Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting

Despite the end of the pandemic phase and declining mortality rates, COVID-19 remains a significant global health concern. According to the Centers for Disease Control and Prevention (CDC) COVID-19 Dashboard, the disease exhibited a peak weekly test positivity of 18% in the U.S. in 2024. Although the recorded hospitalization rate of 4.8 per 10,000 population on August 10, 2024, may appear comparatively low, it underscores the continuing impact of the disease. According to communications received from the CDC, hospitals are mandated to report COVID-19 hospitalizations again starting in mid-November 2024, indicating the resurgence of the disease. The COVID-19 pandemic strained healthcare resources and overloaded hospitals, exacerbating the dramatic loss of human life. SARS-CoV-2 spreads rapidly, causing severe complications due to its high reproduction rate, the ability to spread via asymptomatic individuals, the prevalence of close-contact settings in densely populated areas, continual mutation into more transmissible variants, and the inconsistent application of preventive public health measures across the U.S. As a result, the demand for travel nurses surged during the pandemic, aligning with shifts in COVID-19 infection hotspots (Cole et al. 2021, Longyear et al. 2020). This was partially a geospatial problem related to the timely allocation of limited human and medical resources. Reliable geographic forecasting of COVID-19 hospital admissions could have alleviated this burden through policy-relevant decision-making and proactive allocation of resources in regional hotspots (i.e.