Study population and setting
Authors developed a mathematical model that simulates the spatiotemporal dynamics of infections among 375 Chinese cities during the early stage of the epidemic (January 10–23, 2020) and after travel restrictions were implemented (January 24–February 8, 2020). The model divides infections into two classes: 1) documented infected individuals with symptoms severe enough to be confirmed (i.e., observed), and 2) undocumented infected individuals (i.e., not observed).
Summary of Main Findings
The best-fitting model estimated 13,118 total new COVID-19 infections (both observed and unobserved) occurred during January 10–23, 2020 in Wuhan City. An estimated eighty-six percent of people infected were infected by undocumented cases. Nationwide, 16,829 total new COVID-19 infections occurred during January 10–23, 86% of which were infected by undocumented cases. After travel restrictions were put into place, the model estimated that 65% of all infections were documented, which is up from 14% prior to travel restrictions.
In a method similar to performing a “positive control” in a laboratory experiment, the authors verified the model’s ability to recuperate parameters related to unidentified infections using synthetic data prior to applying their techniques to real data.
Uncertainty around the estimates exist due to changes in travel restrictions, control measures, reporting inaccuracies, and changes in care-seeking. The ability of a system to identify all infections are likely impacted by differences in control measures, viral surveillance and testing, and case definition and reporting, and these findings may not be applicable to countries with different control, surveillance, and reporting practices.
Study estimates prevalence and contagiousness of unidentified infections before and after implementation of travel restrictions in China.