Fisher Andrew, Patel Neelkumar, Patel Preetkumar, Patel Pruthvi, Krishnankutty Vinit, Bhat Vaibhav, Valani Parth, Mago Vijay, Rao Abhijit
Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.
PeerJ Comput Sci. 2022 May 16;8:e980. doi: 10.7717/peerj-cs.980. eCollection 2022.
When modelling epidemics, the outputs and techniques used may be hard for the general public to understand. This can cause fear mongering and confusion on how to interpret the predictions provided by these models. This article proposes a solution for such a model that was created by a Canadian institute for COVID-19 in their region; namely, the NorthCOVID-19 model. In taking these ethical concerns into consideration, first the web interface of this model is analyzed to see how it may be difficult for a user without a strong mathematical background to understand how to use it. Second, a system is developed that takes this model's outputs as an input and produces a video summarization with an auto-generated audio to address the complexity of the interface, while ensuring that the end user is able to understand the important information produced by this model. A survey conducted on this proposed output asked participants, on a scale of 1 to 5, whether they strongly disagreed (1) or strongly agreed (5) with statements regarding the output of the proposed method. The results showed that the audio in the output was helpful in understanding the results (80% responded with 4 or 5) and that it helped improve overallcomprehension of the model (85% responded with 4 or 5). For the analysis of the NorthCOVID-19 interface, a System Usability Scale (SUS) survey was performed where itreceived a scoring of 70.94 which is slightly above the average of 68.
在对流行病进行建模时，所使用的输出和技术可能让普通大众难以理解。这可能会引发恐慌，并让人对如何解读这些模型提供的预测感到困惑。本文针对加拿大一家机构为其所在地区创建的新冠肺炎模型（即NorthCOVID-19模型）提出了一种解决方案。考虑到这些伦理问题，首先分析该模型的网络界面，看看对于没有扎实数学背景的用户来说，理解如何使用它可能会有哪些困难。其次，开发了一个系统，将该模型的输出作为输入，并生成带有自动生成音频的视频总结，以解决界面的复杂性问题，同时确保最终用户能够理解该模型产生的重要信息。针对这个提议的输出进行了一项调查，要求参与者在1到5的量表上表明他们对关于该提议方法输出的陈述是强烈不同意（1）还是强烈同意（5）。结果显示，输出中的音频有助于理解结果（80%的人回答为4或5），并且有助于提高对该模型的整体理解（85%的人回答为4或5）。对于NorthCOVID-19界面的分析，进行了一项系统可用性量表（SUS）调查，该界面的得分为70.94，略高于68的平均分。
