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The forecast of COVID-19 spread risk at the county level

Authors
  • Hssayeni, Murtadha D.1
  • Chala, Arjuna2
  • Dev, Roger2
  • Xu, Lili2
  • Shaw, Jesse2
  • Furht, Borko1
  • Ghoraani, Behnaz1
  • 1 Florida Atlantic University, Boca Raton, FL, 33431, USA , Boca Raton (United States)
  • 2 LexisNexis Risk Solution, Alpharetta, GA, USA , Alpharetta (United States)
Type
Published Article
Journal
Journal of Big Data
Publisher
Springer Nature
Publication Date
Jul 07, 2021
Volume
8
Issue
1
Identifiers
DOI: 10.1186/s40537-021-00491-1
Source
Springer Nature
Keywords
Disciplines
  • Research
License
Green

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 <1000 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 can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk.

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