Multi-chain Fudan-CCDC model for COVID-19 -- a revisit to Singapore’s case

Abstract

Background: COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.

Methods: We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function.

Results: The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of ±30% fluctuation from simulation results.

Conclusion: The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.

Graphical abstract

Keywords

COVID-19 / Singapore / multi-chain Fudan-CCDC model

References

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Data and materials availability

The data employed in this paper are acquired from WIND (like Bloomberg), and the situation reports of the World Health Organization (https://www.who.int). All the data can be accessed publicly. No other data are used in this paper.

AUTHOR CONTRIBUTIONS

The algorithms are implemented by H. P., which are based on the single-chain model implemented by N. S., and designed by W. C. All authors conceived the study, carried out the analysis, discussed the results, drafted the first manuscript, critically read and revised the manuscript, and gave final approval for publication.

ACKNOWLEDGEMENTS

W. C. is supported by the National Natural Science Foundation of China (No. 11671098) and partially supported by Shanghai Science and technology research program (No. 19JC1420101). J. C. is supported in part by the National Natural Science Foundation of China (No. 11971121). Y.Y. is supported by Shanghai Sailing Program (No. 20YF1412400).

We are very grateful to the efforts of Cheng’s group members and the supports by School of Mathematical Sciences, Fudan University and School of Mathematics, Shanghai University of Finance and Economics. We thank Z. B. at UC Davis, X. W. at South University of Science and Technology of China, Q. D. at Columbia University, L. C. at UC Irvine, C.W. at University of Massachusetts at Dartmouth and X. (H.) L. at University of North Carolina at Charlotte. W. C. also thanks W.G. and R. L. at Fudan University, Z. S. at Fusion Fin Trade, Allen at Wind, N. L. at Winning Health Technology Group Company (winning.com.cn), X. C. and J. Z. at Mathworks, and W. Z. All of us thank our families’ supports.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Hanshuang Pan, Nian Shao, Yue Yan, Xinyue Luo, Shufen Wang, Ling Ye, Jin Cheng and Wenbin Chen declare that they have no conflict of interests.

All procedures performed in studies were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

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