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      Quantifying the effects of temperature on mosquito and parasite traits that determine the transmission potential of human malaria

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      PLoS Biology
      Public Library of Science

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          Abstract

          Malaria transmission is known to be strongly impacted by temperature. The current understanding of how temperature affects mosquito and parasite life history traits derives from a limited number of empirical studies. These studies, some dating back to the early part of last century, are often poorly controlled, have limited replication, explore a narrow range of temperatures, and use a mixture of parasite and mosquito species. Here, we use a single pairing of the Asian mosquito vector, An. stephensi and the human malaria parasite, P. falciparum to conduct a comprehensive evaluation of the thermal performance curves of a range of mosquito and parasite traits relevant to transmission. We show that biting rate, adult mortality rate, parasite development rate, and vector competence are temperature sensitive. Importantly, we find qualitative and quantitative differences to the assumed temperature-dependent relationships. To explore the overall implications of temperature for transmission, we first use a standard model of relative vectorial capacity. This approach suggests a temperature optimum for transmission of 29°C, with minimum and maximum temperatures of 12°C and 38°C, respectively. However, the robustness of the vectorial capacity approach is challenged by the fact that the empirical data violate several of the model’s simplifying assumptions. Accordingly, we present an alternative model of relative force of infection that better captures the observed biology of the vector–parasite interaction. This model suggests a temperature optimum for transmission of 26°C, with a minimum and maximum of 17°C and 35°C, respectively. The differences between the models lead to potentially divergent predictions for the potential impacts of current and future climate change on malaria transmission. The study provides a framework for more detailed, system-specific studies that are essential to develop an improved understanding on the effects of temperature on malaria transmission.

          Author summary

          Many of the mosquito and parasite life history traits that combine to influence the transmission intensity of malaria (e.g., adult mosquito longevity, biting rate, the developmental period of the parasite within the mosquito, and the proportion of mosquitoes that become infectious) are strongly temperature sensitive. Yet, in spite of decades of research, the precise relationships between individual traits and temperature remain poorly characterized. As a consequence, the majority of studies exploring the influence of local environmental conditions, or prospective impacts of climate change, draw on a combination of studies that utilize different experimental methods and a range of mosquito and parasite species. Here, we use the Indian malaria mosquito, Anopheles stephensi, and the human malaria parasite, Plasmodium falciparum, to thoroughly characterize the influence of temperature on key transmission-related traits. The results reveal a number of novel insights and challenge some longstanding assumptions regarding the nature of mosquito and parasite thermal responses. This study provides an experimental blueprint for further system-specific studies necessary to more fully understand the implications of changing temperatures on malaria transmission.

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          Most cited references68

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          Drivers, dynamics, and control of emerging vector-borne zoonotic diseases.

          Emerging vector-borne diseases are an important issue in global health. Many vector-borne pathogens have appeared in new regions in the past two decades, while many endemic diseases have increased in incidence. Although introductions and emergence of endemic pathogens are often considered to be distinct processes, many endemic pathogens are actually spreading at a local scale coincident with habitat change. We draw attention to key differences between dynamics and disease burden that result from increased pathogen transmission after habitat change and after introduction into new regions. Local emergence is commonly driven by changes in human factors as much as by enhanced enzootic cycles, whereas pathogen invasion results from anthropogenic trade and travel where and when conditions (eg, hosts, vectors, and climate) are suitable for a pathogen. Once a pathogen is established, ecological factors related to vector characteristics can shape the evolutionary selective pressure and result in increased use of people as transmission hosts. We describe challenges inherent in the control of vector-borne zoonotic diseases and some emerging non-traditional strategies that could be effective in the long term. Copyright © 2012 Elsevier Ltd. All rights reserved.
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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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              Impact of climate change on global malaria distribution.

              Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                16 October 2017
                October 2017
                16 October 2017
                : 15
                : 10
                : e2003489
                Affiliations
                [001]The Pennsylvania State University Department of Entomology and Center for Infectious Disease Dynamics, University Park, Pennsylvania, United States of America
                Stanford University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Vanderbilt University Department of Biological Sciences, Nashville, Tennessee

                Author information
                http://orcid.org/0000-0002-1255-6941
                Article
                pbio.2003489
                10.1371/journal.pbio.2003489
                5658182
                29036170
                8bf126bb-5c64-474b-aa27-c8a9a20a4c0f
                © 2017 Shapiro 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
                : 24 March 2017
                : 15 September 2017
                Page count
                Figures: 7, Tables: 2, Pages: 21
                Funding
                National Science Foundation (grant number GRFP DGE1255832). funded LLMS for her graduate studies, 2013-2016. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Institutes of Health (grant number NIH NIAID R01AI110793). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Methods and Resources
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Mosquitoes
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Biology and Life Sciences
                Organisms
                Eukaryota
                Protozoans
                Parasitic Protozoans
                Malarial Parasites
                Medicine and Health Sciences
                Parasitic Diseases
                Medicine and Health Sciences
                Parasitic Diseases
                Malaria
                Medicine and Health Sciences
                Tropical Diseases
                Malaria
                Medicine and Health Sciences
                Infectious Diseases
                Vector-Borne Diseases
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Blood
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Blood
                Biology and Life Sciences
                Physiology
                Body Fluids
                Blood
                Medicine and Health Sciences
                Physiology
                Body Fluids
                Blood
                Biology and Life Sciences
                Parasitology
                Parasite Groups
                Apicomplexa
                Plasmodium
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-10-26
                Data are currently available in the Dryad data repository, under embargo pending acceptance: http://dx.doi.org/10.5061/dryad.74389. Here we include the raw data for survival and infection, along with R script for the statistical analyses, the creation of Figure 2, as well as an Excel workbook that details the numerical values and further details for recapitulating Figures 1-7 along with supplementary Figures 3-5. 

                Life sciences
                Life sciences

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