Gallo Marin, B. et al. Predictors of COVID-19 severity: A literature review. Rev. Med. Virol. 31, 1–10 (2021).
Google Scholar
Worldometers. COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/ (Accessed 5 May 2021) (2021).
The New York Times. Coronavirus World Map: Tracking the Global Outbreak. https://www.nytimes.com/interactive/2020/world/coronavirus-maps.html (Accessed 5 May) (2021).
Sartorius, B., Lawson, A. & Pullan, R. Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England. Sci. Rep. 11, 5378 (2021).
Google Scholar
Gamio, L. & Symonds, A. Global Virus Cases Reach New Peak, Driven by India and South America. https://nyti.ms/3xYVO94 (Accessed on 5 May 2021) (2021).
Samuel, J. et al. COVID-19 public sentiment insights and machine learning for tweets classification. Information 11, 314 (2020).
Google Scholar
Borriello, A., Master, D., Pellegrini, A. & Rose, J. M. Preferences for a COVID-19 vaccine in Australia. Vaccine 39, 473–479 (2021).
Google Scholar
Samuel, J. et al. Feeling positive about reopening? New normal scenarios from COVID-19 US reopen sentiment analytics. IEEE Access 8, 142173–142190 (2020).
Google Scholar
Max Roser, E. O.-O., Ritchie, H. & Hasell, J. Coronavirus pandemic (COVID-19). https://ourworldindata.org/coronavirus (Accessed on 5 June 2021) (2020).
Centers for Disease Control and Prevention. About variants of the virus that causes COVID-19. https://www.cdc.gov/coronavirus/2019-ncov/transmission/variant.html (Accessed on 5 June 2021) (2021).
Ali, G. G. M. N. et al. Public perceptions of COVID-19 vaccines: Policy implications from US spatiotemporal sentiment analytics. Healthcare 9, 1110 (2021).
Google Scholar
Al Zobbi, M., Alsinglawi, B., Mubin, O. & Alnajjar, F. Measurement method for evaluating the lockdown policies during the COVID-19 pandemic. Int. J. Environ. Res. Public Health 17, 5574 (2020).
Google Scholar
Lu, X., Yuan, D., Chen, W. & Fung, J. A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China (2020). Preprint on webpage at www.researchsquare.com/article/rs-73671/v1.
Vinceti, M. et al. Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking. EClinicalMedicine 25, 100457 (2020).
Google Scholar
Rahman, M. et al. Machine learning on the COVID-19 pandemic, human mobility and air quality: A review. IEEE Access 9, 72420–72450 (2021).
Google Scholar
Rahman, M. et al. COVID-19 pandemic severity, lockdown regimes, and people’s mobility: Early evidence from 88 countries. Sustainability 12, 9101 (2020).
Google Scholar
Silva, P. C. et al. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos Solitons Fractals 139, 110088 (2020).
Google Scholar
Rahman, M. M. et al. Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon 7, e06200 (2021).
Google Scholar
USAFacts. US COVID-19 cases and deaths by state. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/ (Accessed 5 June 2021) (2020).
Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ. 369(8242), m1328 (2020).
Swapnarekha, H., Behera, H. S., Nayak, J. & Naik, B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos Solitons Fractals 138, 109947 (2020).
Google Scholar
Xiang, Y. et al. COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models. Infect. Dis. Model. 6, 324–342 (2021).
Roy, A. & Kar, B. Characterizing the spread of COVID-19 from human mobility patterns and SocioDemographic indicators. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, 39–48 (2020).
Scarpone, C. et al. A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: A cross-sectional case study of COVID-19 incidence in Germany. Int. J. Health Geogr. 19, 32 (2020).
Google Scholar
Polyzos, S., Samitas, A. & Spyridou, A. E. Tourism demand and the COVID-19 pandemic: An LSTM approach. Tour. Recreat. Res. 46(2), 1777053 (2020).
Iwendi, C. et al. COVID-19 patient health prediction using boosted random forest algorithm. Front. Public Health 8, 357 (2020).
Google Scholar
Hou, X. et al. Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race (2020). Preprint on webpage at https://doi.org/10.1101/2020.10.04.20206763v1.
