Current Location:home > Detailed Browse

Article Detail

Dating the First Case of COVID-19 Epidemic from a Probabilistic Perspective

Abstracts

In the early days of the epidemic of coronavirus disease 2019 (COVID-19), due to insufficient knowledge of the pandemic, inadequate nucleic acid tests, lack of timely data reporting, etc., the origin time of the onset of COVID-19 is difficult to determine. Therefore, source tracing is crucial for infectious disease prevention and control. The purpose of this paper is to infer the origin time of pandemic of COVID-19 based on a data and model hybrid driven method. We model the testing positive rate to fit its actual trend, and use the least squares estimation to obtain the optimal model parameters. Further, the kernel density estimation is applied to infer the origin time of pandemic given the specific confidence probability. By selecting 12 representative regions in the United States for analysis, the dates of the first infected case with 50% confidence probability are mostly between August and October 2019, which are earlier than the officially announced date of the first confirmed case in the United States on January 20, 2020. The experimental results indicate that the COVID-19 pandemic in the United States starts to spread around September 2019 with a high confidence probability. In addition, the existing confirmed cases are also used in Wuhan City and Zhejiang Province in China to infer the origin time of COVID-19 and provide the confidence probability. The results show that the spread of COVID-19 pandemic in China is likely to begin in late December 2019.
Download Comment Hits:24891 Downloads:4516
From: 杨周旺
DOI:10.12074/202109.00058
Recommended references: Zhouwang Yang,Yunhe Hu,Zhiwei Ding,Tiande Guo.(2021).Dating the First Case of COVID-19 Epidemic from a Probabilistic Perspective.[ChinaXiv:202109.00058] (Click&Copy)
Version History
[V1] 2021-09-22 11:25:49 chinaXiv:202109.00058V1 Download
Related Paper

Download

Current Browse

Change Subject Browse

Cross Subject Browse

  • - NO