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Forecasting imported COVID-19 cases in South Korea using mobile roaming data

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  • Soo Beom Choi
  • Insung Ahn
Abstract
As the number of global coronavirus disease (COVID-19) cases increases, the number of imported cases is gradually rising. Furthermore, there is no reduction in domestic outbreaks. To assess the risks from imported COVID-19 cases in South Korea, we suggest using the daily risk score. Confirmed COVID-19 cases reported by John Hopkins University Center, roaming data collected from Korea Telecom, and the Oxford COVID-19 Government Response Tracker index were included in calculating the risk score. The risk score was highly correlated with imported COVID-19 cases after 12 days. To forecast daily imported COVID-19 cases after 12 days in South Korea, we developed prediction models using simple linear regression and autoregressive integrated moving average, including exogenous variables (ARIMAX). In the validation set, the root mean squared error of the linear regression model using the risk score was 6.2, which was lower than that of the autoregressive integrated moving average (ARIMA; 22.3) without the risk score as a reference. Correlation coefficient of ARIMAX using the risk score (0.925) was higher than that of ARIMA (0.899). A possible reason for this time lag of 12 days between imported cases and the risk score could be the delay that occurs before the effect of government policies such as closure of airports or lockdown of cities. Roaming data could help warn roaming users regarding their COVID-19 risk status and inform the national health agency of possible high-risk areas for domestic outbreaks.

Suggested Citation

  • Soo Beom Choi & Insung Ahn, 2020. "Forecasting imported COVID-19 cases in South Korea using mobile roaming data," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0241466
    DOI: 10.1371/journal.pone.0241466
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    References listed on IDEAS

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    1. White, Kenneth J, 1992. "The Durbin-Watson Test for Autocorrelation in Nonlinear Models," The Review of Economics and Statistics, MIT Press, vol. 74(2), pages 370-373, May.
    2. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2021. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," Journal of Econometrics, Elsevier, vol. 220(1), pages 158-180.
    3. Soo Beom Choi & Juhyeon Kim & Insung Ahn, 2019. "Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
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    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

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