Belau Matthias Hans, Boenecke Juliane, Ströbele Jonathan, Himmel Mirko, Dretvić Daria, Mustafa Ummul-Khair, Kreppel Katharina Sophia, Sauli Elingarami, Brinkel Johanna, Clemen Ulfia Annette, Clemen Thomas, Streit Wolfgang, May Jürgen, Ahmad Amena Almes, Reintjes Ralf, Becher Heiko
University Medical Centre Hamburg-Eppendorf, Institute of Medical Biometry and Epidemiology, Hamburg, Germany.
Department of Infectious Disease Epidemiology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
PLoS Negl Trop Dis. 2025 Mar 28;19(3):e0012946. doi: 10.1371/journal.pntd.0012946. eCollection 2025 Mar.
Dengue fever is one of the world's most important re-emerging but neglected infectious diseases. We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity.
Our risk assessment framework utilizes the combination of various methodological components: We first focused on (I) identifying relevant clinical signals based on a case definition for suspected dengue, (II) refining the signal for potential dengue diagnosis using contextual data, and (III) determining the public health risk associated with a verified dengue signal across various hazard, exposure, and contextual indicators. We then evaluated our framework using (i) historical clinical signals with syndromic and laboratory-confirmed disease information derived from WHO's Epidemic Intelligence from Open Sources (EIOS) technology using decision tree analyses, and (ii) historical dengue outbreak data from Tanzania at the regional level from 2019 (6,795 confirmed cases) using negative binomial regression analyses adjusted for month and region. Finally, we evaluated a test signal across all steps of our integrated framework to demonstrate the implementation of our multi-method approach.
The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). Regression between confirmed dengue fever cases in 2019 as the dependent variable and a site-specific public health risk score as the independent variable showed strong evidence of an increase in dengue fever cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])).
The framework can be used to rapidly determine the public health risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. It further allows for flexibility in its adaptation to target diseases and geographical contexts.
登革热是全球最重要的再度出现但被忽视的传染病之一。我们旨在开发并评估一个综合风险评估框架，以加强在常规监测和诊断能力有限的环境中对潜在登革热疫情的早期检测和风险评估。
我们的风险评估框架利用了多种方法学组成部分的组合：我们首先着重于（I）根据疑似登革热的病例定义确定相关临床信号，（II）使用背景数据细化潜在登革热诊断的信号，以及（III）确定与经核实的登革热信号相关的公共卫生风险，涵盖各种危害、暴露和背景指标。然后，我们使用（i）来自世界卫生组织开源疫情情报（EIOS）技术的具有症状和实验室确诊疾病信息的历史临床信号，通过决策树分析来评估我们的框架，以及（ii）2019年坦桑尼亚区域层面的历史登革热疫情数据（6795例确诊病例），使用针对月份和区域进行调整的负二项回归分析。最后，我们在综合框架的所有步骤中评估了一个测试信号，以展示我们多方法途径的实施情况。
针对临床定义的症状性病例的疑似病例细化算法结果与实验室确诊诊断（登革热是或否）一致。以2019年确诊登革热病例为因变量、特定地点公共卫生风险评分为自变量的回归分析显示，有强有力的证据表明特定地点风险越高，登革热病例增加（率比 = 2.51（95%可信区间 = [1.76, 3.58]））。
该框架可用于快速确定登革热疫情的公共卫生风险，这对于规划干预措施并确定其优先级或进行疫情防范很有用。它还允许灵活适应目标疾病和地理环境。
