Quantitative Analysis of Group for Epidemiology Architectural Approach

Q1 Decision Sciences

Annals of Data Science Pub Date : 2023-08-18 DOI:10.1007/s40745-023-00493-1

Dephney Mathebula

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引用次数: 0

Abstract

Epidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 and monkeypox. The requirement of accessing highly accurate data useful for disease modelling is beneficial but not without having challenges. Currently, the task of data acquisition is executed by select individuals in different research groups. This approach experiences the drawbacks associated with getting permission to access the desired data and inflexibility to change data acquisition goals due to dynamic epidemiological research objectives. The presented research addresses these challenges and proposes the design and use of dynamic intelligent crawlers for acquiring epidemiological data related to a given goal. In addition, the research aims to quantify how the use of computing entities enhances the process of data acquisition in epidemiological related studies. This is done by formulating and investigating the metrics of the data acquisition efficiency and the data analytics efficiency. The use of human assisted crawlers in the global information networks is found to enhance data acquisition efficiency (DAqE) and data analytics efficiency (DAnE). The use of human assisted crawlers in a hybrid configuration outperforms the case where manual research group member efforts are expended enhancing the DAqE and DAnE by up to 35% and 99% on average, respectively.

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流行病学体系结构方法的群体定量分析

流行病学是以疾病建模为重点的研究领域，需要大量数据。不同研究小组的流行病学研究人员在为 COVID-19 和猴痘开发不同的数据驱动模型方面发挥了关键作用。获取对疾病建模有用的高精度数据的要求是有益的，但也并非没有挑战。目前，获取数据的任务由不同研究小组的选定人员执行。这种方法的缺点是需要获得访问所需数据的许可，而且由于流行病学研究目标的不断变化，数据采集目标的改变也不灵活。本研究针对这些挑战，提出了设计和使用动态智能爬虫来获取与给定目标相关的流行病学数据的建议。此外，研究还旨在量化计算实体的使用如何增强流行病学相关研究的数据采集过程。具体做法是制定和研究数据获取效率和数据分析效率的衡量标准。研究发现，在全球信息网络中使用人工辅助爬虫可提高数据获取效率（DAqE）和数据分析效率（DAnE）。在混合配置中使用人工辅助爬虫的效果优于人工研究小组成员工作的情况，平均可分别提高 35% 和 99% 的 DAqE 和 DAnE。

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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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.