References
- Abbe, E. (2017), “Community Detection and Stochastic Block Models: Recent Developments,” The Journal of Machine Learning Research, 18, 6446–6531.
- Abbe, E., Fan, J., Wang, K., and Zhong, Y. (2020), “Entrywise Eigenvector Analysis of Random Matrices With Low Expected Rank,” Annals of Statistics, 48, 1452–1474.
- Balakrishnan, S., Xu, M., Krishnamurthy, A., and Singh, A. (2011), “Noise Thresholds for Spectral Clustering,” Advances in Neural Information Processing Systems, 24, 954–962.
- Betensky, R. A., and Feng, Y. (2020), “Accounting for Incomplete Testing in the Estimation of Epidemic Parameters,” International Journal of Epidemiology, 49, 1419–1426. DOI: https://doi.org/10.1093/ije/dyaa116.
- Brownlees, C., Gudmundsson, G. S., and Lugosi, G. (2020+), “Community Detection in Partial Correlation Network Models,” Journal of Business & Economic Statistics, 1–11 (to appear).
- Chen, J., and Yuan, B. (2006), “Detecting Functional Modules in the Yeast Protein–Protein Interaction Network,” Bioinformatics, 22, 2283–2290. DOI: https://doi.org/10.1093/bioinformatics/btl370.
- Chen, Y., Chi, Y., Fan, J., and Ma, C. (2020), “Spectral Methods for Data Science: A Statistical Perspective,” arXiv preprint arXiv:2012.08496.
- Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., Liu, Y., and Mills, M. C. (2020), “Demographic Science Aids in Understanding the Spread and Fatality Rates of Covid-19,” Proceedings of the National Academy of Sciences, 117, 9696–9698. DOI: https://doi.org/10.1073/pnas.2004911117.
- Fan, J., Guo, J., and Zheng, S. (2020), “Estimating Number of Factors by Adjusted Eigenvalues Thresholding,” Journal of the American Statistical Association, 1–33 (to appear).
- Fan, J., Wu, Y., and Feng, Y. (2009), “Local Quasi-likelihood With a Parametric Guide,” Annals of Statistics, 37, 4153–4183.
- Fanelli, D., and Piazza, F. (2020), “Analysis and Forecast of Covid-19 Spreading in China, Italy and France,” Chaos, Solitons & Fractals, 134, 109761.
- Gilbert, M., Pullano, G., Pinotti, F., Valdano, E., Poletto, C., Boëlle, P.-Y., D’Ortenzio, E., Yazdanpanah, Y., Eholie, S. P., Altmann, M., Gutierrez, B., Kraemer, M. U. G., and Colizza, V. (2020), “Preparedness and Vulnerability of African Countries Against Importations of Covid-19: A Modelling Study,” Lancet, 395, 871–877. DOI: https://doi.org/10.1016/S0140-6736(20)30411-6.
- Guan, W.-j., Ni, Z.-y., Hu, Y., Liang, W.-h., Ou, C.-q., He, J.-x., Liu, L., Shan, H., Lei, C.-l., Hui, D. S., Du, B., Li, L.-j., Zeng, G., Yuen, K.-Y., Chen, R.-c., Tang, C.-l., Wang, T., Chen, P.-y., Xiang, J., Li, S.-y., Wang, J.-l., Liang, Z.-j., Peng, Y.-x., Wei, L., Liu, Y., Hu, Y.-h., Peng, P., Wang, J.-m., Liu, J.-y., Chen, Z., Li, G., Zheng, Z.-j., Qiu, S.-q., Luo, J., Ye, C.-j., Zhu, S.-y., and Zhong, N.-s. (2020), “Clinical Characteristics of Coronavirus Disease 2019 in China,” New England Journal of Medicine, 382, 1708–1720. DOI: https://doi.org/10.1056/NEJMoa2002032.
- Holland, P. W., Laskey, K. B., and Leinhardt, S. (1983), “Stochastic Blockmodels: First Steps,” Social Networks, 5, 109 – 137. DOI: https://doi.org/10.1016/0378-8733(83)90021-7.
- Hong, H. G., and Li, Y. (2020), “Estimation of Time-varying Reproduction Numbers Underlying Epidemiological Processes: A New Statistical Tool for the Covid-19 Pandemic,” PloS One, 15, e0236464. DOI: https://doi.org/10.1371/journal.pone.0236464.
- Hu, Z., Ge, Q., Jin, L., and Xiong, M. (2020), “Artificial Intelligence Forecasting of Covid-19 in China,” arXiv:2002.07112.
- Im, H., Ahn, C., Wang, P., and Chen, C. (2020), “An Early Examination: Psychological, Health, and Economic Correlates and Determinants of Social Distancing Amidst Covid-19,” PsyArXiv, DOI: https://doi.org/10.31234/osf.io/9ravu.
