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PNAS:科学家揭示气候改变引发的全球疟疾分布情况

来源:    2016-08-27


近日,刊登在国际杂志PNAS上的一篇研究论文中,来自利物浦大学的研究人员通过研究表示,由于气候变化的影响在本世纪末疟疾将会在高低地区被越来越多地出现。

 文章中,研究者表示,截止21世纪80年代,在东非将会有1亿人暴露于疟疾感染之中,而由于气候变化所引发的疟疾流行的机制目前并不清楚,Cyril Caminade博士表示,利用我们开发的新型统计学模型就可以对疾病爆发的情形进行有效的预测。

 2010年全世界有67.7万人死于疟疾,其中5岁以下的个体占据了86%,研究人员发现,气候的改变将会在温暖的季节将疟疾转移至高地地区,而且在那些区域疟疾将会永远存在。

 研究人员开发的统计学模型在此之前并未使用过,本文中研究者发现,疟疾扩散到的热带高地地区正是此前使用统计学模型预测到的地区之一。研究者表示,疟疾将会往北移动,最终扩散到欧洲大陆、俄国以及北美。(2014年2月12日,生物谷Bioon.com)

 Impact of climate change on global malaria distribution

 Cyril Caminadea,b,1, Sari Kovatsc, Joacim Rocklovd, Adrian M. Tompkinse, Andrew P. Morseb, Felipe J. Colón-Gonzáleze, Hans Stenlundd, Pim Martensf, and Simon J. Lloydc

 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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