Disease Control and Vector Biology Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
Malar J. 2010 Mar 5;9:70. doi: 10.1186/1475-2875-9-70.
The world is facing an increased threat from new and emerging diseases, and there is concern that climate change will expand areas suitable for transmission of vector borne diseases. The likelihood of vivax malaria returning to the UK was explored using two markedly different modelling approaches. First, a simple temperature-dependent, process-based model of malaria growth transmitted by Anopheles atroparvus, the historical vector of malaria in the UK. Second, a statistical model using logistic-regression was used to predict historical malaria incidence between 1917 and 1918 in the UK, based on environmental and demographic data. Using findings from these models and saltmarsh distributions, future risk maps for malaria in the UK were produced based on UKCIP02 climate change scenarios.
The process-based model of climate suitability showed good correspondence with historical records of malaria cases. An analysis of the statistical models showed that mean temperature of the warmest month of the year was the major factor explaining the distribution of malaria, further supporting the use of the temperature-driven processed-based model. The risk maps indicate that large areas of central and southern England could support malaria transmission today and could increase in extent in the future. Confidence in these predictions is increased by the concordance between the processed-based and statistical models.
Although the future climate in the UK is favourable for the transmission of vivax malaria, the future risk of locally transmitted malaria is considered low because of low vector biting rates and the low probability of vectors feeding on a malaria-infected person.
世界正面临着新出现的疾病日益增多的威胁，人们担心气候变化将扩大传播病媒传播疾病的适宜地区。使用两种截然不同的建模方法探讨了间日疟原虫返回英国的可能性。首先，使用一种简单的温度依赖型、基于过程的模型来模拟疟原虫的生长，这种疟原虫由英国历史上的疟疾病媒按蚊属传播。其次，使用逻辑回归的统计模型来预测英国 1917 年至 1918 年的历史疟疾发病率，该模型基于环境和人口数据。利用这些模型和盐沼分布的结果，根据英国气候变化情景 02 （UKCIP02）制作了英国疟疾未来风险图。
气候适宜性的基于过程的模型与疟疾病例的历史记录有很好的一致性。对统计模型的分析表明，一年中最暖月份的平均温度是解释疟疾分布的主要因素，进一步支持使用基于温度的过程模型。风险图表明，英国中部和南部的大片地区今天可能支持疟疾传播，并可能在未来扩大范围。基于过程和统计模型的一致性增加了对这些预测的信心。
尽管英国未来的气候有利于间日疟原虫的传播，但由于媒介叮咬率低和媒介叮咬疟疾病人的可能性低，因此，本地传播疟疾的风险被认为较低。
