Conclusions

Our proposed TFT model has achieved state-of-the-art performance while enabling new forms of interpretability through analyzing complex spatiotermporal patterns. Our model has 4 main achievements: (1) outperforms other models in all evaluation metrics (2) exhibits robust performance dealing with non-stionary data (3) interprets termporal patterns (4) interprets spatial patterns.
Our model has creatively incorporated spatial factors at county level, and such methodology can be easily extended to other datasets at community level (e.g population, socioeconomic factors). Future work could focus on analyzing the sensitivity of input features and adaptively optimizing the model for dynamic data.

Acknowledgement

This work is partially supported by NSF grant CCF-1918626 Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology, and NSF Grant 1835631 for CINES: A Scalable Cyberinfrastructure for Sustained Innovation in Network Engineering and Science.