A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

Alberto Aleta; Qitong Hu; Jiachen Ye; Peng Ji; Yamir Moreno

To parameterize the metapopulation epidemiological model we follow Chinazzi et al. [7] and set a generation time of 7.5 days and a reproduction number R 0 equal to 2.4. The latent period is set to 3 days [21] . Although similar . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. values have been reported by other groups, we have also tested larger values of these parameters and the results are consistent (see Materials and Methods) with those reported in what follows. The outbreak is seeded by introducing 40 exposed individuals on Jan. 1st (Jan. 12th in 2019) [7] . Then, the simulations run for 35 days in both cases and we extract the cumulative number of infected cases in each region as a function of time. Note that as there was no travel restriction in 2019, one can see the results obtained with the 2019 data as the more plausible outcome for the 2020 outbreak should travel restrictions were not adopted. In other words, by comparing 2019 with 2020, we can factor out the impact of the early travel reduction in the city of Wuhan and the subsequent changes in the mobility pattern of the population. Fig. 2 shows the cumulative number of infected individuals for Hubei and for the rest of mainland China. The large majority of cases, in all situations considered, are contained within Hubei province. However, when we look at the predictions on the rest of the country, we begin to see growing differences between the scenarios analyzed. The results in Fig. 2B also show that travel restrictions have a positive impact in the temporal evolution of the disease (compare 2019 with 2020), in so far the reduction in the flow of individuals delays the spreading of the disease to the rest of mainland China. However, as it can also be deduced from the trend of the curves in Fig. 2B , there is no indication that the growth in the number of cases will evolve following a different functional form. In other words, if no further measures are taken and no modifications are made to the model, one would find that the number of simulated cases at day τ in 2019 would be the same ∆ days after τ in 2020. Fig. 2A also shows that there is a large difference between the cumulative number of cases predicted in 2020 and the reported one. Although, as previously discussed, this has been reported by several other studies, to ensure that the methodology is correct, and to further analyze the effect of the mobility pattern, in Fig. 3 we show the correlation between the real values of infected individuals and the simulated ones for 2020. We obtain a Pearson correlation of 0.81 implying that the assumptions behind the model, albeit simplistic, can correctly describe the basic dynamics of the epidemic. Furthermore, we also see that the data-driven model is able to predict better the dynamics than its random counterpart. Lastly, we have also studied the difference in the predicted number of cases in each region between 2019 and 2020 at the end of the simulation period. Results are reported in Table I , where we show the median as well as the corresponding 5-95% quantiles. As it can be seen in the Table, in the vast majority of the regions the predicted number of cases in the scenario in which travel is not banned (2019) is larger than for those obtained with data from 2020. Interestingly, there are some regions in which the situation reverses. We have no explanation for