Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

Abstract

The COVID-19, the disease caused by the novel coronavirus 2019 (SARS-CoV-2), has caused graving woes across the globe since first reported in the epicenter Wuhan, Hubei, China, December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that put more than 900 million people housebound for more than two months since the lockdown of Wuhan on 23 January 2020 when other provinces in China followed suit. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns before and during the lockdown period. We quantify the synchrony of mobility patterns across provinces and within provinces. Using these mobility data, we calibrate movement flow between provinces in combination with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicenter Hubei. As such, infections can propagate further into other interconnected places both near and far, thereby necessitating synchronous lockdowns. Moreover, our data-driven modeling analysis shows that lockdowns and consequently reduced mobility lag a certain time to elicit an actual impact on slowing down the spreading and ultimately putting the epidemic under check. In spite of the vastly heterogeneous demographics and epidemiological characteristics across China, mobility data shows that massive travel restrictions have been applied consistently via a top-down approach along with high levels of compliance from the bottom up.

FIG. 1:

Universal lockdowns across China along with a highly coordinated nationwide epidemic response. The plots each show the inter-province mobility, measured by daily total influx and outflux of inter-province travels using Baidu migration data, changes for the period from late December 2019 to March 2020. The Chinese government imposed by far the largest scale of strict travel restrictions on more than 11 million people (beyond) on January 23, 2020 (Level 1 response), amid the busiest period of the year for domestic travels (‘chunyun’, travels made during the Lunar New Year). Such massive travel restrictions have caused a dramatic reduction in travel volume, not only for the outflow from the epicenter Wuhan (Hubei) but also nationwide. Depending on the level of regional disease mitigation efforts, only a few provinces relax their travel restrictions (lowering from Level 1 to Level 3) a month later. The color corresponds to the level of response prior to and after the epidemic outbreak in Wuhan.

FIG. 2:

Changes in inter-province and intra-province mobility over key dates throughout the epidemic outbreak. The non-diagonal elements of each heatmap plot show the migration index (a quantity proportional to the overall volume, as defined by Baidu) of pairwise travel destinations from province A (source) to B (target) while the diagonal the intra-province mobility index (travels within a given province). Prior to lockdowns, the travel peaks correspond to popular domestic travel routes during the Lunar New Year such as from Guangdong to Hunan (e.g., migrant workers return from coastal areas to inner lands to reunite with family). Both the inflow to and the outflow from Hubei (epicenter) are kept at extremely low levels except for essential travels that support epidemic response and basic living needs. These heatmaps complement Fig. 1 by providing more detailed views of mobility during the outbreak.

FIG. 3:

Spatio-temporal pattern of early-stage epidemic spreading of COVID-19 in China. The phase and magnitude of local outbreaks within each province depend on the cumulative population inflow received from epicenter Hubei. Panel (a) shows the cumulative cases on the date 10 March 2020 and the red color corresponds to the most affected provinces. Panel (b) shows the timing of emerging infections (the appearance of the 10th diagnosed case) of each province versus the cumulative mobility index (proportional to the overall volume of travels received from Hubei for the period from 1 Jan 2020 to 27 Jan 2020). The dot size is proportional to the cumulative number of cases during that time window. Most affected provinces are those receiving greater population outflow from Hubei along with much earlier phases of epidemic outgrowth.

FIG. 4:

Quantifying synchrony in reduced mobility due to national lockdowns and massive travel restrictions and assessing their impacts through reductions in disease transmissions inferred from our data-driven modeling. Panel (a) show the intra-province mobility and their strong correlations with the curve of Hubei province. Compared to the year before (numbers given in brackets in the legend), the mobility patterns exhibit significantly higher correlations, suggesting a high level of synchrony during the lockdown period. Panel (b) shows the correlation of mobility index among prefectures within Hubei province. Panel (c) shows the inferred transmission rates using a data-driven multi-compartment framework. In all provinces, reduced mobility levels translate to drastically suppressed transmissions. The effect of lockdowns on transmission reductions has seen a pronounced delay (varying by one or two weeks) for two reasons: (1) people need time to adjust to reduced social contacts despite decreasing mobility (2) local community transmissions cannot be easily controlled unless strict ‘cordon sanitaire’ (home quarantine) is enforced. The color corresponds to the level of epidemic response.

FIG. 5:

Province-specific effective basic reproductive ratio, R_t, inferred from data-driven modeling. Highly coordinated nationwide massive travel restrictions are able to suppress infections across China, despite each province’s distinct pace and magnitude of epidemic impact mitigation. The plot shows that province-specific R_t is heterogeneous and has a distinctive pattern with respect to the implementation of local lockdown measures across provinces, but R_t is uniformly suppressed after two weeks of nationwide lockdown and invariably drops well below one after one month.