Abstract
As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.
Highlights
Since January 2020, the Lancet have published many papers and reviews that have revealed the high mortality rate and person-to-person transmission characteristics of the new coronavirus infection [6, 14]
As the global pandemic of COVID-19 is still happening around the world, it is vital to prevent the spread of the epidemic by implementing control measures [29]
When T0 is 15 or 20, we find that results do not correlate with the value of T1, which are highly similar to each other. This means that once the lockdown is later than a critical time, the spread of the COVID-19 is almost unaffected by the time when the lockdown is lifted
Summary
The process of epidemic spreading is abstracted as the population movements between different warehouses. Logistic Growth Models were originally used to simulate population growth in a limited environment. They were subsequently applied in the simulation of infectious disease epidemic dynamics [20]. Network models were used to study the law of the spread of an epidemic in a population [22]. This paper studies real data of COVID-19 and proposes a model based on the ALPS to simulate the spread of the epidemic in the community. The paper ends by summarizing the advantages of the proposed model and suggesting improvements in the future
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