News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston

Simple item page
dc.citation.articleNumber115104en_US
dc.citation.journalTitleExpert Systems with Applicationsen_US
dc.citation.volumeNumber180en_US
dc.contributor.authorDesai, Prathamesh S.en_US
dc.date.accessioned2021-05-28T20:58:17Zen_US
dc.date.available2021-05-28T20:58:17Zen_US
dc.date.issued2021en_US
dc.description.abstractCoronavirus 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.en_US
dc.identifier.citationDesai, Prathamesh S.. "News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston." <i>Expert Systems with Applications,</i> 180, (2021) Elsevier: https://doi.org/10.1016/j.eswa.2021.115104.en_US
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2021.115104en_US
dc.identifier.urihttps://hdl.handle.net/1911/110651en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsThis manuscript version is made available under the CC-BY-NC-ND 4.0.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subject.keywordCOVID-19 modelen_US
dc.subject.keywordNews sentimenten_US
dc.subject.keywordPublic policyen_US
dc.subject.keywordDeep learningen_US
dc.subject.keywordArtificial intelligenceen_US
dc.subject.keywordPandemic forecasten_US
dc.titleNews Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houstonen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
SITALA_Desai_P_ESWA_Rice.pdf
Size:
983.77 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Rice Coronavirus Research