the effect of travel restrictions on the geographical spread of covid 19 between large CORD-Papers-2022-06-02 (Version 1)

Title: The effect of travel restrictions on the geographical spread of COVID-19 between large cities in China: a modelling study
Abstract: BACKGROUND: To contain the spread of COVID-19 a cordon sanitaire was put in place in Wuhan prior to the Lunar New Year on 23 January 2020. We assess the efficacy of the cordon sanitaire to delay the introduction and onset of local transmission of COVID-19 in other major cities in mainland China. METHODS: We estimated the number of infected travellers from Wuhan to other major cities in mainland China from November 2019 to February 2020 using previously estimated COVID-19 prevalence in Wuhan and publicly available mobility data. We focused on Beijing Chongqing Hangzhou and Shenzhen as four representative major cities to identify the potential independent contribution of the cordon sanitaire and holiday travel. To do this we simulated outbreaks generated by infected arrivals in these destination cities using stochastic branching processes. We also modelled the effect of the cordon sanitaire in combination with reduced transmissibility scenarios to simulate the effect of local non-pharmaceutical interventions. RESULTS: We find that in the four cities given the potentially high prevalence of COVID-19 in Wuhan between December 2019 and early January 2020 local transmission may have been seeded as early as 18 January 2020. By the time the cordon sanitaire was imposed infections were likely in the thousands. The cordon sanitaire alone did not substantially affect the epidemic progression in these cities although it may have had some effect in smaller cities. Reduced transmissibility resulted in a notable decrease in the incidence of infection in the four studied cities. CONCLUSIONS: Our results indicate that sustained transmission was likely occurring several weeks prior to the implementation of the cordon sanitaire in four major cities of mainland China and that the observed decrease in incidence was likely attributable to other non-pharmaceutical transmission-reducing interventions.
Published: 2020-08-19
Journal: BMC Med
DOI: 10.1186/s12916-020-01712-9
DOI_URL: http://doi.org/10.1186/s12916-020-01712-9
Author Name: Quilty Billy J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quilty_billy_j
Author Name: Diamond Charlie
Author link: https://covid19-data.nist.gov/pid/rest/local/author/diamond_charlie
Author Name: Liu Yang
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_yang
Author Name: Gibbs Hamish
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gibbs_hamish
Author Name: Russell Timothy W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/russell_timothy_w
Author Name: Jarvis Christopher I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jarvis_christopher_i
Author Name: Prem Kiesha
Author link: https://covid19-data.nist.gov/pid/rest/local/author/prem_kiesha
Author Name: Pearson Carl A B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pearson_carl_a_b
Author Name: Clifford Samuel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/clifford_samuel
Author Name: Flasche Stefan
Author link: https://covid19-data.nist.gov/pid/rest/local/author/flasche_stefan
Author Name: Klepac Petra
Author link: https://covid19-data.nist.gov/pid/rest/local/author/klepac_petra
Author Name: Eggo Rosalind M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m
Author Name: Jit Mark
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_mark
sha: 5a753323ca84399715d008f38f89990a894a229f
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 32814572
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/32814572
pmcid: PMC7437104
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437104
url: https://doi.org/10.1186/s12916-020-01712-9 https://www.ncbi.nlm.nih.gov/pubmed/32814572/
has_full_text: TRUE
Keywords Extracted from Text Content: 1-8 COVID-19 travellers Wuhan Supplementary Fig. 3 human upper bounds seed chains non-Chunyun people solid Figure S1 A-F seed Hubei province people left Wuhan https://doi.org/10. November-31 people left Wuhan [9 TWR Shenzhen Wuhan before SARS-CoV-2 cordon sanitaire-type SF Supplementary Appendix 1-5 REM R e < 1 lower-traffic COVID-19 R e = 2.2 Figure S7 Fig. 4 NPIs Wuhan Figure S1 A-F coronavirus Endo coronavirus disease 2019 CMMID COVID-19 line R e > 1 Figure S3 Shenzhen. centre [17] Hubei R e Figure S6 coronavirus 2 outflow Chunyun CIJ PK contact networks LNY individuals travellers [1] SC Figure S4 persons Baidu's patients participants Competing interests MJ
