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

ABSTRACT: 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

SUBMITTER: Jiao S 

PROVIDER: S-EPMC11926351 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature