Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Jamie M. Caldwell; A. Desiree LaBeaud; Eric F. Lambin; Anna M. Stewart-Ibarra; Bryson A. Ndenga; Francis M. Mutuku; Amy R. Krystosik; Efraín Beltrán Ayala; Assaf Anyamba; Mercy J. Borbor-Cordova; Richard Damoah; Elysse N. Grossi-Soyster; Froilán Heras Heras; Harun N. Ngugi; Sadie J. Ryan; Melisa M. Shah; Rachel Sippy; Erin A. Mordecai

doi:10.1038/s41467-021-21496-7

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Jamie M. Caldwell, A. Desiree LaBeaud, Eric F. Lambin, Anna M. Stewart-Ibarra, Bryson A. Ndenga, Francis M. Mutuku, Amy R. Krystosik, Efraín Beltrán Ayala, Assaf Anyamba, Mercy J. Borbor-Cordova, Richard Damoah, Elysse N. Grossi-Soyster, Froilán Heras Heras, Harun N. Ngugi, Sadie J. Ryan, Melisa M. Shah, Rachel Sippy, Erin A. Mordecai

Research output: Contribution to journal › Article › peer-review

48 Scopus citations

Abstract

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.

Original language	English
Article number	1233
Journal	Nature Communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-21496-7
State	Published - Dec 1 2021
Externally published	Yes

Funding

J.M.C., A.D.L., E.F.L., and E.A.M. were supported by a Stanford Woods Institute for the Environment—Environmental Ventures Program grant (PIs: E.A.M., A.D.L., and E.F.L.). E.A.M. was also supported by a Hellman Faculty Fellowship and a Terman Award. A.D.L., B.A.N., F.M.M., E.N.G.S., M.S.S., A.R.K., R.D., A.A., and H.N.N. were supported by a National Institutes of Health R01 grant (AI102918; PI: A.D.L.). E.A.M., A.M.S.I., and S.J.R. were supported by a National Science Foundation (NSF) Ecology and Evolution of Infectious Diseases (EEID) grant (DEB-1518681), and A.M.S.I. and S.J.R. were also supported by an NSF DEB RAPID grant (1641145). E.A.M. was also supported by a National Institute of General Medical Sciences Maximizing Investigators’ Research Award grant (R35GM133439) and an NSF and Fogarty International Center EEID grant (DEB-2011147). We thank Cat Lippi for assistance with formatting household quality survey data from Ecuador.

Funders	Funder number
Stanford Woods Institute for the Environment—Environmental Ventures Program
National Science Foundation	1641145, DEB-1518681
National Institutes of Health	AI102918
National Institute of General Medical Sciences	R35GM133439
Fogarty International Center	DEB-2011147
Stanford Woods Institute for the Environment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41467-021-21496-7

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Caldwell, J. M., LaBeaud, A. D., Lambin, E. F., Stewart-Ibarra, A. M., Ndenga, B. A., Mutuku, F. M., Krystosik, A. R., Ayala, E. B., Anyamba, A., Borbor-Cordova, M. J., Damoah, R., Grossi-Soyster, E. N., Heras, F. H., Ngugi, H. N., Ryan, S. J., Shah, M. M., Sippy, R., & Mordecai, E. A. (2021). Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents. Nature Communications, 12(1), Article 1233. https://doi.org/10.1038/s41467-021-21496-7

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abstract = "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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Caldwell, JM, LaBeaud, AD, Lambin, EF, Stewart-Ibarra, AM, Ndenga, BA, Mutuku, FM, Krystosik, AR, Ayala, EB, Anyamba, A, Borbor-Cordova, MJ, Damoah, R, Grossi-Soyster, EN, Heras, FH, Ngugi, HN, Ryan, SJ, Shah, MM, Sippy, R & Mordecai, EA 2021, 'Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents', Nature Communications, vol. 12, no. 1, 1233. https://doi.org/10.1038/s41467-021-21496-7

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Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Abstract

Funding

UN SDGs

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