Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

Linhao Zhong; Lin Mu; Jing Li; Jiaying Wang; Zhe Yin; Darong Liu

doi:10.1109/ACCESS.2020.2979599

Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

IEEE Access. 2020 Mar 9:8:51761-51769. doi: 10.1109/ACCESS.2020.2979599. eCollection 2020.

Authors

Linhao Zhong¹, Lin Mu^{2

3}, Jing Li^{3

4}, Jiaying Wang^{3

4}, Zhe Yin^{3

4}, Darong Liu^{3

4}

Affiliations

¹ 1Key Laboratory of Regional Climate-Environment for Temperate East AsiaInstitute of Atmospheric Physics, Chinese Academy of SciencesBeijing100029China.
² 2College of Life Sciences and OceanographyShenzhen UniversityShenzhen518060China.
³ 3Shenzhen Research Institute, China University of GeosciencesShenzhen518057China.
⁴ 4College of Marine Science and TechnologyChina University of GeosciencesWuhan430074China.

Abstract

The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.

Keywords: Epidemic transmission; infection rate; mathematical model; novel coronavirus; prediction; removal rate.

Grants and funding

This work was supported by the Discipline Layout Project for Basic Research of Shenzhen Science and Technology Innovation Committee under grant No. JCYJ20170810103011913 and supported by the National Science Foundation of China under grant 41975099.