The effect of travel restrictions on the spread of the 2019 novel coronavirus (2019-nCoV) outbreak

Matteo Chinazzi; Jessica T. Davis; Marco Ajelli; Corrado Gioannini; Maria Litvinova; Stefano Merler; Ana Pastore y Piontti; Luca Rossi; Kaiyuan Sun; Cécile Viboud; Xinyue Xiong; Hongjie Yu; M. Elizabeth Halloran; Ira M. Longini; Alessandro Vespignani

for R 0 , the overall evolution of the epidemic is determined by the growth rate of infectious individuals and generates results consistent with those presented here. Results under the assumptions of a different generation time (T g = 9), and the presence of mildly symptomatic individuals not detected internationally are reported in the Supplementary To analyze the effect of the travel ban within Wuhan, we implemented long-range travel restrictions beginning on January 23rd and restricted the local commuting flows on January 25th. Initially, we assume no changes in the transmissibility and disease dynamics. The model output shows no noticeable differences in the epidemic trajectory of Wuhan, while it shows a delay of about 3 days occurring for other locations in China (see Fig. 1A ). The overall reduction of cases in Mainland China excluding Wuhan is close to 20% by February 22, with a relative reduction of cases across specific locations varying in a range from 1% to 57%, (Fig. 2) . With a doubling time of approximately 5 days, this level of reduction corresponds to only a modest delay of the epidemic trajectory of 1 to 6 days. These results are in agreement with estimates resulting from the combination of epidemiological and human mobility data (21) . The model indicates clearly that as of January 23, 2020, the epidemic was seeded in several locations across Mainland China. As an independent validation test, we show in fig 1B) the cumulative number of cases in Mainland China provinces through February 5, 2020, as reported from DXY.cn, a Chinese online community network for physicians, health care professionals, pharmacies and healthcare facilities established in 2000 (22; 16) , and compare these results with model projections. The model projections are highly correlated with the observed data (Pearson's r = 0.77, P < 0.000001), although as expected we find that there are significantly fewer reported cases than projected (See Fig. 1 B) . If we assume that the observed number of cases are the result of different binomial processes that with a certain probability will determine the actual detection of a case, we find that the median ascertainment rate of detecting an infected individual in the population is equal to 19.59% [IQR: 14.36%, 35.58%] in Mainland China. In other words, our model suggests that surveillance only detects one out of five cases.