Journal of Data Science

Hu G, Geng J (2020). Heterogeneity learning for SIRS model: an application to the COVID-19. Statistics and Its Interface. Forthcoming.

Kermack WO, McKendrick AG (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, 115(772): 700–721.

Pan T, Shen W, Hu G (2020). Spatial homogeneity learning for spatially correlated functional data with application to COVID-19 growth rate curves. ArXiv preprint: https://arxiv.org/abs/2008.09227.

Thomas LJ, Huang P, Yin F, Luo XI, Almquist ZW, Hipp JR, et al. (2020). Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity. Proceedings of the National Academy of Sciences, 117(39): 24180–24187.

Tian T, Tan J, Jiang Y, Wang X, Zhang H (2021). Evaluate the timing of resumption of business for the states of New York, New Jersey, and California via a pre-symptomatic and asymptomatic transmission model of COVID-19. Journal of Data Science, 19. In this issue.

Wang L, Zhou Y, He J, Zhu B, Wang F, Tang L, et al. (2020). An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China (with discussion). Journal of Data Science, 18(3): 409–432.

Yang HC, Xue Y, Pan Y, Liu Q, Hu G (2020). Time fused coefficient SIR model with application to COVID-19 epidemic in the United States. ArXiv preprint: https://arxiv.org/abs/2008.04284.