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Effects of changes in temperature on Zika dynamics and control.

Authors
  • Ngonghala, Calistus N1, 2
  • Ryan, Sadie J2, 3
  • Tesla, Blanka4, 5
  • Demakovsky, Leah R4
  • Mordecai, Erin A6
  • Murdock, Courtney C4, 7, 8, 9, 10, 11
  • Bonds, Matthew H12
  • 1 Department of Mathematics, University of Florida, Gainesville, FL 32611, USA.
  • 2 Emerging Pathogens Institute, University of Florida, Gainesville, FL 32608, USA.
  • 3 Quantitative Disease Ecology and Conservation Laboratory, Department of Geography, University of Florida, Gainesville, FL 32611, USA.
  • 4 Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA. , (Georgia)
  • 5 Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA. , (Georgia)
  • 6 Biology Department, Stanford University, Stanford, CA 94305, USA.
  • 7 Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. , (Georgia)
  • 8 Center of Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA. , (Georgia)
  • 9 River Basin Center, University of Georgia, Athens, GA 30602, USA. , (Georgia)
  • 10 Agriculture and Life Sciences, Cornell University, Ithaca, NY 14850, USA.
  • 11 Northeast Regional Center of Excellence for Vector-borne Disease Research and the Cornell Institute for Host-Microbe Interactions and Disease, Cornell University, Ithaca, NY 14850, USA.
  • 12 Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA.
Type
Published Article
Journal
Journal of The Royal Society Interface
Publisher
The Royal Society
Publication Date
May 01, 2021
Volume
18
Issue
178
Pages
20210165–20210165
Identifiers
DOI: 10.1098/rsif.2021.0165
PMID: 33947225
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

When a rare pathogen emerges to cause a pandemic, it is critical to understand its dynamics and the impact of mitigation measures. We use experimental data to parametrize a temperature-dependent model of Zika virus (ZIKV) transmission dynamics and analyse the effects of temperature variability and control-related parameters on the basic reproduction number (R0) and the final epidemic size of ZIKV. Sensitivity analyses show that these two metrics are largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our R0 estimate has a single optimum temperature (≈30°C), comparable to other published results (≈29°C). However, the final epidemic size is maximized across a wider temperature range, from 24 to 36°C. The models indicate that ZIKV is highly sensitive to seasonal temperature variation. For example, although the model predicts that ZIKV transmission cannot occur at a constant temperature below 23°C (≈ average annual temperature of Rio de Janeiro, Brazil), the model predicts substantial epidemics for areas with a mean temperature of 20°C if there is seasonal variation of 10°C (≈ average annual temperature of Tampa, Florida). This suggests that the geographical range of ZIKV is wider than indicated from static R0 models, underscoring the importance of climate dynamics and variation in the context of broader climate change on emerging infectious diseases.

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