Assessing the asymptomatic proportion of SARS-CoV-2 infection with age in China before mass vaccination

Author:

Wang Zengmiao1ORCID,Wu Peiyi1,Wang Jingyuan23,Lourenço José4ORCID,Li Bingying1,Rader Benjamin56ORCID,Laine Marko7ORCID,Miao Hui8,Wang Ligui9,Song Hongbin9,Bharti Nita10ORCID,Brownstein John S.511ORCID,Bjornstad Ottar N.1012,Dye Christopher13,Tian Huaiyu1ORCID

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, People's Republic of China

2. School of Computer Science and Engineering, Beihang University, Beijing, People's Republic of China

3. Peng Cheng Laboratory, Shenzhen, People's Republic of China

4. Biosystems and Integrative Sciences Institute, University of Lisbon, Lisbon, Portugal

5. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA

6. Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA

7. Meteorological Research Unit, Finnish Meteorological Institute, Helsinki, Finland

8. Department of Statistics, College of Art and Science, Ohio State University, Columbus, OH, USA

9. Center of Disease Control and Prevention, PLA, Beijing, People's Republic of China

10. Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA

11. Harvard Medical School, Harvard University, Boston, MA, USA

12. Department of Entomology, College of Agricultural Sciences, Pennsylvania State University, University Park, PA, USA

13. Department of Biology, University of Oxford, Oxford, UK

Abstract

Some asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19. Here, we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate the relationship between asymptomatic proportion and age. Epidemiological data obtained before mass vaccination provide valuable insights into the nature of pathogenicity of SARS-CoV-2. The data were collected by multiple rounds of city-wide PCR testing with contact tracing, where each patient was monitored for symptoms through the whole course of infection. The clinical endpoint (asymptomatic or symptomatic) for each patient was recorded (the pre-symptomatic patients were classified as symptomatic). We find that the proportion of infections that are asymptomatic declines with age (coefficient = −0.006, 95% CI: −0.008 to −0.003,p< 0.01), falling from 42% (95% CI: 6–78%) in age group 0–9 years to 11% (95% CI: 0–25%) in age group greater than 60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern, together with the distribution of cases by age, can explain most of the reported variation in asymptomatic proportions among cities. Our analysis suggests that SARS-CoV-2 surveillance strategies should take account of the variation in asymptomatic proportion with age.

Funder

Key Scientific and Technology Project of Inner Mongolia Autonomous Region

Young Elite Scientist Sponsorship Program

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Research and Development Program of China

Major Project of New Generation Artificial Intelligence

Beijing Science and Technology Planning Project

State Key Laboratory of Remote Sensing Science

Beijing Advanced Innovation Program for Land Surface Science

Beijing Natural Science Foundation

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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