Desai Prathamesh S
Rice University, 6100 Main St, MS-321 Houston, TX 77005, USA.
Expert Syst Appl. 2021 Oct 15;180:115104. doi: 10.1016/j.eswa.2021.115104. Epub 2021 Apr 29.
Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level is needed as opposed to the state or country level. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the number of tests normalizes the latter. Positivity gives a better picture of the spread of this pandemic as, with time, more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD (Susceptible, Exposed, Infectious, Recovered, and Deceased) lacks information about the sentiment of people concerning coronavirus. Thirdly, models that make use of social media posts might have too much noise and misinformation. On the other hand, news sentiment can capture long-term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new artificial intelligence (i.e., AI) model, viz., Sentiment Informed Time-series Analyzing AI (SITALA), trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76, and that for the test dataset of 22 consecutive days is 9.6. A cone of uncertainty is provided within which future COVID-19 test positivity has been shown to fall with high accuracy. The model predictions fare better than a published Bayesian-based SEIRD model. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment (e.g., death count for COVID-19 will grow at an alarming rate in Houston if mask orders are not followed) will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.
冠状病毒病（COVID-19）已演变成一场存在诸多未知因素的大流行病。位于得克萨斯州哈里斯县的休斯顿正成为这场大流行病的下一个热点地区。随着国际和州际旅行的大幅减少，需要一个县级而非州级或国家级的模型。现有方法存在一些缺陷。首先，所使用的数据是COVID-19阳性病例数而非阳性率。前者是所进行检测数量的函数，而检测数量使后者标准化。阳性率能更好地反映这场大流行病的传播情况，因为随着时间推移，检测数量在增加。企业重新开放至近100%产能时，期望阳性率低于5%。其次，像SEIRD（易感、暴露感染、康复和死亡）等模型所使用的数据缺乏人们对冠状病毒情绪方面的信息。第三，利用社交媒体帖子的模型可能存在过多噪音和错误信息。另一方面，新闻情绪能够捕捉隐藏变量的长期影响，如公共政策、当地医生的意见以及对全州指令的违抗。本研究引入了一种新的人工智能（即AI）模型，即基于情绪的时间序列分析人工智能（SITALA），该模型基于哈里斯县超过2750篇新闻文章的COVID-19检测阳性率数据和新闻情绪进行训练。新闻情绪是使用IBM Watson Discovery News获得的。SITALA受到谷歌WaveNet架构的启发，并使用了TensorFlow。连续66天训练数据集的平均绝对误差为2.76，连续22天测试数据集的平均绝对误差为9.6。提供了一个不确定性区间，未来COVID-19检测阳性率已被证明能高精度地落在该区间内。该模型的预测比已发表的基于贝叶斯的SEIRD模型表现更好。该模型预测，为了遏制休斯顿冠状病毒的传播，持续的负面新闻情绪（例如，如果不遵守口罩指令，休斯顿COVID-19的死亡人数将以惊人的速度增长）将是可取的。公共政策制定者可使用SITALA来设定地方政策和指令的基调。
