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 in | BMJ open Vol. 14; no. 5; p. e071402 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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England
British Medical Journal Publishing Group
20.05.2024
BMJ Publishing Group LTD BMJ Publishing Group |
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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. |
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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 – name: 3 School of Epidemiology and Public Health , University of Ottawa Faculty of Medicine , Ottawa , Ontario , Canada – name: 2 Centre de Recherche en Santé Publique (CReSP) , Montréal , Québec , Canada |
Author_xml | – sequence: 1 givenname: Daniel orcidid: 0000-0002-2608-4228 surname: Szaroz fullname: Szaroz, Daniel email: daniel.szaroz@umontreal.ca organization: Centre de Recherche en Santé Publique (CReSP), Montréal, Québec, Canada – sequence: 2 givenname: Manisha orcidid: 0000-0002-5084-4960 surname: Kulkarni fullname: Kulkarni, Manisha organization: School of Epidemiology and Public Health, University of Ottawa Faculty of Medicine, Ottawa, Ontario, Canada – sequence: 3 givenname: Claudia Ximena orcidid: 0000-0003-2101-7955 surname: Robayo González fullname: Robayo González, Claudia Ximena organization: Centre de Recherche en Santé Publique (CReSP), Montréal, Québec, Canada – sequence: 4 givenname: Kate orcidid: 0000-0003-1388-1145 surname: Zinszer fullname: Zinszer, Kate organization: Centre de Recherche en Santé Publique (CReSP), Montréal, Québec, Canada |
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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SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
StartPage | e071402 |
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 |
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