Version 1
: Received: 26 February 2020 / Approved: 27 February 2020 / Online: 27 February 2020 (11:00:23 CET)
How to cite:
Luo, X.; Feng, S.; Yang, J.; Peng, X.; Cao, X.; Zhang, J.; Yao, M.; Zhu, H.; Li, M.Y.; Wang, H.; Jin, Z. Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model. Preprints2020, 2020020398. https://doi.org/10.20944/preprints202002.0398.v1
Luo, X.; Feng, S.; Yang, J.; Peng, X.; Cao, X.; Zhang, J.; Yao, M.; Zhu, H.; Li, M.Y.; Wang, H.; Jin, Z. Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model. Preprints 2020, 2020020398. https://doi.org/10.20944/preprints202002.0398.v1
Luo, X.; Feng, S.; Yang, J.; Peng, X.; Cao, X.; Zhang, J.; Yao, M.; Zhu, H.; Li, M.Y.; Wang, H.; Jin, Z. Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model. Preprints2020, 2020020398. https://doi.org/10.20944/preprints202002.0398.v1
APA Style
Luo, X., Feng, S., Yang, J., Peng, X., Cao, X., Zhang, J., Yao, M., Zhu, H., Li, M.Y., Wang, H., & Jin, Z. (2020). Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model. Preprints. https://doi.org/10.20944/preprints202002.0398.v1
Chicago/Turabian Style
Luo, X., Hao Wang and Zhen Jin. 2020 "Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model" Preprints. https://doi.org/10.20944/preprints202002.0398.v1
Abstract
The ongoing outbreak of the novel coronavirus pneumonia (also known as COVID-19) has triggered a series of stringent control measures in China, such as city closure, traffic restrictions, contact tracing and household quarantine. These containment efforts often lead to changes in the contact pattern among individuals of the population. Many existing compartmental epidemic models fail to account for the effects of contact structure. In this paper, we devised a pairwise epidemic model to analyze the COVID-19 outbreak in China based on confirmed cases reported during the period February 3rd--17th, 2020. By explicitly incorporating the effects of family clusters and contact tracing followed by household quarantine and isolation, our model provides a good fit to the trajectory of COVID-19 infections and is useful to predict the epidemic trend. We obtained the average of the reproduction number $R=1.494$ ($95\%$ CI: $1.483-1.507$) for Hubei province and $R=1.178$ ($95\%$ CI: $1.145-1.158$) for China (except Hubei), suggesting that some existing studies may have overestimated the reproduction number by neglecting the dynamical correlations and clustering effects. We forecasted that the COVID-19 epidemic would peak on February 13th ($95\%$ CI: February $9-17$th) in Hubei and 6 days eariler in the regions outside Hubei. Moreover the epidemic was expected to last until the middle of March in China (except Hubei) and late April in Hubei. The sensitivity analysis shows that ongoing exposure for the susceptible and population clustering play an important role in the disease propagation. With the enforcement of household quarantine measures, the reproduction number $R$ effectively reduces and epidemic quantities decrease accordingly. Furthermore, we gave an answer to the public concern on how long the stringent containment strategies should maintain. Through numerical analysis, we suggested that the time for the resumption of work and production in China (except Hubei) and Hubei would be the middle of March and the end of April, 2020, respectively. These constructive suggestions may bring some immeasurable social-economic benefits in the long run.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