Tang, B. et al. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect. Dis. Model. 5, 248–255 (2020).
Google Scholar
Zhao, S. et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int. J. Infect. Dis. 92, 214–217 (2020).
Google Scholar
Fanelli, D. & Piazza, F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals 134, 109761 (2020).
Google Scholar
Choi, S. & Ki, M. Estimating the reproductive number and the outbreak size of COVID-19 in Korea. Epidemiol. Health 42, e2020011 (2020).
Tolles, J. & Luong, T. Modeling epidemics with compartmental models. JAMA 323, 2515–2516 (2020).
Google Scholar
Soures, N. et al. SIRNet: Understanding social distancing measures with hybrid neural network model for COVID-19 infectious spread. arXiv (2020). preprint on webpage at arXiv:2004.10376.
Chimmula, V. K. R. & Zhang, L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 135, 109864 (2020).
Google Scholar
Wang, D. et al. Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19. arXiv (2020). Preprint on webpage at arXiv:2010.09648.
Kai, D., Goldstein, G.-P., Morgunov, A., Nangalia, V. & Rotkirch, A. Universal masking is urgent in the covid-19 pandemic: Seir and agent based models, empirical validation, policy recommendations. arXiv (2020). Preprint on webpage at arXiv:2004.13553.
Panovska-Griffiths, J. et al. Modelling the potential impact of mask use in schools and society on COVID-19 control in the UK. Sci. Rep. 11, 8747 (2021).
Google Scholar
Szczepanek, R. Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from cracow and machine learning. PeerJ 8, e10132 (2020).
Google Scholar
Ahmed, I., Ahmad, M., Rodrigues, J. J., Jeon, G. & Din, S. A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65, 102571 (2020).
Google Scholar
Spada, A. et al. Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2. Sci. Rep. 11, 8358 (2021).
Google Scholar
Jiang, F. et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2, e000101 (2017).
Google Scholar
Khalifa, N. E. M., Taha, M. H. N., Ali, D. E., Slowik, A. & Hassanien, A. E. Artificial intelligence technique for gene expression by tumor RNA-Seq data: A novel optimized deep learning approach. IEEE Access 8, 22874–22883 (2020).
Google Scholar
Dexter, G. P., Grannis, S. J., Dixon, B. E. & Kasthurirathne, S. N. Generalization of machine learning approaches to identify notifiable conditions from a statewide health information exchange. AMIA Summits Transl. Sci. Proc. 2020, 152–161 (2020).
Google Scholar
Wang, Y., Liao, Z., Mathieu, S., Bin, F. & Tu, X. Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model. J. Hazard. Mater. 404, 123965 (2021).
Google Scholar
Shao, Y. E., Hou, C.-D. & Chiu, C.-C. Hybrid intelligent modeling schemes for heart disease classification. Appl. Soft Comput. 14, 47–52 (2014).
Google Scholar
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Google Scholar
Sun, J. et al. Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Sci. Rep. 10, 1–10 (2020).
Google Scholar
Chen, Y., Qin, R., Zhang, G. & Albanwan, H. Spatial temporal analysis of traffic patterns during the COVID-19 epidemic by vehicle detection using planet remote-sensing satellite images. Remote Sens. 13, 208 (2021).
Google Scholar
Briz-Redón, Á. & Serrano-Aroca, Á. A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Sci. Total Environ. 728, 138811 (2020).
Google Scholar
Liu, Q. et al. Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sens. 12, 1576 (2020).
Google Scholar
Jarvis, K. F. & Kelley, J. B. Temporal dynamics of viral load and false negative rate influence the levels of testing necessary to combat COVID-19 spread. Sci. Rep. 11, 1–12 (2021).
Google Scholar
Ugarte, M. D., Adin, A., Goicoa, T. & Militino, A. F. On fitting spatio-temporal disease mapping models using approximate Bayesian inference. Stat. Methods Med. Res. 23, 507–530 (2014).
Google Scholar
Zhang, C. H. & Schwartz, G. G. Spatial disparities in coronavirus incidence and mortality in the United States: An ecological analysis as of May 2020. J. Rural Health 36, 433–445 (2020).
