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Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics

Author

Listed:
  • Bulut Boru

    (College of Engineering, Koc University, Rumelifeneri Yolu, Istanbul 34450, Turkey)

  • M. Emre Gursoy

    (College of Engineering, Koc University, Rumelifeneri Yolu, Istanbul 34450, Turkey)

Abstract

The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s Community Mobility Reports (CMRs) toward forecasting future COVID-19 case counts. We utilize features derived from the amount of daily activity in different location categories such as transit stations versus residential areas based on the time series in CMRs, as well as historical COVID-19 daily case and test counts, in forecasting future cases. Our method trains optimized regression models for different countries based on dynamic and data-driven selection of the feature set, regression type, and time period that best fit the country under consideration. The accuracy of our method is evaluated on 13 countries with diverse characteristics. Results show that our method’s forecasts are highly accurate when compared to the real COVID-19 case counts. Furthermore, visual analysis shows that the peaks, plateaus and general trends in case counts are also correctly predicted by our method.

Suggested Citation

  • Bulut Boru & M. Emre Gursoy, 2022. "Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics," Data, MDPI, vol. 7(11), pages 1-24, November.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:166-:d:978450
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    References listed on IDEAS

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    1. Nelson Mileu & Nuno M. Costa & Eduarda M. Costa & André Alves, 2022. "Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data," Data, MDPI, vol. 7(8), pages 1-17, July.
    2. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Yun Li & Moming Li & Megan Rice & Haoyuan Zhang & Dexuan Sha & Mei Li & Yanfang Su & Chaowei Yang, 2021. "The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective," IJERPH, MDPI, vol. 18(3), pages 1-23, January.
    4. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

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