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

Desai, P. S.

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, as opposed to the state or country level, is needed. 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 latter is normalized by the number of tests. 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 lacks information about the sentiment of people with respect to coronavirus. Thirdly, models that make use of social media posts might have too much noise. News sentiment, on the other hand, 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 AI model, viz., Sentiment Informed Timeseries Analyzing AI (SITALA), that has been trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for the 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. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.