Google Scholar
Fitzpatrick, K. M., Harris, C. & Drawve, G. Fear of COVID-19 and the mental health consequences in America. Psychol. Trauma Theory Res. Pract. Policy 12, S17–S21 (2020).
Google Scholar
Lalmuanawma, S., Hussain, J. & Chhakchhuak, L. Applications of machine learning and artificial intelligence for covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 139, 110059 (2020).
Google Scholar
Adhikari, B., Xu, X., Ramakrishnan, N. & Prakash, B. A. Epideep: Exploiting embeddings for epidemic forecasting. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 577–586 (2019).
Gautam, Y. Transfer learning for COVID-19 cases and deaths forecast using LSTM network. ISA Transactions (2021).
Chen, S. et al. Exploring feasibility of multivariate deep learning models in predicting covid-19 epidemic. Front. Public Health 9, 661615 (2021).
Google Scholar
Venna, S. R. et al. A novel data-driven model for real-time influenza forecasting. IEEE Access 7, 7691–7701 (2018).
Google Scholar
Wu, D. et al. Deepgleam: a hybrid mechanistic and deep learning model for covid-19 forecasting. arXiv (2021). Preprint on webpage at arXiv:2102.06684.
Zhu, X. et al. Attention-based recurrent neural network for influenza epidemic prediction. BMC Bioinform. 20, 575 (2019).
Google Scholar
Ben Said, A., Erradi, A., Aly, H. & Mohamed, A. Predicting covid-19 cases using bidirectional lstm on multivariate time series. arXiv (2020). Preprint on webpage at arXiv:2009.12325.
Aktay A., A. et al. Google COVID-19 Community Mobility Reports: anonymization process description (version 1.1). arXiv (2020). Preprint on webpage at arXiv:2004.04145.
SafeGraph. SafeGraph Places Schema. https://docs.safegraph.com/docs (Accessed 15 Sept 2020) (2020).
Johns Hopkins University. COVID-19 dashboard by the center for systems science and engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/us-map (Accessed 15 Sept 2020) (2020).
Ray, E. L. et al. Ensemble forecasts of coronavirus disease 2019 (covid-19) in the us. MedRXiv (2020). Preprint on webpage at https://www.medrxiv.org/content/10.1101/2020.08.19.20177493v1.
Cartenì, A., Di Francesco, L. & Martino, M. How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study. Sci. Total Environ. 741, 140489 (2020).
Google Scholar
Reich, N. et al. har96, x. Zhang, jinghuichen, G. Espana, X. Xinyue, H. Biegel, L. Castro, Y. Wang, qjhong, E. Lee, A. Baxter, S. Bhatia, E. Ray, and abrennen, and ERDC CV19 Modeling Team (2020). Preprint on webpage at https://github.com/reichlab/covid19-forecast-hub/tree/master/data-processed/COVIDhub-ensemble.
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
Google Scholar
Yao, W., Huang, P. & Jia, Z. Multidimensional lstm networks to predict wind speed. In 2018 37th Chinese Control Conference (CCC), 7493–7497 (IEEE, 2018).
Cromley, E. K. & McLafferty, S. L. GIS and Public Health (Guilford Press, XXX, 2011).
Veličković, P. et al. Cross-modal recurrent models for weight objective prediction from multimodal time-series data. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 178–186 (2018).
Shi, X. & Yeung, D.-Y. Machine learning for spatiotemporal sequence forecasting: A survey. arXiv (2018). Preprint on webpage at arXiv:1808.06865.
Bengio, S., Vinyals, O., Jaitly, N. & Shazeer, N. Scheduled sampling for sequence prediction with recurrent neural networks. arXiv (2015). Preprint on webpage at arXiv:1506.03099.
Taieb, S. B. & Hyndman, R. Boosting multi-step autoregressive forecasts. In International Conference on Machine Learning, Vol. 32, 109–117 (PMLR, 2014). http://proceedings.mlr.press/v32/taieb14.html.
Veličković, P. et al. Graph attention networks. arXiv (2017). Preprint on webpage at arXiv:1710.10903.
Hamilton, W. L., Ying, R. & Leskovec, J. Inductive representation learning on large graphs. arXiv (2017). Preprint on webpage at arXiv:1706.02216.
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