
The novel coronavirus can spread before patients are symptomatic, according to two new studies, one to be published in the May issue of Emerging Infectious Diseases and the other published March 16 in Science.
A team of researchers led by Dr. Zhanwei Du of the University of Texas in Austin found that that "time between cases in a chain of transmission is less than a week and that more than 10% of patients are infected by somebody who has the virus but does not yet have symptoms," the university said in a statement.
Du and colleagues assessed more than 450 coronavirus infection reports from 93 cities in China and discovered that more than 1 in 10 infections were from people who had the virus but were not yet sick (Emerging Infectious Diseases, May 2020, Vol. 26:5).
"This provides evidence that extensive control measures including isolation, quarantine, school closures, travel restrictions, and cancellation of mass gatherings may be warranted," said study author Lauren Ancel Meyers, PhD, in the university statement. "Asymptomatic transmission definitely makes containment more difficult."
In a related study, researchers from Columbia University Mailman School of Public Health found that undetected cases were responsible for the rapid spread of the coronavirus outbreak in China.
A team led by Jeffrey Shaman, PhD, used a computer model of the outbreak to track its spread. The group found the following:
- Of all the infections, 86% were undocumented prior to the January 23 Wuhan travel shutdown.
- These undocumented infections were half as contagious as documented infections, yet they were the source of two-thirds of documented infections.
"The explosion of COVID-19 cases in China was largely driven by individuals with mild, limited, or no symptoms who went undetected," Shaman said in a university statement. "Depending on their contagiousness and numbers, undetected cases can expose a far greater portion of the population to virus than would otherwise occur. We find for COVID-19 in China these undetected infected individuals are numerous and contagious. These stealth transmissions will continue to present a major challenge to the containment of this outbreak going forward."





![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=100&q=70&w=100)







![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)







