Effect of implementation of interprovincial travel measures
| Study | Study design | Model type | Epidemiological assumptions | Travel measure(s) | Outcomes investigated | Scenarios of intervention | Estimated effect(s) |
| Aleta et al45 | Modelled | Stochastic SEIR-metapopulation model. | Generation time: 7.5 days; R0: 2.4; later period: 3 days. | Wuhan travel measures. | Cases in Mainland China outside of Wuhan. | Travel measures implemented on 23 January. | A reduced reduced number of cases but only in the short term. |
| Chinazzi et al28 | Modelled | Individual-based, stochastic global epidemic and mobility model. | R0: 2.57; doubling time: 4.2; no changes in transmissibility within China. | Wuhan travel measures. | Cases in Mainland China outside of Wuhan. | Travel measures implemented on 23 January. | Reduction of cases was approximately 10% by 31 January (range 1–58%). |
| Timing of epidemic peak. | Wuhan travel ban delayed epidemic progression by 3–5 days in China. | ||||||
| Fang et al40 | Modelled | Dynamic distributed lag regression model. | Incubation period: up to 22 days. | Wuhan travel measures. | Number of cases in cities outside of Hubei from 23 January to 29 February. | Travel measures implemented on 23 January. | COVID-19 cases would be 64.81% higher in 347 cities outside Hubei (20 810 vs 12 626), and 52.64% higher in 16 other cities in Hubei as of 29 February (23 400 vs 15 330). |
| Shi and Fang al41 | Modelled | Autoregressive integrated moving average model. | Incubation period: 4–6 days. | Wuhan travel measures. | Cumulative number of confirmed cases outside Wuhan by 29 February. | Travel measures implemented on 23 January. | Travel ban may have prevented approximately 19 768 (95% CI 13 589 to 25 946) cases outside of Wuhan by 29 February (39% reduction). |
| Tian et al42 | Modelled | Deterministic SEIR model. | R0: 3.15. | Wuhan travel measures. | Arrival times in cities across Mainland China by 19 February. | Travel measures implemented on 23 January. | Delayed arrival time by 2.91 days (95% CI: 2.54, 3.29). |
| Cases in Mainland China outside of Wuhan by 19 February. | National number of cases decreased from 744 000 (±156 000) to 202 000 (±10 000) (72.8% decrease). | ||||||
| Kraemer et al44 | Modelled | Generalised linear model. | Doubling time: 4.0 days (outside Hubei), 7.2 days (inside Hubei); incubation period 5.1 days. | Wuhan travel measures. | Cases in Mainland China outside of Wuhan by end of February. | Travel measures implemented on 23 January. | Travel measures reduced growth rates outside, which became negative after 23 January; provinces with greater mobility from Wuhan displayed more rapidly declining growth rates. |
| Tang et al43 | Modelled | Deterministic SEIR model. | R0: 6.47. | Wuhan travel measures. | Cases in select locations outside of Wuhan. | Travel measures implemented on 23 January. | Travel restriction reduced the number of infected individuals in Beijing over 7 days by 91.14%. |
| Hou et al47 | Modelled | Deterministic SEIR model. | Incubation period: 7 days. | Wuhan travel measures. | Effective reproductive rate. | Travel measures implemented on 23 January. | Travel measures significantly changed transmission dynamics within China. |
| Li et al48 | Modelled | Stochastic SEIR model. | R0 at the beginning of the epidemic to be 2.38. | Wuhan travel measures. | Reproductive number. | Travel measures implemented on 23 January. | Travel measure reduced the reproductive number from 2.38 down to 1.34 and 0.98, in the 1-week and 2-week period immediately following their introduction. |
| Lau et al49 | Observational | Retrospective regression model. | R0: 2.2–3.9: mean incubation period: 5.1 days. | Wuhan travel measures. | Doubling time. | Travel measures implemented on 23 January. | Significant increase in doubling time from 2 to 4 days after lockdown. |
| Wu et al53 | Modelled | Deterministic SEIR model. | R0=2.6, zoonotic force=86/day until 1 January market closure. | Wuhan travel measures. | Exported cases in the rest of Mainland China. | Travel restrictions led to either a 0% or 50% reduction in travel outside of Wuhan. | Even a 50% reduction in intercity mobility would have a negligible effect on the epidemic dynamics. |
| Liu et al52 | Observational | Linear regression. | Incubation period: 3–7 days. | Wuhan travel measures. | Cases exported outside of Wuhan. | Travel measures implemented on 23 January. | A mean value of 129 cases exported per 10 000 people who left Wuhan. |
| Travel measures implemented 2 days earlier. | An estimated 1420 (95% CI 1059 to 1833) cases would have been prevented. | ||||||
| Travel measures implemented 2 days later. | An estimated 1462 (95% CI 1059 to 1833) additional cases would have happened. | ||||||
| Yuan et al46 | Modelled | Regression model. | Incubation period: 5 days. | Wuhan travel measures in combination with a stay-at-home movement. | Cases in 44 regions outside of Wuhan by 27 February. | Travel measures implemented on 23 January. | Reduced the number of cases outside of Wuhan from 41 477 to 30 765. |
| Cases in 44 regions outside of Wuhan by 27 February. | Travel measures implemented 3 days earlier. | Further reduce the number of cases to between 15 768 and 21 245. | |||||
| Su et al54 | Modelled | Deterministic SEIR model. | R0=2.91, 2.78, 2.02 and 1.75 for Beijing, Shanghai, Guangzhou and Shenzhen, respectively. | Wuhan travel measures in combination with other non-pharmaceutical interventions. | Transmission rates in four metropolitan areas of China. | Different contract rates were assumed to result from reduced population flow. | Travel restrictions contributed to a reduction in the contact rate and reduced the time to peak and the number of cases. |
| Jiang et al50 | Modelled | Time-varying sparse vector autoregressive model. | Incubation period: 10 days. | Travel measures introduced in five cities of Hubei (Wuhan, Huanggang, Ezhou, Chibi and Zhijiang). | Daily transmission routes from Hubei to other provinces through 19 February. | Travel measures started to be implemented on 23 January. | Travel restrictions reduced transmission between provinces. |
| Lai et al51 | Modelled | Stochastic SEIR model. | R0: 2.2; incubation period: 5.2 days. | Wuhan travel measures in combination with other non-pharmaceutical interventions. | Cases in Mainland China outside of Wuhan. | Travel measures implemented on 23 January. | Early detection and isolation of cases more effective than travel restrictions; travel restricts reduced the number of cases outside of Wuhan as well as its geographic spread. |
| Cases in Mainland China outside of Wuhan. | If travel restrictions of same magnitude were implemented 1, 2 or 3 weeks earlier. | If interventions were conducted 1, 2 or 3 weeks earlier, cases will reduce by 66%, 86% or 95%, respectively. | |||||
| Total number of cases outside of Wuhan. | If travel restrictions of same magnitude were implemented 1, 2 or 3 weeks later. | If interventions were conducted 1, 2 or 3 weeks later, cases may increase 3-fold, 7-fold or 18-fold, respectively. | |||||
| Hossain et al58 | Modelled | Meta-population model. | R0: 2.92; latent period: 5.2 days; generation time: 8.4 days. | Border control and quarantine. | Arrival time outside of Wuhan. | Theoretical application of measures. | Arrival time is delayed by 32.5 days and 44 days under a low R0 (1.4) but under higher R0 (2.92) only 10 extra days can be gained. |