Ajit N Babu, Center for Advancement of Global Health, Cochin, India.
Engelbert Niehaus, University of Koblenz-Landau, Landau, Germany.
Environ Monit Assess. 2019 Jun 28;191(Suppl 2):393. doi: 10.1007/s10661-019-7425-0.
India has the largest number of dengue cases in the world, contributing approximately 34% of the global burden. The framework for a geospatially enabled early warning and adaptive response system (EWARS) was first proposed in 2008. It was meant to be a decision support system for enhancing traditional surveillance methods for preventing mosquito-borne diseases in India by utilizing remote sensing data and fuzzy logic-based mathematical predictive modeling. This conceptual paper presents a significant evolution of EWARS such that it synthesizes inputs from not only traditional surveillance and reporting systems for dengue but also from the public via participatory disease surveillance. Two smartphone-based applications have been developed to support EWARS. The first-MOSapp-allows field health workers to upload surveillance data and collect key data on environmental parameters by both direct observation and via portable microclimate stations. The second-DISapp-collects relevant information directly from the community to support participatory disease surveillance. It also gives the user a real-time estimate of the risk of exposure to dengue in proximity to their home and has an educational component that provides information on relevant preventive measures. Both applications utilize a new mosquito abundance measure-the mosquito perception index (MPI)-as reported by the user. These data streams will feed into the EWARS model to generate dynamic risk maps that can guide resource optimization and strengthen disease surveillance, prevention, and response. It is anticipated that such an approach can assist in addressing gaps in the current system of dengue surveillance and control in India.
印度是全球登革热病例最多的国家，约占全球负担的 34%。地理空间增强型早期预警和自适应响应系统 (EWARS) 的框架最初是在 2008 年提出的。该系统旨在通过利用遥感数据和基于模糊逻辑的数学预测建模，为增强印度预防蚊媒疾病的传统监测方法提供决策支持系统。本文提出了 EWARS 的重大演变，使其不仅综合了登革热传统监测和报告系统的输入，还综合了公众通过参与性疾病监测提供的输入。已经开发了两个基于智能手机的应用程序来支持 EWARS。第一个-MOSapp-允许现场卫生工作者上传监测数据，并通过直接观察和便携式微气候站收集有关环境参数的关键数据。第二个-DISapp-直接从社区收集相关信息，以支持参与性疾病监测。它还为用户提供了一个实时估计，了解其家庭附近接触登革热的风险，并具有教育组件，提供有关相关预防措施的信息。这两个应用程序都利用了用户报告的新的蚊子丰度衡量标准-蚊子感知指数 (MPI)。这些数据流将输入到 EWARS 模型中，以生成动态风险地图，从而指导资源优化并加强疾病监测、预防和应对。预计这种方法可以帮助解决印度当前登革热监测和控制系统中的差距。