Extracted Text Content in Record: First 5000 Characters:Background: To contain the spread of COVID-19, a cordon sanitaire was put in place in Wuhan prior to the Lunar New Year, on 23 January 2020. We assess the efficacy of the cordon sanitaire to delay the introduction and onset of local transmission of COVID-19 in other major cities in mainland China. Methods: We estimated the number of infected travellers from Wuhan to other major cities in mainland China from November 2019 to February 2020 using previously estimated COVID-19 prevalence in Wuhan and publicly available mobility data. We focused on Beijing, Chongqing, Hangzhou, and Shenzhen as four representative major cities to identify the potential independent contribution of the cordon sanitaire and holiday travel. To do this, we simulated outbreaks generated by infected arrivals in these destination cities using stochastic branching processes. We also modelled the effect of the cordon sanitaire in combination with reduced transmissibility scenarios to simulate the effect of local non-pharmaceutical interventions. Results: We find that in the four cities, given the potentially high prevalence of COVID-19 in Wuhan between December 2019 and early January 2020, local transmission may have been seeded as early as 1-8 January 2020. By the time the cordon sanitaire was imposed, infections were likely in the thousands. The cordon sanitaire alone did not substantially affect the epidemic progression in these cities, although it may have had some effect in smaller cities. Reduced transmissibility resulted in a notable decrease in the incidence of infection in the four studied cities. Conclusions: Our results indicate that sustained transmission was likely occurring several weeks prior to the implementation of the cordon sanitaire in four major cities of mainland China and that the observed decrease in incidence was likely attributable to other non-pharmaceutical, transmission-reducing interventions. Since late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID- 19) , has spread to over 114 countries and was declared a pandemic on 11 March 2020 [1] . Some countries have enacted cordon sanitairetype travel restrictions, either to prevent the export of infections from an initial disease epicentre (such as Wuhan in January 2020 [2] or Northern Italy in March 2020 [3] ) to other countries and regions or to prevent the import of infections from high-risk countries or regions (such as the USA's ban on travel from Europe [4] ). Cordon sanitaires aim to curb the number of infected travellers entering a region with a high proportion of susceptible individuals, where they may seed additional chains of transmission. However, historically, they at best delay, rather than prevent outbreaks elsewhere [5] . Hence, the efficacy of cordon sanitaires in averting or delaying outbreaks in other locations is an open question. Chinese authorities imposed a cordon sanitaire on the city of Wuhan on 23 January 2020 [2] and extended the travel restrictions to the whole of Hubei province by 26 January 2020 [6] . The restrictions were imposed 1 day prior to the Lunar New Year (LNY) holidays and during Chunyun, the 40-day holiday travel period that marks the largest annual human migration event in the world [7] . At the same time, other public health interventions, such as physical distancing, were also enacted across China [8] . This study aims to assess the impacts of the cordon sanitaire around Wuhan, the epicentre of the COVID-19 pandemic, on reducing incidence and delaying outbreaks in other well-connected large population centres in mainland China. We used publicly available mobility data based on location-based service (LBS) provided by Baidu Huiyan, to construct four mobility scenarios. Combined with daily estimated prevalence of COVID-19 in Wuhan before 11 February 2020 by Kucharski et al. [9] , we simulated the daily importations of infected travellers to Beijing, Chongqing, Hangzhou, and Shenzhen to assess the risk that they would cause sustained local transmission. We obtained daily prefecture-level human mobility data, expressed by a relative index scale, for mainland China from Baidu Huiyan for both the 2019 and 2020 travel periods surrounding the LNY, known as Chunyun. The platform aggregates mobile phone travel data from an estimated 189 million daily active users, processing > 120 billion daily positioning requests mainly through WiFi and GPS [10] . We examined the proportions of the total outflow leaving Wuhan and entering all other prefectures in China (excluding Wuhan). We then selected Beijing, Chongqing, Hangzhou, and Shenzhen for further analysis as major population centres with substantial travel with Wuhan and a wide geographic spread. We assume that the early transmission dynamics of SARS-CoV-2 in cities of this size were similar to that in Wuhan. To estimate the absolute number of daily travellers leaving Wuhan, we assumed that each unit of Baidu's
Keywords Extracted from PMC Text: UI 6–19 outflow Figure S6 Fig. 2a Figure S7 seed NPIs seed chains line people left [1] human people left Wuhan coronavirus 2 centre Shenzhen. individuals Endo Wuhan [9 Wuhan cordon sanitaire-type contact networks people travellers upper bounds Chunyun [17] Wuhan before SARS-CoV-2 Hubei province coronavirus disease 2019 " non-Chunyun Figure S1 A-F Figure S3 's Fig. 4 LNY UI 23–44 coronavirus UI 77–115 Figure S4 persons Hubei Shenzhen COVID-19 lower-traffic
