Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model

References

• Alzahrani, S. I., I. A. Aljamaan, and E. A. Al-Fakih. 2020. Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. Journal of Infection and Public Health 13 (7):914–9. doi:10.1016/j.jiph.2020.06.001.
   PubMed Web of Science ®Google Scholar
• Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis 27 (2):93–115. doi:10.1111/j.1538-4632.1995.tb00338.x.
   Web of Science ®Google Scholar
• Arora, P., H. Kumar, and B. K. Panigrahi. 2020. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons, and Fractals 139:110017. doi:10.1016/j.chaos.2020.110017.
   PubMed Web of Science ®Google Scholar
• Banerjee, S, and Y. Lian. 2022. Data driven COVID-19 spread prediction based on mobility and mask mandate information. Applied Intelligence (Dordrecht, Netherlands) 52 (2):1969–78. doi:10.1007/s10489-021-02381-8.
   PubMed Web of Science ®Google Scholar
• Calafiore, G. C., C. Novara, and C. Possieri. 2020. A modified SIR model for the COVID-19 contagion in Italy. In Proceedings of the 59th IEEE Conference on Decision and Control, 3889–94.
   Google Scholar
• Ceylan, Z. 2020. Estimation of COVID-19 prevalence in Italy, Spain, and France. The Science of the Total Environment 729:138817. doi:10.1016/j.scitotenv.2020.138817.
   PubMed Web of Science ®Google Scholar
• Cuevas, E. 2020. An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine 121:103827. doi:10.1016/j.compbiomed.2020.103827.
   PubMed Web of Science ®Google Scholar
• Deng, S., S. Wang, H. Rangwala, L. Wang, and Y. Ning. 2020. Cola-GNN: Cross-location attention based graph neural networks for long-term ILI prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 245–54.
   Google Scholar
• Gallagher, S, and J. Baltimore. 2017. Comparing compartment and agent-based models. In Proceedings of the Joint Statistical Meeting. Alexandria, VA: American Statistical Association.
   Google Scholar
• He, S., Y. Peng, and K. Sun. 2020. SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics 101 (3):1667–80. doi:10.1007/s11071-020-05743-y.
   PubMed Web of Science ®Google Scholar
• Jing, N., Z. Shi, Y. Hu, and J. Yuan. 2022. Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases. Applied Intelligence (Dordrecht, Netherlands) 52 (3):3303–18. doi:10.1007/s10489-021-02616-8.
   PubMed Web of Science ®Google Scholar
• Kapoor, A., X. Ben, L. Liu, B. Perozzi, M. Barnes, M. Blais, and S. O’Banion. 2020. Examining COVID-19 forecasting using spatio-temporal graph neural networks. arXiv Preprint arXiv:2007 :3113.
   Google Scholar
• Kiamari, M., G. Ramachandran, Q. Nguyen, E. Pereira, J. Holm, and B. Krishnamachari. 2020. COVID-19 risk estimation using a time-varying SIR-model. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, 36–42.
   Google Scholar
• Kipf, T. N, and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. Poster presented at International Conference on Learning Representations, Palais des Congrès Neptune, Toulon, France, April 26.
   Google Scholar
• Lai, G., W.-C. Chang, Y. Yang, and H. Liu. 2018. Modeling long-and short-term temporal patterns with deep neural networks. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 95–104.
   Google Scholar
• Li, Y., R. Yu, C. Shahabi, and Y. Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Poster presented at International Conference on Learning Representations, Vancouver, BC, Canada, April 30.
   Google Scholar
• Loshchilov, I, and F. Hutter. 2019. Decoupled weight decay regularization. Poster presented at International Conference on Learning Representations, New Orleans, Louisiana, May 8.
   Google Scholar
• Panagopoulos, G., G. Nikolentzos, and M. Vazirgiannis. 2021. Transfer graph neural networks for pandemic forecasting. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 4838–45.
   Google Scholar
• Pandey, G., P. Chaudhary, R. Gupta, and S. Pal. 2020. SEIR and regression model based COVID-19 outbreak predictions in India. arXiv Preprint arXiv 2004:958.
   Google Scholar
• Rodríguez, A., N. Muralidhar, B. Adhikari, A. Tabassum, N. Ramakrishnan, and B. A. Prakash. 2021. Steering a historical disease forecasting model under a pandemic: Case of flu and COVID-19. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 4855–63.
   Google Scholar
• Roy, S., G. S. Bhunia, and P. K. Shit. 2021. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Modeling Earth Systems and Environment 7 (2):1385–91. doi:10.1007/s40808-020-00890-y.
   PubMed Web of Science ®Google Scholar
• Shamil, M. S., F. Farheen, N. Ibtehaz, I. M. Khan, and M. S. Rahman. 2021. An agent-based modeling of COVID-19: Validation, analysis, and recommendations. Cognitive Computation 1–12. doi:10.1007/s12559-020-09801-w.
   PubMed Web of Science ®Google Scholar
• Singh, R. K., M. Rani, A. S. Bhagavathula, R. Sah, A. J. Rodriguez-Morales, H. Kalita, C. Nanda, S. Sharma, Y. D. Sharma, A. A. Rabaan, et al. 2020. Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance 6 (2):e19115. doi:10.2196/19115.
   PubMedGoogle Scholar
• Wang, P., X. Zheng, G. Ai, D. Liu, and B. Zhu. 2020. Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran. Chaos, Solitons, and Fractals 140:110214. doi:10.1016/j.chaos.2020.110214.
   PubMed Web of Science ®Google Scholar
• Wu, Y., Y. Yang, H. Nishiura, and M. Saitoh. 2018. Deep learning for epidemiological predictions. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 1085–8.
   Google Scholar
• Yu, B., H. Yin, and Z. Zhu. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, 3634–40.
   Google Scholar
• Zeroual, A., F. Harrou, A. Dairi, and Y. Sun. 2020. Deep learning methods for forecasting COVID-19 time-series data: A comparative study. Chaos, Solitons, and Fractals 140:110121. doi:10.1016/j.chaos.2020.110121.
   PubMed Web of Science ®Google Scholar