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Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina

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  • Soo Beom Choi
  • Insung Ahn

Abstract

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

Suggested Citation

  • Soo Beom Choi & Insung Ahn, 2020. "Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-14, July.
    • Handle: RePEc:plo:pone00:0233855
    DOI: 10.1371/journal.pone.0233855
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    References listed on IDEAS

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    1. Radina P Soebiyanto & Farida Adimi & Richard K Kiang, 2010. "Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    2. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    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.
    4. Arora, Vishal S. & McKee, Martin & Stuckler, David, 2019. "Google Trends: Opportunities and limitations in health and health policy research," Health Policy, Elsevier, vol. 123(3), pages 338-341.
    5. Eui-Ki Kim & Jong Hyeon Seok & Jang Seok Oh & Hyong Woo Lee & Kyung Hyun Kim, 2013. "Use of Hangeul Twitter to Track and Predict Human Influenza Infection," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
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    1. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.

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