Quantifying the Effects of Social Distancing on the Spread of COVID-19

Abstract

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

Keywords: COVID-19; agent-based modeling; disease spread; social distancing.

Figure 1

ODD Design protocol [12].

Figure 2

ODD+D protocol [12]. Plus sign (+) indicates numbers of added new questions (compared to the previous ODD).

Figure 3

Process overview and scheduling.

Figure 4

Sliders and switches of the NetLogo software interface.

Figure 5

The Netlogo model interface [16].

Figure 6

The initial visualization of the experiment.

Figure 7

First experiment results of 0% social distancing.

Figure 8

Final visualization of agents at 0% social distancing.

Figure 9

Movement patterns in the first experiment at 0% social distancing.

Figure 10

Infected, recovered, deceased, and survival population rate at 0% social distancing.

Figure 11

Infected versus the recovered people curve, when the fatality rate is 3% with 0% social distancing.

Figure 12

Movement patterns in the second experiment with 30% social distancing.

Figure 13

Infected versus the recovered people curve, when the fatality rate is 3% with 30% social distancing.

Figure 14

Infected versus the recovered people curve, when the fatality rate is 3% with 40% social distancing.

Figure 15

The final visual outputs of applying different levels of social distancing (without tracing individuals’ movement).

Figure 16

The final visual outputs of applying different levels of social distancing (with tracing individuals’ movement).

Figure 17

Effect of social distancing on infection rates.

Figure 18

Number of deaths versus percentage of social distancing.

Figure 19

Recovery rate (recovered/total population) versus percentage of social distancing.

Figure 20

Survival rate versus percentage of social distancing.