An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres

References

• Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle (pp. 267–281). Akadémiai Kiadó.
   Google Scholar
• Ball, F., & Clancy, D. (1993). The final size and severity of a generalised stochastic multitype epidemic model. Advances in Applied Probability, 25(4), 721–736. https://doi.org/https://doi.org/10.2307/1427788
   Web of Science ®Google Scholar
• Becker, N. (1977). On a general stochastic epidemic model. Theoretical Population Biology, 11(1), 23–36. https://doi.org/https://doi.org/10.1016/0040-5809(77)90004-1
   Web of Science ®Google Scholar
• Becker, N., & Britton, T. (1999). Statistical studies of infectious disease incidence. Journal of the Royal Statistical Society: Series B, 61(2), 287–307. https://doi.org/https://doi.org/10.1111/rssb.1999.61.issue-2
   Google Scholar
• Chen, Y. C., Lu, P. E., Chang, C. S., & Liu, T. H. (2020). A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE Transactions on Network Science and Engineering, 7(4), 3279–3294. https://doi.org/10.1109/TNSE.2020.3024723
   Web of Science ®Google Scholar
• Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. https://doi.org/https://doi.org/10.1080/01621459.1988.10478639
   Web of Science ®Google Scholar
• Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 115, 513–583. https://doi.org/https://doi.org/10.1098/rstl.1825.0026
   Google Scholar
• González, R. E. R. (2020). Different scenarios in the dynamics of SARS-Cov-2 infection: An adapted ODE model. arXiv:2004.01295. https://doi.org/10.21203/rs.3.rs–29563/v1.
   Google Scholar
• He, S., Peng, Y., & Sun, K. (2020). SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics, 101, 1667–1680. https://doi.org/https://doi.org/10.1007/s11071-020-05743-y
   Web of Science ®Google Scholar
• Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653. https://doi.org/https://doi.org/10.1137/S0036144500371907
   Web of Science ®Google Scholar
• Huang, N. E., Qiao, F., & Tung, K. K. (2020). A data-driven model for predicting the course of COVID-19 epidemic with applications for China, Korea, Italy, Germany, Spain. UK and USA. medRxiv e-prints. https://doi.org/10.1101/2020.03.28.20046177.
   Google Scholar
• Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S., Lau, E. H., Wong, J. Y., Xing, X., Xiang, N., Wu, Y., Li, C., Chen, Q., Li, D., Liu, T., Zhao, J., Liu, M., …Feng, Z. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. The New England Journal of Medicine, 382, 1199–1207. https://doi.org/https://doi.org/10.1056/NEJMoa2001316
   PubMed Web of Science ®Google Scholar
• Lin, H., Liu, W., Gao, H., Nie, J., & Fan, Q. (2020). Comparative analysis of early dynamic trends in novel coronavirus outbreak: A modeling framework. medRxiv e-prints. https://doi.org/10.1101/2020.02.21.20026468.
   Google Scholar
• Liu, C., Wu, X., Niu, R., Wu, X., & Fan, R. (2020). A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19). Nonlinear Dynamics, 101, 1777–1787. https://doi.org/https://doi.org/10.1007/s11071-020-05704-5
   Web of Science ®Google Scholar
• Maier, B. F., & Brockmann, D. (2020). Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China. Science, 368(6492), 742–746. https://doi.org/https://doi.org/10.1126/science.abb4557
   PubMed Web of Science ®Google Scholar
• Mohamadou, Y., Halidou, A., & Kapen, P. T. (2020). A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Applied Intelligence, 50, 3913–3925. https://doi.org/https://doi.org/10.1007/s10489-020-01770-9
   PubMed Web of Science ®Google Scholar
• Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of COVID-19 in China by dynamical modeling. medRxiv e-prints. https://doi.org/10.1101/2020.02.16.20023465.
