@article{adc72d96ae74484fb1fa62b60ca36f51,
title = "Assessing the asymptomatic proportion of SARS-CoV-2 infection with age in China before mass vaccination",
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.",
keywords = "Age-stratified compartment model, Asymptomatic infection, SARS-CoV-2",
author = "Zengmiao Wang and Peiyi Wu and Jingyuan Wang and Jos{\'e} Louren{\c c}o and Bingying Li and Benjamin Rader and Marko Laine and Hui Miao and Ligui Wang and Hongbin Song and Nita Bharti and Brownstein, {John S.} and Bjornstad, {Ottar N.} and Christopher Dye and Huaiyu Tian",
note = "Funding Information: This study was supported by the Scientific and Technological Innovation 2030-Major Project of New Generation Artificial Intelligence (2021ZD0111201); National Natural Science Foundation of China (82073616, 82161148011, 72171013, 82204160); National Key Research and Development Program of China; Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202106); Beijing Science and Technology Planning Project (Z201100005420010); Beijing Science and Technology Planning Project; Beijing Advanced Innovation Program for Land Surface Science; Key Scientific and Technology Project of Inner Mongolia Autonomous Region (2021ZD0006); Fundamental Research Funds for the Central Universities (2021NTST17). The funders had no role in study design, data collection and analysis, the decision to publish, or in preparation of the manuscript Funding Information: This study was supported by the Scientific and Technological Innovation 2030—Major Project of New Generation Artificial Intelligence (2021ZD0111201); National Natural Science Foundation of China (82073616, 82161148011, 72171013, 82204160); National Key Research and Development Program of China; Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202106); Beijing Science and Technology Planning Project (Z201100005420010); Beijing Science and Technology Planning Project; Beijing Advanced Innovation Program for Land Surface Science; Key Scientific and Technology Project of Inner Mongolia Autonomous Region (2021ZD0006); Fundamental Research Funds for the Central Universities (2021NTST17). The funders had no role in study design, data collection and analysis, the decision to publish, or in preparation of the manuscript. Acknowledgements Publisher Copyright: {\textcopyright} 2022 The Authors.",
year = "2022",
month = oct,
day = "12",
doi = "10.1098/rsif.2022.0498",
language = "English",
volume = "19",
journal = "Journal of the Royal Society Interface",
issn = "1742-5689",
publisher = "The Royal Society",
number = "195",
}