Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States

Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States

<italic>Goal:</italic> The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US&#x2019; diversity, geographic spread, and economic inequality, the COVID...

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Bibliographic Details
Main Authors: MichaelC. Lucic, Hakim Ghazzai, Carlo Lipizzi, Yehia Massoud
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
ARIMA
COVID-19
data analytics
<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-means clustering
time series analysis
Online Access:https://ieeexplore.ieee.org/document/9479783/
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Summary:<italic>Goal:</italic> The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US&#x2019; diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. <italic>Method:</italic> In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-means, is employed to group US counties based on demographic and economic similarities. Then, time series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. <italic>Results:</italic> To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. <italic>Conclusion:</italic> Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.
ISSN:2644-1276

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