Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents Article Swipe
Jamie M. Caldwell
,
A. Desirée LaBeaud
,
Éric F. Lambin
,
Anna M. Stewart‐Ibarra
,
Bryson Ndenga
,
Francis Mutuku
,
Amy Krystosik
,
Efraín Beltrán Ayala
,
Assaf Anyamba
,
Mercy J. Borbor‐Cordova
,
R. Damoah
,
Elysse N. Grossi-Soyster
,
Froilán Heras
,
Harun N. Ngugi
,
Sadie J. Ryan
,
Melisa M. Shah
,
Rachel Sippy
,
Erin A. Mordecai
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.02.07.938720
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.02.07.938720
Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28 – 85% for vectors, 44 – 88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.02.07.938720
- https://www.biorxiv.org/content/biorxiv/early/2021/01/13/2020.02.07.938720.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 92
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3004832016Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.02.07.938720Digital Object Identifier
- Title
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Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continentsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-07Full publication date if available
- Authors
-
Jamie M. Caldwell, A. Desirée LaBeaud, Éric F. Lambin, Anna M. Stewart‐Ibarra, Bryson Ndenga, Francis Mutuku, Amy Krystosik, Efraín Beltrán Ayala, Assaf Anyamba, Mercy J. Borbor‐Cordova, R. Damoah, Elysse N. Grossi-Soyster, Froilán Heras, Harun N. Ngugi, Sadie J. Ryan, Melisa M. Shah, Rachel Sippy, Erin A. MordecaiList of authors in order
- Landing page
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https://doi.org/10.1101/2020.02.07.938720Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/01/13/2020.02.07.938720.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2021/01/13/2020.02.07.938720.full.pdfDirect OA link when available
- Concepts
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Outbreak, Dengue fever, Climate change, Context (archaeology), Chikungunya, Geography, Aedes aegypti, Ecology, Population, Climate model, Climatology, Environmental science, Biology, Environmental health, Virology, Medicine, Geology, Archaeology, LarvaTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2024: 1, 2023: 3, 2022: 1, 2021: 3, 2019: 2Per-year citation counts (last 5 years)
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92Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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