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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 journalArticlepeer-review

90 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 languageEnglish
Article number1233
JournalNature Communications
Volume12
Issue number1
DOIs
StatePublished - Dec 1 2021
Externally publishedYes

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.

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