Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions Article Swipe
Sophie Lee
,
Christopher I Jarvis
,
W. John Edmunds
,
Theo Economou
,
Rachel Lowe
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1098/rsif.2021.0096
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1098/rsif.2021.0096
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1098/rsif.2021.0096
- OA Status
- green
- Cited By
- 24
- References
- 289
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3164036231
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3164036231Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1098/rsif.2021.0096Digital Object Identifier
- Title
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Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptionsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-01Full publication date if available
- Authors
-
Sophie Lee, Christopher I Jarvis, W. John Edmunds, Theo Economou, Rachel LoweList of authors in order
- Landing page
-
https://doi.org/10.1098/rsif.2021.0096Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/8150046Direct OA link when available
- Concepts
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Spatial epidemiology, Spatial ecology, Computer science, Spatial analysis, Process (computing), Statistical model, Covariate, Ecology, Machine learning, Biology, Statistics, Mathematics, Operating system, Internal medicine, Epidemiology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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24Total citation count in OpenAlex
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2025: 4, 2024: 6, 2023: 5, 2022: 6, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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289Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_institution_ids | https://openalex.org/I4210089966 |
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