Tags

Type your tag names separated by a space and hit enter

Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland.
Innovation (N Y). 2020 Aug 28; 1(2):100026.I

Abstract

Recently, considerable efforts have been focused on intensifying the screening process for asymptomatic COVID-19 cases in the Chinese Mainland, especially for up to 10 million citizens living in Wuhan City by nucleic acid testing. However, a high percentage of domestic asymptomatic cases did not develop into symptomatic ones, which is abnormal and has drawn considerable public attention. Here, we aimed to investigate the prevalence of COVID-19 infections in the Chinese Mainland from a statistical perspective, as it is of referential significance for other regions. By conservatively assuming a development time lag from pre-symptomatic (i.e., referring to the infected cases that were screened before the COVID-19 symptom onset) to symptomatic as an incubation time of 5.2 days, our results indicated that 92.5% of those tested in Wuhan City, China, and 95.1% of those tested in the Chinese Mainland should have COVID-19 syndrome onset, which was extremely higher than their corresponding practical percentages of 0.8% and 3.3%, respectively. We propose that a certain false positive rate may exist if large-scale nucleic acid screening tests for asymptomatic cases are conducted in common communities with a low incidence rate. Despite adopting relatively high-sensitivity, high-specificity detection kits, we estimated a very low prevalence of COVID-19 infections, ranging from 10-6 to 10-4 in both Wuhan City and the Chinese Mainland. Thus, the prevalence rate of asymptomatic infections in China had been at a very low level. Furthermore, given the lower prevalence of the infection, close examination of the data for false positive results is necessary to minimize social and economic impacts.

Authors+Show Affiliations

Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, P. R. China.State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, P. R. China. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32914140

Citation

Jia, Xiaoqian, et al. "Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland." Innovation (New York, N.Y.), vol. 1, no. 2, 2020, p. 100026.
Jia X, Chen J, Li L, et al. Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland. Innovation (N Y). 2020;1(2):100026.
Jia, X., Chen, J., Li, L., Jia, N., Jiangtulu, B., Xue, T., Zhang, L., Li, Z., Ye, R., & Wang, B. (2020). Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland. Innovation (New York, N.Y.), 1(2), 100026. https://doi.org/10.1016/j.xinn.2020.100026
Jia X, et al. Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland. Innovation (N Y). 2020 Aug 28;1(2):100026. PubMed PMID: 32914140.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modeling the Prevalence of Asymptomatic COVID-19 Infections in the Chinese Mainland. AU - Jia,Xiaoqian, AU - Chen,Junxi, AU - Li,Liangjing, AU - Jia,Na, AU - Jiangtulu,Bahabaike, AU - Xue,Tao, AU - Zhang,Le, AU - Li,Zhiwen, AU - Ye,Rongwei, AU - Wang,Bin, Y1 - 2020/08/04/ PY - 2020/04/28/received PY - 2020/07/21/accepted PY - 2020/9/11/entrez PY - 2020/9/12/pubmed PY - 2020/9/12/medline KW - Bayes' formula KW - COVID-19 KW - China KW - asymptomatic infections KW - subclinical infection SP - 100026 EP - 100026 JF - Innovation (New York, N.Y.) JO - Innovation (N Y) VL - 1 IS - 2 N2 - Recently, considerable efforts have been focused on intensifying the screening process for asymptomatic COVID-19 cases in the Chinese Mainland, especially for up to 10 million citizens living in Wuhan City by nucleic acid testing. However, a high percentage of domestic asymptomatic cases did not develop into symptomatic ones, which is abnormal and has drawn considerable public attention. Here, we aimed to investigate the prevalence of COVID-19 infections in the Chinese Mainland from a statistical perspective, as it is of referential significance for other regions. By conservatively assuming a development time lag from pre-symptomatic (i.e., referring to the infected cases that were screened before the COVID-19 symptom onset) to symptomatic as an incubation time of 5.2 days, our results indicated that 92.5% of those tested in Wuhan City, China, and 95.1% of those tested in the Chinese Mainland should have COVID-19 syndrome onset, which was extremely higher than their corresponding practical percentages of 0.8% and 3.3%, respectively. We propose that a certain false positive rate may exist if large-scale nucleic acid screening tests for asymptomatic cases are conducted in common communities with a low incidence rate. Despite adopting relatively high-sensitivity, high-specificity detection kits, we estimated a very low prevalence of COVID-19 infections, ranging from 10-6 to 10-4 in both Wuhan City and the Chinese Mainland. Thus, the prevalence rate of asymptomatic infections in China had been at a very low level. Furthermore, given the lower prevalence of the infection, close examination of the data for false positive results is necessary to minimize social and economic impacts. SN - 2666-6758 UR - https://www.unboundmedicine.com/medline/citation/32914140/Modeling_the_Prevalence_of_Asymptomatic_COVID_19_Infections_in_the_Chinese_Mainland_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S2666-6758(20)30026-6 DB - PRIME DP - Unbound Medicine ER -
Try the Free App:
Prime PubMed app for iOS iPhone iPad
Prime PubMed app for Android
Prime PubMed is provided
free to individuals by:
Unbound Medicine.