Statistical analysis of the community lockdown for COVID-19 pandemic

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.

Analysis of the Complex Network of the Urban Function under the Lockdown of COVID-19: Evidence from Shenzhen in China

“…The results show that lockdown measures have a positive impact on the pandemic, but countries must decide on the best timing and strategies for exiting the lockdown measures according to the capacity of their economies and health systems, in order to safely return to normal life with minimal loss of life and economic losses. Based on the agent-level pandemic simulation model, Wu et al, proposed a multivariable analysis model, including the time of implementation and unlocking, population mobility and other factors, to investigate the community lockdown for COVID-19 [25]. The results show that an early lockdown can greatly limit the spread of the disease, and once the situation can be prevented and controlled, population movements must be gradually restored.…”

Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns

“…The spreading of diseases can be modelled in a number of ways, such as deterministically with differential equations in the mean-field version of the susceptible-infected-recovered (SIR) model [9] or stochastically using methods such as agent models [10][11][12][13][14]. A modelling more realistic than the mean-field is the study of the dynamics on networks [2,[15][16][17][18][19].…”

“…In response to the SARS-CoV 2 pandemic, many governmental bodies imposed interventions to impede the spread of the disease [22]. Thus, it has become rather fashionable to study the effect of disease prevention methods [12,13,16,[23][24][25][26][27].…”

“…The idea is to greatly reduce the frequency of the contacts themselves. This can be modelled by reducing the transmission rate [23] in mean-field and stochastic models, or by restricting the motion of walkers in agent based models [12,13]. Network based models will typically model lockdowns through the removal or rewiring of edges [15][16][17][18][19].…”