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The transmission rate of COVID-19 pandemic under different mobility control in Europe

  • 1.

    School of Data Science, University of Science and Technology of China, Hefei 230026, China

  • 2.

    School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUST-2021-0146
  • Received Date: 04 June 2021
  • Rev Recd Date: 18 August 2021
  • Publish Date: 30 September 2021
    Abstract

  • Abstract

    COVID-19 pandemic captured the full attention of the world in 2020, and the government declared a series of non-pharmacological interventions (NPIs) to curb the influence of social movement on transmission. In different countries, different policies bring about different results. Quantifying the effect of the movement becomes a vital issue for evaluating the effectiveness of these actions. The transmission rate changes and is hard to computer after altering activity. Therefore, this research sets some European countries as the research objects, collects mobility data and daily cases during some periods, and proposes a mobility-susceptible-exposure-infectious-recovery (M-SEIR) model. Unlike the SEIR model, the movement change is quantified as a variable (σ(t)) and added in the M-SEIR model. With random sampling to get the number of people in different initial states, this research iterates the model. The iterative filtering ensemble adjustment Kalman filter (IF-EAKF) is used to adjust the subsequent iterative results. In the research, it receives the changing trend of parameters and the daily new estimation in the end. Set the first round as the fitting period and repeat the experiment 100 times in the fitting part. The result confirms the feasibility and robustness of the model. In addition, this study makes a reasonable forecast for European countries about the second round. By controlling the strength and the time point of applying non-pharmacological interventions, the research predicts the impact of these actions on the pandemic and provides some suggestions for the deployment of relevant policies in the future. Finally the study eliminates the external factors such as motion and temperature, and obtains an interesting discovery: Despite the daily case in the third round higher than that in the first round, the transmission parameter in the former appears lower than that in the latter. The further survey shows that it might be related to vaccination.

