On Improving Toll Accuracy for COVID-like Epidemics in Underserved Communities Using User-generated Data

H. Aboubakr, A. Magdy
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引用次数: 5

Abstract

This paper envisions using user-generated data as a cheap way to improve accuracy of epidemic tolls in underserved communities. The global widespread of COVID-19 pandemic has imposed several unprecedented challenges. One of these challenges is constantly monitoring the unprecedented epidemic widespread at a fine-granular spatial scale, so experts can model, understand, and prevent disease transmission and field personnel can reach and treat infected people. Unfortunately, the limited resources compared to the pandemic widespread has led to a significant number of unreported cases in underserved communities and developing countries, including a large number of severe cases. We propose in this paper enhancing epidemic case reporting in underserved communities through exploiting the power of data that are posted by people on web. Our vision is building a data analysis pipeline that filters and categories use-generated data objects to provide informal estimates for tolls in unreachable regions and enhance estimates in other regions. The pipeline consist of five stages, that starts with filtering epidemic-specific data to visualize advanced aggregates to end users. We also discuss several technical challenges that face different stages of the pipeline.
关于利用用户生成数据提高服务不足社区covid - 19流行病收费准确性的研究
本文设想使用用户生成的数据作为一种廉价的方法来提高服务不足社区流行病收费的准确性。2019冠状病毒病全球大流行带来了一些前所未有的挑战。其中一项挑战是不断监测在细颗粒空间尺度上广泛传播的前所未有的流行病,因此专家可以建模,了解和预防疾病传播,现场人员可以接触和治疗感染者。不幸的是,与大流行病相比,资源有限,导致服务不足的社区和发展中国家出现大量未报告病例,包括大量严重病例。我们在本文中建议通过利用人们在网络上发布的数据的力量来加强服务不足社区的流行病病例报告。我们的愿景是建立一个数据分析管道,过滤和分类用户生成的数据对象,为无法到达的地区提供非正式的通行费估计,并增强其他地区的估计。该管道由五个阶段组成,首先是过滤特定于流行病的数据,以便向最终用户可视化高级聚合。我们还讨论了管道不同阶段面临的几个技术挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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