Extracted PMC Text Content in Record: First 5000 Characters:Since late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has spread to over 114 countries and was declared a pandemic on 11 March 2020 [1]. Some countries have enacted cordon sanitaire-type travel restrictions, either to prevent the export of infections from an initial disease epicentre (such as Wuhan in January 2020 [2] or Northern Italy in March 2020 [3]) to other countries and regions or to prevent the import of infections from high-risk countries or regions (such as the USA's ban on travel from Europe [4]). Cordon sanitaires aim to curb the number of infected travellers entering a region with a high proportion of susceptible individuals, where they may seed additional chains of transmission. However, historically, they at best delay, rather than prevent outbreaks elsewhere [5]. Hence, the efficacy of cordon sanitaires in averting or delaying outbreaks in other locations is an open question. Chinese authorities imposed a cordon sanitaire on the city of Wuhan on 23 January 2020 [2] and extended the travel restrictions to the whole of Hubei province by 26 January 2020 [6]. The restrictions were imposed 1 day prior to the Lunar New Year (LNY) holidays and during Chunyun, the 40-day holiday travel period that marks the largest annual human migration event in the world [7]. At the same time, other public health interventions, such as physical distancing, were also enacted across China [8]. This study aims to assess the impacts of the cordon sanitaire around Wuhan, the epicentre of the COVID-19 pandemic, on reducing incidence and delaying outbreaks in other well-connected large population centres in mainland China. We used publicly available mobility data based on location-based service (LBS) provided by Baidu Huiyan, to construct four mobility scenarios. Combined with daily estimated prevalence of COVID-19 in Wuhan before 11 February 2020 by Kucharski et al. [9], we simulated the daily importations of infected travellers to Beijing, Chongqing, Hangzhou, and Shenzhen to assess the risk that they would cause sustained local transmission. We obtained daily prefecture-level human mobility data, expressed by a relative index scale, for mainland China from Baidu Huiyan for both the 2019 and 2020 travel periods surrounding the LNY, known as Chunyun. The platform aggregates mobile phone travel data from an estimated 189 million daily active users, processing > 120 billion daily positioning requests mainly through WiFi and GPS [10]. We examined the proportions of the total outflow leaving Wuhan and entering all other prefectures in China (excluding Wuhan). We then selected Beijing, Chongqing, Hangzhou, and Shenzhen for further analysis as major population centres with substantial travel with Wuhan and a wide geographic spread. We assume that the early transmission dynamics of SARS-CoV-2 in cities of this size were similar to that in Wuhan. To estimate the absolute number of daily travellers leaving Wuhan, we assumed that each unit of Baidu's migration index corresponds linearly to 50,000 travellers. This was chosen as the most credible value after synthesising evidence from several sources [8, 11–14] (see Additional file 1: Supplementary Appendix 1). We calculated the total number of daily travellers leaving Wuhan and entering each city by taking the product of the scaling factor, the total daily outflow index from Wuhan, and the daily proportion of travellers from Wuhan entering the four cities. Daily estimated COVID-19 prevalence in Wuhan was retrieved from the exposed (incubating) and infectious compartments of a published SEIR model on the early dynamics of COVID-19 transmission in Wuhan [9]. We estimated the number of daily infected arrivals in a destination city as a Poisson process governed by the daily number of travellers and prevalence in Wuhan (Additional file 1: Supplementary Appendix 2). Each day, we simulated this arrival process 100 times to capture the uncertainty in the process; this represents 7100 samples for the 71 days for each city in each scenario. We assumed that individuals would travel regardless of their infection status, and Wuhan was the sole source of infected individuals and populations within destination cities mixed homogeneously. We examined four travel scenarios (Table 1): Scenario 1 is based on the observed travel pattern in 2020 and represents the Chunyun period with cordon sanitaire introduced on 23 January. Scenario 2 represents a counterfactual travel pattern used to evaluate how the COVID-19 outbreak would spread if no cordon sanitaire was implemented. This was based upon the actual travel from Wuhan for the equivalent Chunyun period in 2019. In scenario 3, we synthesised a hypothetical travel pattern to represent a typical non-Chunyun period with cordon sanitaire introduced on 23 January, using outward travel flow on representative non-Chunyun days in 2019. Scenario 4 is a variation on scenario 3
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