Predicting the Spread of Infectious Diseases: A Time Series Approach Using Historical Case Data and Mobility Patterns
Keywords:
COVID-19, Forecasting Models, Infectious Diseases, Mobility Data, Public Health, Time Series AnalysisAbstract
This paper explores the use of time series analysis to predict the spread of infectious diseases, particularly focusing on COVID-19. A detailed methodology that combines historical case data with mobility patterns to boost the accuracy of our forecasts is proposed. In this study, a close look at different time series models, such as Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), and various machine learning techniques, to estimate daily case numbers was taken. Evaluation of the effectiveness of mobility data from multiple sources to see how it influences disease transmission was also considered. Our findings reveal that factoring in mobility patterns significantly improves prediction accuracy compared to models that rely only on historical case data. Notably, the ensemble method that merges SARIMA and Long Short – Term Memory (LSTM) models achieved the lowest prediction error Root Mean Square Error (RMSE) = 156.3 and Mean Absolute Error (MAE) = 112.7 when tested over a 14-day forecast period. These results indicate that human mobility is a key indicator of disease spread and can offer valuable insights for early intervention strategies. This research carries significant implications for public health policy, providing a framework to better anticipate disease outbreaks and optimize resource allocation during emerging infectious disease crises.
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