背景:伊蚊(Segogomyia)传播的疾病是一个不断扩大的全球威胁,但监测的差距使全面和可比的风险评估具有挑战性。地理统计模型将来自多个位置的数据结合起来,并使用与环境和社会经济因素的联系来制作预测风险图。在这里,我们系统地回顾了过去从本地到全球范围内绘制不同伊蚊传播虫媒病毒风险图谱的方法,识别数据类型的差异和相似性,协变量,和使用的建模方法。
方法:我们在在线数据库中搜索了登革热的预测性风险图谱研究,Zika,基孔肯雅,和黄热病,没有地理或日期限制。我们纳入了需要将其模型参数化或拟合到现实世界的流行病学数据的研究,并对一些衡量人群水平病毒传播风险的新空间位置进行预测(例如发病率,发生,适用性,等。).
结果:我们发现越来越多的虫媒病毒风险图谱研究涉及所有流行地区和虫媒病毒病,2002-2022年共发表了176篇论文,其中主要流行病发生后不久增幅最大。出现了三个主要的用例:(i)确定传播限制的全球地图,估计负担并评估未来全球变化的影响,(ii)用于预测主要流行病在国家之间传播的区域模型,以及(iii)使用本地数据集更好地了解传播动态以改善疫情检测和响应的国家和国家以下模型。温度和降雨是最受欢迎的协变量选择(分别包括在50%和40%的研究中),但越来越多地包括诸如人类流动性等变量。令人惊讶的是,很少有研究(22%,31/144)来自不同领域的协变量的稳健测试组合(例如,气候、社会人口统计学,生态,等。),只有49%的研究通过样本外验证程序评估了预测性能。
结论:在这里,我们表明,针对不同虫媒病毒的风险映射方法随着用例的变化而多样化,流行病学和数据可用性。我们确定了不同虫媒病毒病之间作图方法的关键差异,讨论未来的研究需求,并概述未来虫媒病毒作图的具体建议。
BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically
review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.
METHODS: We searched on-line databases for predictive risk mapping studies for dengue,
Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.).
RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures.
CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.