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      Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.

          Author summary

          Understanding the drivers of recent Zika, dengue, and chikungunya epidemics is a major public health priority. Temperature may play an important role because it affects virus transmission by mosquitoes, through its effects on mosquito development, survival, reproduction, and biting rates as well as the rate at which mosquitoes acquire and transmit viruses. Here, we measure the impact of temperature on transmission by two of the most common mosquito vector species for these viruses, Aedes aegypti and Ae. albopictus. We integrate data from several laboratory experiments into a mathematical model of temperature-dependent transmission, and find that transmission peaks at 26–29°C and can occur between 18–34°C. Statistically comparing model predictions with recent observed human cases of dengue, chikungunya, and Zika across the Americas suggests an important role for temperature, and supports model predictions. Using the model, we predict that most of the tropics and subtropics are suitable for transmission in many or all months of the year, but that temperate areas like most of the United States are only suitable for transmission for a few months during the summer (even if the mosquito vector is present).

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          Most cited references43

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          Systematic variation in the temperature dependence of physiological and ecological traits.

          To understand the effects of temperature on biological systems, we compile, organize, and analyze a database of 1,072 thermal responses for microbes, plants, and animals. The unprecedented diversity of traits (n = 112), species (n = 309), body sizes (15 orders of magnitude), and habitats (all major biomes) in our database allows us to quantify novel features of the temperature response of biological traits. In particular, analysis of the rising component of within-species (intraspecific) responses reveals that 87% are fit well by the Boltzmann-Arrhenius model. The mean activation energy for these rises is 0.66 ± 0.05 eV, similar to the reported across-species (interspecific) value of 0.65 eV. However, systematic variation in the distribution of rise activation energies is evident, including previously unrecognized right skewness around a median of 0.55 eV. This skewness exists across levels of organization, taxa, trophic groups, and habitats, and it is partially explained by prey having increased trait performance at lower temperatures relative to predators, suggesting a thermal version of the life-dinner principle-stronger selection on running for your life than running for your dinner. For unimodal responses, habitat (marine, freshwater, and terrestrial) largely explains the mean temperature at which trait values are optimal but not variation around the mean. The distribution of activation energies for trait falls has a mean of 1.15 ± 0.39 eV (significantly higher than rises) and is also right-skewed. Our results highlight generalities and deviations in the thermal response of biological traits and help to provide a basis to predict better how biological systems, from cells to communities, respond to temperature change.
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            Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti.

            Most studies on the ability of insect populations to transmit pathogens consider only constant temperatures and do not account for realistic daily temperature fluctuations that can impact vector-pathogen interactions. Here, we show that diurnal temperature range (DTR) affects two important parameters underlying dengue virus (DENV) transmission by Aedes aegypti. In two independent experiments using different DENV serotypes, mosquitoes were less susceptible to virus infection and died faster under larger DTR around the same mean temperature. Large DTR (20 °C) decreased the probability of midgut infection, but not duration of the virus extrinsic incubation period (EIP), compared with moderate DTR (10 °C) or constant temperature. A thermodynamic model predicted that at mean temperatures 18 °C, larger DTR reduces DENV transmission. The negative impact of DTR on Ae. aegypti survival indicates that large temperature fluctuations will reduce the probability of vector survival through EIP and expectation of infectious life. Seasonal variation in the amplitude of daily temperature fluctuations helps to explain seasonal forcing of DENV transmission at locations where average temperature does not vary seasonally and mosquito abundance is not associated with dengue incidence. Mosquitoes lived longer and were more likely to become infected under moderate temperature fluctuations, which is typical of the high DENV transmission season than under large temperature fluctuations, which is typical of the low DENV transmission season. Our findings reveal the importance of considering short-term temperature variations when studying DENV transmission dynamics.
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              Optimal temperature for malaria transmission is dramatically lower than previously predicted.

