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Murtadha D Hssayeni is a PhD Candidate studying Computer Science at Florida Atlantic University, Florida, USA.

Murtadha was born in Babil, Iraq. He received the B.S. degree in Computer Engineering from the University of Technology-Baghdad, Iraq, in 2012 and the M.S. degree in the same field from Rochester Institute of Technology, Rochester, NY, in 2017. His research interests include biomedical signal and image processing, and machine learning.

He worked as part of the ”Modeling Corona Spread Using Big Data Analytics” project funded by the NSF RAPID grant in a collaboration between FAU and LexisNexis Risk Solutions Group during the Fall of 2020.

Poster Abstract

The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage.

Lessons learned from this study not only can help with managing the COVID-19 pandemic but also help with early and effective management of possible future pandemics. The project used the HPCC Systems platform for collecting, hosting, and analyzing the data. For more details, please visit https://covid19.hpccsystems.com.

Presentation

In this Video Recording, Murtadha provides a tour and explanation of his poster content.

The Forecast of COVID-19 Spread Risk at The County Level

Click on the poster for a larger image.