Muñoz Ángel G, Thomson Madeleine C, Stewart-Ibarra Anna M, Vecchi Gabriel A, Chourio Xandre, Nájera Patricia, Moran Zelda, Yang Xiaosong
Atmospheric and Oceanic Sciences, Princeton UniversityPrinceton, NJ, United States.
Geophysical Fluid Dynamics Laboratory, Princeton UniversityPrinceton, NJ, United States.
Front Microbiol. 2017 Jul 12;8:1291. doi: 10.3389/fmicb.2017.01291. eCollection 2017.
Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic -borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of and . We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower-but still of potential use to decision-makers-for the rest of the year. In particular, we find that above-normal for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.
根据当时所掌握的知识,近期2015 - 2016年的寨卡病毒(ZIKV)疫情可能无法被预测。由于事先并不知晓寨卡病毒已在南美洲存在,且对该大陆关键的流行病学过程缺乏了解,同时也没有寨卡病毒病例的长期记录,因此对于一种由媒介传播疾病的潜在风险,所能做出的最佳相关预测是针对一种一般性媒介传播疾病疫情的潜在风险。在此,我们使用最近发表的一个双媒介基本再生数模型,来评估有利于寨卡、基孔肯雅或登革热等疾病流行的条件的可预测性,这些疾病是由伊蚊和白纹伊蚊单独或同时存在传播的。我们将迫使模型运行的潜在传播风险与观测到的气候以及来自北美多模式集合(NMME)的最先进业务预报进行比较,发现这个新的季节性预报系统在12月至2月和3月至5月期间,对拉丁美洲和加勒比地区的多个国家预测技能最高,而在一年中的其他时间略低,但对决策者仍有潜在用途。特别是,我们发现对于2015年初寨卡疫情的发生,高于正常水平的情况至少可以提前1个月在几个寨卡热点地区成功预测出来,尤其是对于巴西东北部:疫情的核心地区。尽管如此,疫情的爆发和传播取决于气候条件之外的多种因素的影响,因此这种方法必须被视为一种指导,而不是媒介传播疾病疫情的正式预测工具。