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A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones.

Ryosuke Susuta1, Kenta Yamada2, Hideki Takayasu1

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This study shows that combining GPS data and COVID-19 blog keywords can forecast infection trends. An adaptive model achieved 90% accuracy in predicting new cases seven days ahead, aiding public health preparedness.

Keywords:
COVID-19GPS dataforecastingregressionrobust variable selectionsocial media datatrend decomposition

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • Accurate forecasting of infectious disease spread is crucial for effective public health interventions.
  • Traditional surveillance methods can be slow to detect emerging trends.
  • Novel data sources offer potential for real-time monitoring of disease dynamics.

Purpose of the Study:

  • To assess the feasibility of using Global Positioning System (GPS) data and the frequency of COVID-19-related blog terms to predict new infection trends.
  • To develop and compare forecasting models based on these data sources.
  • To evaluate the accuracy and effectiveness of adaptive learning models for infectious disease forecasting.

Main Methods:

  • Linear regression analysis was employed to model the relationship between data inputs and infection rates.
  • Time series trend decomposition and Spearman's rank correlation were used for variable selection.
  • Two models were constructed: a fixed-period model and a sequential adaptive model that updates with new infection waves.
  • Model performance was evaluated based on forecasting accuracy for new COVID-19 cases.

Main Results:

  • The adaptive model demonstrated superior performance in capturing long-term trends compared to the fixed-period model.
  • The adaptive model achieved approximately 90% accuracy in forecasting COVID-19 infection rates seven days in advance.
  • While exact value prediction remains challenging, the combined data approach significantly improved forecasting capabilities.

Conclusions:

  • Combining GPS data and COVID-19 blog word frequency through a dynamic, wave-based learning model offers a promising approach to enhance infectious disease forecasting.
  • This methodology has significant implications for improving public health preparedness and response strategies.
  • The study highlights the potential of integrating diverse data streams for real-time epidemiological surveillance.