16
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Limiting global-mean temperature increase to 1.5–2 °C could reduce the incidence and spatial spread of dengue fever in Latin America

      research-article

      Read this article at

      Bookmark
          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.

          Significance

          This study is a multigeneral circulation model, multiscenario modeling exercise developed to quantify the dengue-related health benefits of limiting global warming to 1.5–2.0 °C above preindustrial levels in Latin America and the Caribbean. We estimate the impact of future climate change and population growth on the additional number of dengue cases and provide insights about the regions and periods most likely affected by changes in the length of the transmission season. Here, we show that future climate change may amplify dengue transmission and that significant impacts could be avoided by constraining global warming to 1.5 °C above preindustrial levels. Our work could be a starting point for future risk assessments incorporating other important drivers of disease such as urbanization and international traveling.

          Abstract

          The Paris Climate Agreement aims to hold global-mean temperature well below 2 °C and to pursue efforts to limit it to 1.5 °C above preindustrial levels. While it is recognized that there are benefits for human health in limiting global warming to 1.5 °C, the magnitude with which those societal benefits will be accrued remains unquantified. Crucial to public health preparedness and response is the understanding and quantification of such impacts at different levels of warming. Using dengue in Latin America as a study case, a climate-driven dengue generalized additive mixed model was developed to predict global warming impacts using five different global circulation models, all scaled to represent multiple global-mean temperature assumptions. We show that policies to limit global warming to 2 °C could reduce dengue cases by about 2.8 (0.8–7.4) million cases per year by the end of the century compared with a no-policy scenario that warms by 3.7 °C. Limiting warming further to 1.5 °C produces an additional drop in cases of about 0.5 (0.2–1.1) million per year. Furthermore, we found that by limiting global warming we can limit the expansion of the disease toward areas where incidence is currently low. We anticipate our study to be a starting point for more comprehensive studies incorporating socioeconomic scenarios and how they may further impact dengue incidence. Our results demonstrate that although future climate change may amplify dengue transmission in the region, impacts may be avoided by constraining the level of warming.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          An Overview of CMIP5 and the Experiment Design

          The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Missing value estimation methods for DNA microarrays.

            Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Potential effect of population and climate changes on global distribution of dengue fever: an empirical model.

              Existing theoretical models of the potential effects of climate change on vector-borne diseases do not account for social factors such as population increase, or interactions between climate variables. Our aim was to investigate the potential effects of global climate change on human health, and in particular, on the transmission of vector-borne diseases. We modelled the reported global distribution of dengue fever on the basis of vapour pressure, which is a measure of humidity. We assessed changes in the geographical limits of dengue fever transmission, and in the number of people at risk of dengue by incorporating future climate change and human population projections into our model. We showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure. In 1990, almost 30% of the world population, 1.5 billion people, lived in regions where the estimated risk of dengue transmission was greater than 50%. With population and climate change projections for 2085, we estimate that about 5-6 billion people (50-60% of the projected global population) would be at risk of dengue transmission, compared with 3.5 billion people, or 35% of the population, if climate change did not happen. We conclude that climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk.
                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                12 June 2018
                23 May 2018
                23 May 2018
                : 115
                : 24
                : 6243-6248
                Affiliations
                [1] aSchool of Environmental Sciences, University of East Anglia , Norwich NR4 7TJ, United Kingdom;
                [2] bTyndall Centre for Climate Change Research, University of East Anglia , Norwich NR4 7TJ, United Kingdom;
                [3] cLaboratorio de Mastozoologia, Programa de Pós-Graduação em Ciências Ambientais, Universidade do Estado de Mato Grosso , Cavalhada, Cáceres, Mato Grosso, Brazil 78200-000;
                [4] dNorwich Medical School, University of East Anglia , Norwich NR4 7TJ, United Kingdom
                Author notes
                1To whom correspondence should be addressed. Email: f.colon@ 123456uea.ac.uk .

                Edited by B. L. Turner, Arizona State University, Tempe, AZ, and approved April 27, 2018 (received for review October 30, 2017)

                Author contributions: F.J.C.-G., P.R.H., and I.R.L. designed research; F.J.C.-G., I.H., T.J.O., C.S.S.B., C.A.P., and I.R.L. performed research; F.J.C.-G., I.H., and T.J.O. contributed new reagents/analytic tools; F.J.C.-G., I.H., T.J.O., and P.R.H. analyzed data; and F.J.C.-G., I.H., T.J.O., C.S.S.B., C.A.P., P.R.H., and I.R.L. wrote the paper.

                Author information
                http://orcid.org/0000-0002-9671-3405
                http://orcid.org/0000-0002-5608-6144
                Article
                201718945
                10.1073/pnas.1718945115
                6004471
                29844166
                e1c71c94-b54e-4bdd-acc1-717c97404178
                Copyright © 2018 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: DH | National Institute for Health Research (NIHR) 501100000272
                Award ID: HPRU-2012-10141
                Award Recipient : Felipe J. Colón-González Award Recipient : Paul R. Hunter Award Recipient : Iain R. Lake
                Funded by: Global Innovation Initiative grant
                Award ID: GII111
                Award Recipient : Christine Steiner São Bernardo Award Recipient : Carlos A. Peres
                Categories
                Biological Sciences
                Environmental Sciences
                Social Sciences
                Sustainability Sciences

                climate change impacts,disease modeling,latin america

                Comments

                Comment on this article