Institute of Infection and Global Health, Department of Epidemiology and Population Health and School of Environmental Sciences, Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, United Kingdom.
Proc Natl Acad Sci U S A. 2014 Mar 4;111(9):3286-91. doi: 10.1073/pnas.1302089111. Epub 2014 Feb 3.
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.
疟疾是一种重要的疾病,具有全球分布和重大的健康负担。其分布和季节性活动的空间范围对气候因素以及当地控制疾病的能力非常敏感。疟疾也是为数不多的通过多个研究小组建模的健康结果之一,因此可以促进在气候变化未来下对健康影响的首次模型比较。我们使用耦合模型比较计划第 5 阶段气候模型的校正偏差温度和降雨模拟,以比较五个统计和动态疟疾影响模型的指标,用于三个未来时期(2030 年代、2050 年代和 2080 年代)。我们在全球和区域水平上评估了三个疟疾结果指标:气候适宜性、风险人口增加以及模型输出中风险人口的月数增加。疟疾预测基于五个不同的全球气候模型,每个模型在四个排放情景(代表性浓度途径,RCPs)和一个单一人口预测下运行。我们还调查了与未来因气候变化而面临疟疾风险的人口的建模不确定性。我们的研究结果表明,全球气候适宜性总体上呈净增长,面临疟疾风险的人口也呈净增长,但存在很大的不确定性。模型输出表明,与 2050 年代相比,2080 年代从 RCP2.6 到 RCP8.5 的年风险月数净增加。疟疾结果指标对疟疾影响模型的选择高度敏感,尤其是在疟疾分布的流行边缘。