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

Talal Daghriri,Ozlem Ozmen

doi:10.3390/ijerph18115566

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%.

Highlights

In 2020, the novel coronavirus (COVID-19) has spread across the globe, taking lives and bringing devastating consequences
Our study aims to quantify the effects of different levels of social distancing on the disease spread based on the transmission and fatality rates of a specific location
The first experiment was run under a 0% level of social distancing (Figure 4)

Summary

Introduction

In 2020, the novel coronavirus (COVID-19) has spread across the globe, taking lives and bringing devastating consequences. The pandemic is the most severe global health crisis in recent history since the 1918 influenza pandemic. 50 million people died, and one-third of the world’s population was affected [1]. The extent of the coronavirus pandemic is yet to be seen as cases continue to rise exponentially. At the time of this study, there are at least three COVID-19 vaccines available. The distribution and administration to fully vaccinate the global population, is expected to take some time. Using nonpharmacological strategies remains essential to reduce the spread of the disease and protect public health. Countries worldwide are implementing various social distancing policies, an important factor in reducing contact between people [2]

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