humans. In North America, LD surveillance and research...
In the temperate world, Lyme disease (LD) is the most common vector-borne disease affecting humans. In North America, LD surveillance and research have...
Introduction In the temperate world, Lyme disease (LD) is the most common vector-borne disease affecting humans. In North America, LD surveillance and research...
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SubjectTerms Accuracy
Arachnids
Epidemiology
Forecasting
Geographical mapping
Grey literature
Humans
Librarians
Lyme disease
Lyme Disease - diagnosis
Lyme Disease - epidemiology
Medical Subject Headings-MeSH
Methods
Public health
Research Design
Scoping Reviews As Topic
STATISTICS & RESEARCH METHODS
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Title Study protocol for a scoping review of Lyme disease prediction methodologies
URI https://bmjopen.bmj.com/content/14/5/e071402.full
https://www.ncbi.nlm.nih.gov/pubmed/38772589
https://www.proquest.com/docview/3056959098
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https://pubmed.ncbi.nlm.nih.gov/PMC11110606
https://doaj.org/article/0a0ecd9e0fac49c78456f679a968fa73
Volume 14
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