              The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission. © 2012 Blackwell Publishing Ltd/CNRS.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                27 April 2017
                April 2017
                : 11
                : 4
                : e0005568
                Affiliations
                [1 ]Biology Department, Stanford University, 371 Serra Mall, Stanford, CA, United States of America
                [2 ]Department of Integrative Biology, University of South Florida, 4202 East Fowler Ave, SCA110 Tampa, FL, United States of America
                [3 ]Odum School of Ecology, University of Georgia, Athens, GA, United States of America
                [4 ]Department of Statistics, Virginia Polytechnic and State University, 250 Drillfield Drive Blacksburg, VA, United States of America
                [5 ]Department of Geography, University of Florida, Turlington Hall, Gainesville, FL, United States of America
                [6 ]Center for Tropical and Emerging Global Disease, Department of Infectious Diseases, University of Georgia College of Veterinary Medicine, 501 D.W. Brooks Drive, Athens, GA, United States of America
                [7 ]Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States of America
                [8 ]Center for Global Health and Translational Science, Department of Microbiology and Immunology, Weiskotten Hall, SUNY Upstate Medical University, Syracuse, NY, United States of America
                [9 ]School of Life Sciences, College of Agriculture, Engineering, and Science, University of KwaZulu Natal, Private Bag X01, Scottsville, KwaZulu Natal, South Africa
                [10 ]Department of Ecology and Evolutionary Biology, University of California Los Angeles and Department of Biomathematics, University of California Los Angeles, Los Angeles, CA, United States of America
                [11 ]Santa Fe Institute, Santa Fe, NM, United States of America
                [12 ]Department of Biology, Indiana University, Jordan Hall 142, Bloomington, IN, United States of America
                [13 ]Center for Global Health and Translational Sciences, SUNY Upstate Medical University, Syracuse, NY, United States of America
                [14 ]Department of Entomology and Center for Infectious Disease Dynamics, Penn State University, 112 Merkle Lab, University Park, PA, United States of America
                [15 ]Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, United States of America
                Institute for Disease Modeling, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: EAM MBT VS SJR LRJ ASI JRR.

                • Data curation: JMC MVE PG KM CCM MS DPW EAM CAL.

                • Formal analysis: EAM MVE PG KM MSS DPW LRJ CAL.

                • Funding acquisition: EAM JRR SJR MBT ASI LRJ VS CCM.

                • Investigation: JMC MVE PG KM CCM MS DPW CAL EAM.

                • Methodology: EAM LRJ SJR MSS.

                • Project administration: EAM.

                • Software: LRJ EAM MSS MVE DPW.

                • Supervision: EAM MBT JRR VS LRJ SJR ASI CCM.

                • Validation: LRJ EAM.

                • Visualization: JMC SJR LRJ EAM.

                • Writing – original draft: EAM.

                • Writing – review & editing: EAM JMC MVE PG LRJ CAL KM CCM JRR SJR VS MSS ASI MBT DPW.

                Author information
                http://orcid.org/0000-0002-4402-5547
                Article
                PNTD-D-17-00075
                10.1371/journal.pntd.0005568
                5423694
                28448507
                bb0e43d0-80a5-4f4c-b230-ef9e5d83b405
                © 2017 Mordecai et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 January 2017
                : 12 April 2017
                Page count
                Figures: 4, Tables: 0, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: National Science Foundation (US)
                Award ID: EF-1241889
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM109499
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01TW010286-01
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000199, U.S. Department of Agriculture;
                Award ID: 2009-35102-0543
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000139, U.S. Environmental Protection Agency;
                Award ID: CAREER 83518801
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1640780
                Award Recipient :
                Funded by: Stanford University Woods Institute for the Environment
                Award Recipient :
                Funded by: Stanford University Center for Innovation in Global Health
                Award Recipient :
                Funded by: National Science Foundation (US)
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1518681
                Award Recipient :
                Funded by: National Science Foundation (US)
                Award ID: DEB-1640780
                Award Recipient :
                EAM, MBT, VS, SJR, LRJ, ASI, JRR, MS, JC, and DPW were supported by the National Science Foundation (DEB-1518681; https://nsf.gov/). JRR was supported by the NSF (EF-1241889; https://nsf.gov/), National Institutes of Health (R01GM109499 and R01TW010286-01; https://www.nih.gov/), US Department of Agriculture (2009-35102-0543; https://www.usda.gov/wps/portal/usda/usdahome) and US Environmental Protection Agency (CAREER 83518801; https://www.epa.gov/). EAM and CCM were supported by the NSF (DEB-1640780; https://nsf.gov/). EAM was supported by the Stanford Woods Institute for the Environment ( https://woods.stanford.edu/research/environmental-venture-projects) and the Stanford Center for Innovation in Global Health ( http://globalhealth.stanford.edu/research/seed-grants.html). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
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                2017-05-09
                All data, code, and outputs are available on Figshare: https://figshare.com/s/b79bc7537201e7b5603f, DOI: https://dx.doi.org/10.6084/m9.figshare.4563928

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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