   Google Scholar
• Rajagopal, K., Hasanzadeh, N., Parastesh, F., Hamarash, I. I., Jafari, S., & Hussain, I. (2020). A fractional-order model for the novel coronavirus (COVID-19) outbreak. Nonlinear Dynamics, 101, 711–718. https://doi.org/https://doi.org/10.1007/s11071-020-05757-6
   PubMed Web of Science ®Google Scholar
• Richards, F. J. (1959). A flexible growth function for empirical use. Journal of Experimental Botany, 10(2), 290–300. https://doi.org/https://doi.org/10.1093/jxb/10.2.290
   Web of Science ®Google Scholar
• Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/https://doi.org/10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Song, P. X., Wang, L., Zhou, Y., He, J., Zhu, B., Wang, F., Tang, L., & Eisenberg, M. (2020). An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. medRxiv e-prints. https://doi.org/10.1101/2020.02.29.20029421.
   Google Scholar
• Sun, H., Qiu, Y., Yan, H., Huang, Y., Zhu, Y., & Chen, S. X. (2020). Tracking and predicting COVID-19 epidemic in China Mainland. medRxiv e-prints. https://doi.org/10.1101/2020.02.17.20024257.
   Google Scholar
• Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., & Wu, J. (2020). Estimation of the transmission risk of 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462. https://doi.org/https://doi.org/10.3390/jcm9020462
   PubMed Web of Science ®Google Scholar
• Verhulst, P. F. (1838). Notice sur la loi que la population poursuit dans son accroissement. Correspondance Mathématique Et Physique, 10, 113–121.
   Google Scholar
• Wallinga, J., & Lipsitch, M. (2007). How generation intervals shape the relationship between growth rates and reproductive numbers. Proceedings of the Royal Society B: Biological Sciences, 274, 599–604. https://doi.org/https://doi.org/10.1098/rspb.2006.3754
   PubMed Web of Science ®Google Scholar
• Wang, X., Wang, S., Lan, Y., Tao, X., & Xiao, J. (2020). The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19. Nonlinear Dynamics, 101, 2003–2012. https://doi.org/https://doi.org/10.1007/s11071-020-05736-x
   Web of Science ®Google Scholar
• Wang, T., Wu, Y., Lau, J. Y. N., Yu, Y., Liu, L., Li, J., Zhang, K., Tong, W., & Jiang, B. (2020). A four-compartment model for the COVID-19 infection – implications on infection kinetics, control measures, and lockdown exit strategies. Precision Clinical Medicine, 3(2), 104–112. https://doi.org/https://doi.org/10.1093/pcmedi/pbaa018
   Google Scholar
• Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. The Lancet, 395(10225), 689–697. https://doi.org/https://doi.org/10.1016/S0140-6736(20)30260-9
   PubMed Web of Science ®Google Scholar
• Xu, C., Yu, Y., Chen, Y., & Lu, Z. (2020). Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model. Nonlinear Dynamics, 101, 1621–1634. https://doi.org/https://doi.org/10.1007/s11071-020-05946-3
   PubMed Web of Science ®Google Scholar
• 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., Luo, S., …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(2), 165–174. https://doi.org/https://doi.org/10.21037/jtd
   Google Scholar
• Yip, P. S., & Chen, Q. (1998). Statistical inference for a multitype epidemic model. Journal of Statistical Planning and Inference, 71(1–2), 229–244. https://doi.org/https://doi.org/10.1016/S0378-3758(98)00087-1
   Web of Science ®Google Scholar
• Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., & Wang, M. H. (2020). 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. International Journal of Infectious Diseases, 92, 214–217. https://doi.org/https://doi.org/10.1016/j.ijid.2020.01.050
   PubMed Web of Science ®Google Scholar
• Zhao, S., Musa, S. S., Fu, H., He, D., & Qin, J. (2019). Simple framework for real-time forecast in a data-limited situation: The Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example. Parasites & Vectors, 12, 344. https://doi.org/https://doi.org/10.1186/s13071-019-3602-9
   Google Scholar