Identifying regional COVID-19 presence early with time series analysis. (28th August 2020)

• Record Type: Journal Article
• Authors: Kruse, Ryan; Alkhushayni, Suboh
• Is Part Of: IOP SciNotes. Volume 1:Number 2(2020)
• Publication Date: 2020-08-28
• Subjects: COVID-19 -- ILINet -- time series analysis -- prediction intervals -- residual hypothesis test -- changepoint analysis; 500
• DOI: 10.1088/2633-1357/aba739
• Languages: English
• ISSNs: 2633-1357

Abstract

Abstract: The first confirmed case of COVID-19 in the United States was January 20, 2020 in Washington, while the first globally confirmed cases were in China in December 2019. The CDC's Influenza-like Illness Surveillance Network is used to track the amount of people who seek medical attention for influenza-like illnesses, along with the illness cause. The metric rILI- is used to assess the amount of people who test negative for influenza or any other specific cause. To assess the evidence of COVID-19 presence in the US in late December 2019 or early January 2020, rILI- data from 2010 to mid-March 2020 was used to perform three types of analysis. First, we forecast prediction intervals using data until mid-November 2019 and compared the predictions with observed values for the subsequent 16 weeks. Second, we performed residual hypothesis testing by removing the trend and seasonality in order to compare residuals from before and after November 17, 2019. Third, we used changepoint analysis to identify major changes in trend and seasonality. This study provides strong evidence of COVID-19 presence in the US in late December 2019 or early January 2020. Combined with the knowledge that COVID-19 was spreading across other parts of the world, anomalous patterns in ILINet data should have been a warning sign that COVID-19 was already spreading in the US. The purpose of the study was not to identify specific states, but South Dakota has the strongest evidence of any US state, followed by California, Delaware, Maine, and New Mexico.