- Jin, J. (2015), “Fast Community Detection by Score,” Annals of Statistics, 43, 57–89.
- Kucharski, A., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R., Sun, F., Jit, M., Munday, J., Davies, N., Gimma, A., Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C., Clifford, S., Quilty, B., Bosse, N., and Flasche, S. (2020), “Early Dynamics of Transmission and Control of Covid-19: A Mathematical Modelling Study,” The Lancet Infectious Diseases, 20, 553–558. DOI: https://doi.org/10.1016/S1473-3099(20)30144-4.
- Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H., Azman, A. S., Reich, N. G., and Lessler, J. (2020), “The Incubation Period of 2019-ncov from Publicly Reported Confirmed Cases: Estimation and Application,” Annals of Internal Medicine, 172, 577–582. DOI: https://doi.org/10.7326/M20-0504.
- Lei, J. and Rinaldo, A. (2015), “Consistency of Spectral Clustering in Stochastic Block Models,” Annals of Statistics, 43, 215–237.
- Lippi, G., Mattiuzzi, C., Sanchis-Gomar, F., and Henry, B. M. (2020), “Clinical and Demographic Characteristics of Patients Dying From Covid-19 in Italy vs. China,” Journal of Medical Virology, 92, 1759–1760. DOI: https://doi.org/10.1002/jmv.25860.
- Liu, Z., magal, p., Seydi, O., and Webb, G. (2020), “Predicting the Cumulative Number of Cases for the Covid-19 Epidemic in China From Early Data,” Mathematical Biosciences and Engineering, 17, 3040–3051. DOI: https://doi.org/10.3934/mbe.2020172.
- Ng, A. Y., Jordan, M. I., and Weiss, Y. (2001), “On Spectral Clustering: Analysis and an Algorithm,” in Advances in Neural Information Processing Systems, T. Dietterich and S. Becker and Z. Ghahramani, eds., 849–856. Vancouver, BC: MIT Press.
- Peng, L., Yang, W., Zhang, D., Zhuge, C., and Hong, L. (2020), “Epidemic Analysis of Covid-19 in China by Dynamical Modeling,” medRxiv.
- Rajapakse, J. C., Gupta, S., and Sui, X. (2017), “Fitting Networks Models for Functional Brain Connectivity,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 515–519. DOI: https://doi.org/10.1109/ISBI.2017.7950573.
- Roda, W. C., Varughese, M. B., Han, D., and Li, M. Y. (2020), “Why is It Difficult to Accurately Predict the Covid-19 Epidemic?” Infectious Disease Modelling, 5, 271 – 281. DOI: https://doi.org/10.1016/j.idm.2020.03.001.
- Rohe, K., Chatterjee, S., and Yu, B. (2011), “Spectral Clustering and the High-dimensional Stochastic Blockmodel,” The Annals of Statistics, 39, 1878–1915. DOI: https://doi.org/10.1214/11-AOS887.
- Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986), “Learning Representations by Back-propagating Errors,” Nature, 323, 33– 536. DOI: https://doi.org/10.1038/323533a0.
- Unacast (2020), “Unacast Social Distancing Dataset.” https://www.unacast.com/covid19/social-distancing-scoreboard.
- von Luxburg, U. (2007), “A Tutorial on Spectral Clustering,” Statistics and Computing, 17, 395–416. DOI: https://doi.org/10.1007/s11222-007-9033-z.
- Wang, L. and Wong, A. (2020), “Covid-net: A Tailored Deep Convolutional Neural Network Design for Detection of Covid-19 Cases from Chest X-ray Images,” Science Reports, 10, 19549.
- Wasserman, S., and Anderson, C. (1987), “Stochastic a Posteriori Blockmodels: Construction and Assessment,” Social Networks, 9, 1 – 36. DOI: https://doi.org/10.1016/0378-8733(87)90015-3.
- Xie, M., Singh, K., and Strawderman, W. E. (2011), “Confidence Distributions and a Unifying Framework for Meta-Analysis,” Journal of the American Statistical Association, 106, 320–333. DOI: https://doi.org/10.1198/jasa.2011.tm09803.
- Yang, Z., Zeng, Z., Wang, K., Wong, S.-S., Liang, W., Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., Liang, J., Liu, X., Li, S., Li, Y., Ye, F., Guan, W., Yang, Y., Li, F., anddYuqi Xie, S. L., Liu, B., Wang, Z., Zhang, S., Wang, Y., Zhong, N., and He, J. (2020), “Modified Seir and AI Prediction of the Epidemics Trend of Covid-19 in China Under Public Health Interventions,” Journal of Thoracic Disease, 12, 165–174. DOI: https://doi.org/10.21037/jtd.2020.02.64.
- Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., and Wang, X. (2020), “Deep Learning-based Detection for Covid-19 From Chest CT Using Weak Label,” medRxiv.