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

Summarizing, we have studied a data-driven metapopulation model that allows assessing the effects of early travel restrictions in Wuhan and Hubei province. Even if our modeling framework is simpler than other more sophisticated We also note that the observed shift of the epidemic curve and the effect of travel restriction might be underestimated because the large majority of people had already moved before these mobility restrictions were implemented (as it can be seen in Fig. 1 ). Our study is limited in several aspects that can constitute future research goals. First, the geographic resolution allowed by the mobility data used here is low. Considering large regions have the undesired effect that one can not add structure to the population and therefore the dynamics within each subpopulation is constrained by the homogeneous mixing hypothesis. This limitation could be overcome if less granular spatial and temporal data becomes available. Secondly, and perhaps more important as it currently represents a scientific challenge, we have assumed that the transmissibility does not change during the whole simulation period. This implies that changes in behavioral patterns of the population are not fully accounted for nor they can be completely disentangled from those associated with travel restrictions. Understanding how to deal with such behavioral changes is key for the development of more realistic descriptions of the large-scale spreading of diseases. Finally, another critical feature of current models that needs to be improved in future research is the use of disease parameters −notably R 0 − that are constant both in time and across populations.