Tianlong Yang1, Yao Wang2, Nankun Liu3, Guzainuer Abudurusuli1, Shiting Yang1, Shanshan Yu1, Weikang Liu1, Xuecheng Yin4, Tianmu Chen1,2. 1. School of Public Health, Xiamen University, Xiamen City, Fujian Province, China. 2. State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen City, Fujian Province, China. 3. Chinese Center for Disease Control and Prevention, Beijing, China. 4. School of Public Health, Yale University, New Haven, Connecticut, US.
Abstract
Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission. Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022.
Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission. Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022.
Entities:
Keywords:
COVID-19; containment and control measures; cross-regional transmission; transmission dynamics model
Authors: Bisong Hu; Pan Ning; Jingyu Qiu; Vincent Tao; Adam Thomas Devlin; Haiying Chen; Jinfeng Wang; Hui Lin Journal: Int J Infect Dis Date: 2021-04-13 Impact factor: 3.623
Authors: Lin T Brandal; Emily MacDonald; Lamprini Veneti; Tine Ravlo; Heidi Lange; Umaer Naseer; Siri Feruglio; Karoline Bragstad; Olav Hungnes; Liz E Ødeskaug; Frode Hagen; Kristian E Hanch-Hansen; Andreas Lind; Sara Viksmoen Watle; Arne M Taxt; Mia Johansen; Line Vold; Preben Aavitsland; Karin Nygård; Elisabeth H Madslien Journal: Euro Surveill Date: 2021-12