Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism

The outbreak of infectious diseases can have profound impacts on socio-economic balances globally. Accurate short-term forecasting of infectious diseases is crucial for policymakers and healthcare systems. This study proposes a novel deep learning approach for short-term forecasting of infectious disease trends, using COVID-19 confirmed cases and hospitalizations in Japan as a case study. This method provides weekly updates and forecasts outcomes over 1-4 weeks. The proposed model combines long short-term memory (LSTM) networks and multi-head attention mechanism strengths and is trained on public data sourced from open-access platforms. We conduct a comprehensive and rigorous evaluation of the performance of our model. We assess its weekly predictive capabilities over a long period of time by employing multiple error metrics. Furthermore, we carefully explore how the performance of the model varies over time and across geographical locations. The results demonstrate that the proposed model outperforms baseline approaches, particularly in short-term forecasts, achieving lower error rates across multiple metrics. Additionally, the inclusion of mobility data improves the predictive accuracy of the model, especially for longer-term forecasts, by capturing spatio-temporal dynamics more effectively. The proposed model has the potential to assist in decision-making processes, help develop strategies for controlling the spread of infectious diseases, and mitigate the pandemic's impact.