Tracking Stealth Transmission of COVID-19


A Columbia professor has used data modeling to explore what he calls the “stealth transmission” of COVID-19. Jeff Shaman used a data-intensive computer model of the coronavirus outbreak including mobility data and reported infection rates to simulate the spread of the virus within China. The model also estimated the contagiousness and amount of undocumented infections in China before and after the government instituted a travel shutdown and other control measures in Wuhan.

For every confirmed case of the virus, there were an estimated six additional people in China with undocumented infections prior to the implementation of control measures. Though about half as infectious as documented cases, people with undetected symptoms were responsible for about 79 percent of China’s coronavirus cases. He found that people who had the virus but went undiagnosed were the source of the majority of China's documented COVID-19 cases in the early period of the outbreak. There is a high probability that the virus will follow similar “stealth transmission” patterns in other countries around the world.

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