The coronavirus SARS-CoV-2 causes the current outbreak of the respiratory disease COVID-19. The virus was first detected in Wuhan, China, at the end of 2019 and has spread globally by now. Understanding infection pathways is important to curb the continuing spread of the virus. In particular, it has been hypothesized that unrecognized infections could play an important role in the growing number of cases. When an infection with mild or even no symptoms goes undocumented, the patient can continue to move freely and expose others to the virus.
Sen Pei, Jeffrey Shaman, Columbia University, NY, USA, and colleagues have developed a mathematical model that simulates the development of infections, based on reported cases within China during the weeks around the shutdown of travel to and from Wuhan. The team used location data collected by a Chinese technology company to estimate the daily numbers of travelers between 375 Chinese cities. In their model, they separated cases into documented and undocumented infections, with different transmission rates for these two groups. The researchers then adjusted their model to match the number of observed, documented cases.
The team was able to simulate different scenarios using this model. They estimate that 86 % of all infections were undocumented during the period before travel restrictions were imposed on January 23, 2020. When the transmission from undocumented infection was artificially set zero, known infections were reduced by 78.8 % across China in the simulation. This means undocumented infections could have been the source of over three-fourths of the known cases. According to the researchers, these unknown, likely mild cases seem to have facilitated the rapid spread of the virus throughout China before major control measures were implemented. This implies that a significantly improved identification of currently unknown infections might be needed to fully control the outbreak of COVID-19.
- Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2),
Ruiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman,
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