Additional file 1 of Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models
Creators: name: Rui, Jia
name: Zheng, Jin-Xin
name: Chen, Jin
name: Wei, Hongjie
name: Yu, Shanshan
name: Zhao, Zeyu
name: Wang, Xin-Yi
name: Chen, Mu-Xin
name: Xia, Shang
name: Zhou, Ying
name: Chen, Tianmu
name: Zhou, Xiao-Nong
Publisher: name: figshare
Year: 2022
Subjects: Medicine
Science Policy
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer
Computational Biology

description: Additional file 1: Table S1. COVID-19 daily reported cases and response policies by local government. Table S2. Variable definitions and parameter values. Table S3. PHMS-Index calculation. Table S4. Intervention simulation corresponding parameters. Table S5. Parameter estimation of actual spillover rate and spillover threshold. Table S6. Rt and PHMS-Index values at different stages. Table S7. Mean, standard deviation, Person correlation coefficient of Rt and PHSM_Index. Table S8. Simulation of different mask wear rates in 4 cities. Table S9. Simulation of different social distances in 4 cities. Table S10. Simulation of different social distances and mask wear rates in 4 cities. Table S11. Simulation of different isolation ratios in 4 cities. Table S12. Simulation of different combinations of mask wear, social distance rates and isolation rates in 4 cities. Table S13. Spillover risk values at different stages. Fig. S1. Biological invasion theory framework. (a: Date of reporting of the first case; b: The time when the third-generation cases appeared, that is, a + 6 days; c, d: According to the PTA methods, the case time series data of points 0-H are calculated in segments.). Fig. S2. SEIAR model with booster vaccination intervention. Fig. S3. The stock flow diagram of the system dynamic model for COVID-19. (The intervention strategies are represented by causal variables shown in red color. Other causal variables, are shown in parameter tables.). Fig. S4. Geographical distribution of the study area and importation of cases into other Chinese cities from study areas (Study areas include Shanghai, Shenzhen, Nanjing, and Suzhou. The red line shows the direction of spillover.). Fig. S5. Five stages of the epidemic curve based on the theory of biological invasion in Shanghai (Spread: Landscape spread). Fig. S6. Five stages of the epidemic curve based on the theory of biological invasion in Shenzhen (Spread: Landscape spread). Fig. S7. Five stages of the epidemic curve based on the theory of biological invasion in Nanjing (Spread: Landscape spread). Fig. S8. Five stages of the epidemic curve based on the theory of biological invasion in Suzhou (spread: landscape spread). Fig. S9. Simulation of different mask wear rates in 4 cities (a total of 10 mask wear rates were simulated, ranging from 90 to 0%.). Fig. S10. Simulation of different social distances in 4 cities (a total of 10 social distances were simulated, ranging from 90 to 0%.). Fig. S11. Simulation of different isolation ratios in 4 cities (a total of 26 segregation ratios were modelled, ranging from 80 to 10%.). Fig. S12. Simulation of different combinations of mask wear and social distance rates in 4 cities (a total of 56 possible real-world combinations of masks + social distance measures are simulated.). Fig. S13. Simulation of comprehensive interventions in 4 cities (a total of 100 possible real- world integrated interventions are simulated.). Fig. S14. Map of spillover city top 30 ranking of each city.