科研解读 | 次季节(S2S)预报对登革热爆发有极大的指示意义

(https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020GH000253)

Researchers Show Potential for Subseasonal Forecasts to Predict Dengue Outbreaks

中文翻译由DeepL提供技术支持

一项新的研究首次证明，次季节降雨量和温度预报可以通过估计蚊子的数量来预测登革热的爆发。

气候模型经常被应用于未来的预测，但改进和测试这些模型能力的最可靠的方法之一是研究过去。在发表在《GeoHealth》上的一项新研究中，研究人员重新审视了留尼旺岛的一次大规模登革热爆发，并确定使用现有的气候数据可以预测该事件。特别是，他们将爆发前4周产生的降雨量和温度预测整合到蚊子数量模型中。他们的成功对今后的公共卫生工作中使用气候数据具有借鉴意义。

留尼旺是印度洋马达加斯加和毛里求斯之间的一个法国海外岛屿，2018年面临着前所未有的登革热爆发。登革热是一种由蚊子传播的热带病毒性疾病，在留尼旺，该病由当地物种白纹伊蚊携带。蚊子的数量受到复杂的环境因素的影响，很难在当地范围内进行监测。然而，哥伦比亚大学国际气候与社会研究所、乌梅大学、纽约大学和欧洲疾病控制和预防中心的研究人员的研究表明，利用气候信息来预测环境对病媒传播疾病的适宜程度有很大潜力。

蚊种白纹伊蚊原产于东南亚，但在整个热带和亚热带地区都能找到。照片：James Gathany（美国疾病控制和预防中心）。James Gathany (疾病控制和预防中心)

蚊子的种类，如A. albopictus，只能在一个独特的温度范围内生长。降雨事件对它们的生存也有独特的影响。这些因素和其他因素可以在气候数据中被识别出来，研究人员认识到，这些环境因素的理想汇合可能会带来蚊子数量的增加，从而导致蚊子的爆发。

"与热带气旋相关的降雨事件和高于平均水平的气温在2018年登革热爆发中发挥了作用，"国际气候与社会研究所的高级研究人员、该研究的主要作者Laurel DiSera说。"由于我们可以提前4周预测这样的情况，我们认为有可能提前几周预测到爆发本身。"

为了测试这个想法，DiSera和她的同事将欧洲中期天气预报中心（ECMWF）模型的亚季气候预测（可通过亚季到季预测项目数据库获得）纳入一个生成蚊子数量预测的矢量模型中；他们发现，该方法对2018年的爆发事件具有合理的预测性。

"我们的结果强烈地表明，我们可以使用次季节数据来更好地了解蚊子种群的适宜性以及由此产生的爆发事件的可能性，"DiSera说。

留尼旺的降雨预测地图 上图(a)显示的是2018年1月8日留尼旺及其附近岛屿的降雨量异常情况。它下面的地图是2018年1月8日一（b）、二（c）、三（d）和四（e）周前做出的降雨异常预报。

"预防和管理登革热爆发的常规方法是通过控制蚊子，"瑞典乌梅奥大学的共同作者Joacim Rocklöv说。"有更多的时间来行动，在操作上丝毫没有差别。"

DiSera补充说，在许多公共卫生系统已经处于紧张状态的时候，像所提出的新方法可能会帮助公共卫生机构节省资源和时间，并使他们能够更灵活地应对COVID-19这样的紧急和意外威胁。

这项研究得到了美国国家海洋和大气管理局（赠款NA18OAR4310339）、ARBOPREVENT项目（瑞典研究委员会Formas赠款2018-01754）和哥伦比亚世界项目 "今日农业适应气候，为明天服务"（ACToday）的支持。

英文原文

A new study demonstrates for the first time that subseasonal rainfall and temperature forecasts can be used to predict outbreaks of dengue fever by estimating mosquito abundance.

Climate models are often applied to future predictions, but one of the most reliable ways to improve and test the capabilities of these models is to look to the past. In a new study published in GeoHealth, researchers revisited a large dengue outbreak on the island of Réunion and determined it would have been possible to predict the event using available climate data. In particular, they integrated forecasts of rainfall and temperature generated up to four weeks ahead of the start of the outbreak into a mosquito population model. Their success has implications for the use of climate data in future public health efforts.

Réunion, a French overseas island between Madagascar and Mauritius in the Indian Ocean, faced an unprecedented outbreak of dengue in 2018. Dengue fever is a viral tropical disease spread by mosquitoes; in Réunion, the disease is carried by the local species Aedes albopictus. Mosquito populations are influenced by a complicated web of environmental factors that are difficult to monitor on a local scale. However, the study by researchers at Columbia University’s International Research Institute for Climate and Society, Umeä University, New York University and the European Center for Disease Control and Prevention shows that there is great potential for using climate information to predict how suitable an environment will be for vector-borne diseases.

Mosquito species such as A. albopictus thrive only within a unique temperature range. Rainfall events also have distinctive effects on their survival. These factors and others can be identified among climate data, and the researchers recognized that an ideal confluence of these environmental factors could bring on an increase in the abundance of mosquitoes, which could lead to an outbreak.

“Tropical-cyclone-related rainfall events and higher-than-average temperatures played a role in the 2018 dengue outbreak,” said Laurel DiSera, a senior research staff associate at the International Research Institute for Climate and Society and the study’s lead author. “Since we can forecast such conditions up to four weeks in advance, we thought it would be possible that the outbreak itself could be predicted weeks ahead.”

To test the idea, DiSera and her colleagues incorporated subseasonal climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model, available via the Subseasonal-to-seasonal Prediction Project Database, into a vector model that generated predictions of mosquito populations; they found that the methodology was reasonably predictive of the outbreak event in 2018.

“Our results strongly suggest that we can use subseasonal data to better understand suitability for mosquito populations and the potential for resulting outbreak events,” DiSera said.

“The routine way to prevent and manage dengue outbreaks is through mosquito control,” said coauthor Joacim Rocklöv for Sweden’s Umeå University. “Having more time to act makes a difference, not in the least operationally.”

DiSera added that in a time when many public-health systems are already under strain, new methodologies like the one proposed may help public health agencies save resources and time and allow them to be more flexible in dealing with urgent and unexpected threats such as COVID-19.

This research was supported by the National Oceanic and Atmospheric Administration (grant NA18OAR4310339), the ARBOPREVENT project (Swedish Research Council Formas grant 2018-01754), and Adapting Agriculture to Climate Today, for Tomorrow (ACToday), a Columbia World Project.