## 5 Conclusions  
This study highlights the potential of spatiotemporal modeling in predicting shelter and lodging demand during disasters, with a focus on hurricane evacuations amid the COVID-19 pandemic. Using vehicle trajectory data combined with Generalized Additive Models (GAMs) and Gaussian radial basis functions, we developed a framework to forecast vehicle counts and estimate demand at shelters and lodging facilities, supporting more efficient disaster response and resource allocation. While the model showed strong performance in predicting traditional shelter demand, it was less accurate for lodging facilities, primarily due to simplifying assumptions (e.g., four people per room, uniform vehicle occupancy rates) that failed to fully capture real-world variability. These findings underscore the need to refine modeling approaches to better account for diverse shelter types and evacuee behaviors. The compounded challenges of hurricanes and pandemics amplify the necessity for real-time data integration into predictive models. The COVID-19 pandemic introduced complexities like non-congregate sheltering needs, which this study addresses by highlighting strategies for dynamic resource allocation while maintaining public health safeguards. Despite limitations, including anonymization constraints, limited data coverage, and potential sampling biases, this research offers valuable insights for disaster management. Incorporating real-time data into forecasting frameworks represents a significant advancement, enabling emergency managers to anticipate demand and optimize response efforts. While our focus in this study was on a specific disaster scenario, studying the generalizability of the proposed GAM based predictive modeling to other geographic regions or hazards is of potential interest. The proposed spatiotemporal modeling approach is general and can be adapted to new regions and disaster scenarios, provided relevant data is available. The model's ability to generalize depends on how similar the evacuation dynamics and facility distributions in the training data are to those in the new context. Future studies can evaluate the model's performance across other disaster types (e.g., floods, wildfires, earthquakes) and different geographic regions with varied transportation networks and demographics. Integrating mobile phone location data, behavioral insights, and real-time facility occupancy will enhance predictive accuracy. In addition, future research could integrate machine learning (ML) tools, such as random forest, extreme gradient boosting (XGBoost), and gated recurrent units (GRUs), as applied in geohazard prediction [44; 50]. These models could complement GAM-based interpretability with higher predictive power of ML approaches, as discussed, for example, by [51] in which a Lasso approach is integrated with a GAM. In summary, this study offers a scalable, interpretable modeling approach to support disaster preparedness and evidence-based policymaking, helping emergency managers design adaptive and equitable evacuation strategies, especially in contexts where multiple hazards intersect, such as hurricanes and pandemics. Conceptualization, D.E.T. and O.A.V. ; Methodology, D.E.T. ; Software, D.E.T. ; Validation, O.A.V., O.A. and J.Y. ; Formal analysis, D.E.T. ; Investigation, J.Y. ; Data curation, O.A. and J.Y. ; Writing--original draft, D.E.T. ; Supervision, O.A.V. and E.E.O. ; Project administration, O.A.V. and E.E.O. ; Funding acquisition, O.A.V. and E.E.O. All authors have read and agreed to the published version of the manuscript. This research was supported by the US National Science Foundation (NSF) grant CMMI-2101091. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors would like to acknowledge the funding support from NSF. The authors declare no conflicts of interest.