    Abstract

    COVID-19 pandemic captured the full attention of the world in 2020, and the government declared a series of non-pharmacological interventions (NPIs) to curb the influence of social movement on transmission. In different countries, different policies bring about different results. Quantifying the effect of the movement becomes a vital issue for evaluating the effectiveness of these actions. The transmission rate changes and is hard to computer after altering activity. Therefore, this research sets some European countries as the research objects, collects mobility data and daily cases during some periods, and proposes a mobility-susceptible-exposure-infectious-recovery (M-SEIR) model. Unlike the SEIR model, the movement change is quantified as a variable (σ(t)) and added in the M-SEIR model. With random sampling to get the number of people in different initial states, this research iterates the model. The iterative filtering ensemble adjustment Kalman filter (IF-EAKF) is used to adjust the subsequent iterative results. In the research, it receives the changing trend of parameters and the daily new estimation in the end. Set the first round as the fitting period and repeat the experiment 100 times in the fitting part. The result confirms the feasibility and robustness of the model. In addition, this study makes a reasonable forecast for European countries about the second round. By controlling the strength and the time point of applying non-pharmacological interventions, the research predicts the impact of these actions on the pandemic and provides some suggestions for the deployment of relevant policies in the future. Finally the study eliminates the external factors such as motion and temperature, and obtains an interesting discovery: Despite the daily case in the third round higher than that in the first round, the transmission parameter in the former appears lower than that in the latter. The further survey shows that it might be related to vaccination.
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    • References(16)
  • [1]
    Tian H, Liu Y, Li Y, et al. An investigation of transmission control measures during thefirst 50 days of the COVID-19 pandemic in China. Science,2020, 368(6491): 638-642.
    [2]
    Chang S, Pierson E, Pang W K, et al. Mobility network models of COVID-19 explaininequities and inform reopening. Nature, 2020, 589(7840): 82-87.
    [3]
    Kuhbandner C, Homburg S. Commentary: Estimating the effects of non-pharmaceuticalinterventions on COVID-19 in Europe. Frontiers in Medicine, 2020, 7: 580361.
    [4]
    Desson Z, Lambertz L, Peters J W, et al. Europe’s Covid-19 outliers: German,Austrian and Swiss policy responses during the early stages of the 2020 pandemic. HealthPolicy and Technology, 2020, 9(4):405-418.
    [5]
    Kiesha P, Cook A R, Mark J, et al. Projecting social contact matrices in 152 countriesusing contact surveys and demographic data. PLoS Computational Biology, 2017, 13(9):e1005697.
    [6]
    Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant tothe spread of infectious diseases. PLoS Medicine, 2008, 5(3):e74.
    [7]
    Glass L M, Glass R J. Social contact networks for the spread of pandemic influenzain children and teenagers. BMC Public Health, 2008, 8: Article number 61.
    [8]
    Boehmer T K, Devies J, Caruso E, et al. Changing age distribution of the COVID-19 pandemic: United States, May-August 2020. Morbidity and MortalityWeekly Report (MMWR), 2020, 69(39): 1404-1409.
    [9]
    Levin A T, Hanage W P, Owusu-Boaitey N, et al. Assessing the age specificity ofinfection fatality rates for COVID-19: Systematic review, meta-analysis, and public policyimplications. European Journal of Epidemiology, 2020, 35(12): 1123-1138.
    [10]
    Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches.Hoboken, NJ: Wiley-Interscience, 2006.
    [11]
    Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapiddissemination of novel coronavirus (SARS-CoV-2). Science, 2020, 368(6490): 489-493.
    [12]
    Ionides E L, Breto C, King A A. Inference for nonlinear dynamical systems.Proceedings of the National Academy of Sciences of the United States of America, 2006,103(49): 18438-18443.
    [13]
    Anderson J L. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 2001, 129(12): 2884-2903.
    [14]
    Qi H, Xiao S, Shi R, et al. COVID-19 transmission in Mainland China is associated withtemperature and humidity: A time-series analysis. Science of the Total Environment,2020, 728: 138778.
    [15]
    Kodera S, Rashed E A, Hirata A. Correlation between COVID-19 morbidity andmortality rates in Japan and local population density, temperature and absolute humidity. International Journal of Environmental Research and Public Health, 2020, 17(15): 5477.
    [16]
    Kissler S M, Tedijanto C, Goldstein E, et al. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, 2020, 368(6493): 860-868.
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    [1]
    Tian H, Liu Y, Li Y, et al. An investigation of transmission control measures during thefirst 50 days of the COVID-19 pandemic in China. Science,2020, 368(6491): 638-642.
    [2]
    Chang S, Pierson E, Pang W K, et al. Mobility network models of COVID-19 explaininequities and inform reopening. Nature, 2020, 589(7840): 82-87.
    [3]
    Kuhbandner C, Homburg S. Commentary: Estimating the effects of non-pharmaceuticalinterventions on COVID-19 in Europe. Frontiers in Medicine, 2020, 7: 580361.
    [4]
    Desson Z, Lambertz L, Peters J W, et al. Europe’s Covid-19 outliers: German,Austrian and Swiss policy responses during the early stages of the 2020 pandemic. HealthPolicy and Technology, 2020, 9(4):405-418.
    [5]
    Kiesha P, Cook A R, Mark J, et al. Projecting social contact matrices in 152 countriesusing contact surveys and demographic data. PLoS Computational Biology, 2017, 13(9):e1005697.
    [6]
    Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant tothe spread of infectious diseases. PLoS Medicine, 2008, 5(3):e74.
    [7]
    Glass L M, Glass R J. Social contact networks for the spread of pandemic influenzain children and teenagers. BMC Public Health, 2008, 8: Article number 61.
    [8]
    Boehmer T K, Devies J, Caruso E, et al. Changing age distribution of the COVID-19 pandemic: United States, May-August 2020. Morbidity and MortalityWeekly Report (MMWR), 2020, 69(39): 1404-1409.
    [9]
    Levin A T, Hanage W P, Owusu-Boaitey N, et al. Assessing the age specificity ofinfection fatality rates for COVID-19: Systematic review, meta-analysis, and public policyimplications. European Journal of Epidemiology, 2020, 35(12): 1123-1138.
    [10]
    Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches.Hoboken, NJ: Wiley-Interscience, 2006.
    [11]
    Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapiddissemination of novel coronavirus (SARS-CoV-2). Science, 2020, 368(6490): 489-493.
    [12]
    Ionides E L, Breto C, King A A. Inference for nonlinear dynamical systems.Proceedings of the National Academy of Sciences of the United States of America, 2006,103(49): 18438-18443.
    [13]
    Anderson J L. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 2001, 129(12): 2884-2903.
    [14]
    Qi H, Xiao S, Shi R, et al. COVID-19 transmission in Mainland China is associated withtemperature and humidity: A time-series analysis. Science of the Total Environment,2020, 728: 138778.
    [15]
    Kodera S, Rashed E A, Hirata A. Correlation between COVID-19 morbidity andmortality rates in Japan and local population density, temperature and absolute humidity. International Journal of Environmental Research and Public Health, 2020, 17(15): 5477.
    [16]
    Kissler S M, Tedijanto C, Goldstein E, et al. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, 2020, 368(6493): 860-868.
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