Assessing the Impact of Social Distancing During COVID-19

by Christopher P. Morley, PhD

In the late Winter and Spring of 2020, the novel coronavirus, SARS-COV-2, which causes Coronavirus Disease 2019 (COVID-19), began to appear on both coasts of the United States, beginning in Seattle in February, and in New York State and other eastern cities in March. Genomic analyses revealed a two-pronged viral assault, with cases in the western US originating in Asia, and with the eastern seaboard impacted by strains of the virus that arrived via Europe. As a product of a new viral species, previously unknown before late 2019, little was known about how to prevent or treat COVID-19.

What was apparent in the initial outbreaks was that the virus was contagious. Viral transmission is often expressed as an R value, or reproduction rate, which is an indicator of the average number of new cases of an infectious disease are generated by an infected individual. An R of 1 indicates that an infected individual will transmit the virus to 1 new person. R values higher than 1 indicate an expanding epidemic; and conversely, an R value below 1 suggests that cases are infecting an average of less than one other person, and that an epidemic is subsiding.

The initial R value (R0, or “R nought”) is often characterized by aspects of the infectious agent itself, such as its modes of transmission (for example, airborne vs. bloodborne). As social systems react to the presence of the infectious agent, the reproduction rate (or R value) can change, in response to measures such as social distancing, mask use, or immunity within the population. The reproduction value at any given time – R(t) – is therefore a movable number that is reflective both of the pathogen’s characteristics, as well as the effectiveness of interventions.

Very early estimates of R0 for the SARS-COV-2 virus was quite high in the cities that saw the first outbreaks, estimated at 5.8 in Wuhan, China, where the virus was first identified, and at 2.43-3.10 in northern Italy. For comparison, the R0 of the H1N1 flu in 2009 was estimated to be between 1.2 – 1.6. Faced with a rapidly expanding pandemic of a virus, by March 2020, many parts of the United States, including New York State, were issuing shut-down orders, limiting public movement to essential services (primarily food and medicine). At the time, several basic questions about prevention had no answer, such as the effectiveness of masks, or the length of time the virus could live on surfaces or in the air. Faced with uncertainty, enforced “social distancing” became the rule of the day, as an emergency measure when few other options existed.

As might be expected, many questions arose about the basic need for a societal shut-down, as well as about its comprehensiveness and duration. Faced with drastic measures in an already politically fractious context, it became apparent that the need existed to rapidly assess whether extreme measures were working. Fortunately, a number of elements came together to allow our team to do so rapidly. First, Unacast, a company that specializes in using mobile telephone data to assess human mobility, created publicly available measures of social distancing. These measures represented encounters between individuals (or more specifically, their cell phones), non-essential visitation, and average distance traveled, as well as an average overall score, for US counties. The measures were calculated as changes since a baseline date before New York State shut down. Secondly, as a function of our hospital’s Incident Command structure, members of our team were calculating local R(t) rates for the counties in our region. Using these data, we observed that each social distancing metric was statistically associated with the average R(t) for the week that followed. In short, the more people adhered to social distancing practices, the reproduction rate of the virus for the following week dropped (and conversely, poorer adherence led to higher R(t) estimates).

Surely, other factors helped limit the epidemic in our region, where R(t) did not exceed 2.o for any sustained period. First, all of New York State shut down at the same time, in response to an obvious and catastrophic outbreak in the greater New York City area. As the entire state shut down in response to that geographically more limited epidemic, the areas where the virus had not yet taken a deep hold appear to have been spared for a time. Additionally, much of New York State is less densely populated that New York City and its suburbs. While any number of factors may have contributed to the stemming of the epidemic in our region in the spring of 2020, the rapid implementation of social distancing measures appears to have had an impact.

Since the time period of our study, New York State has begun a gradual, cautious, phased reopening of businesses and activities, and so far (as of mid-July), R values across the state have remained close to or below 1. If imposed, broad social distancing measures were effective, why is the reversal of these measures not immediately leading to disaster? While this remains a question open to study and speculation, there are some likely reasons. First, masking has been comparatively embraced as a protective measure across New York State, and businesses have been slow to open. Most restaurants are open for take-out and outdoor dining only (or with limited seating), retail businesses are opening with strict crowd size limits and universal masking requirements, and large events have been largely banned. In general, the political climate has been favorable to the measures that are known to work, including masking and maintaining physical distance.

However, the story has not been universally the same. Across much of the southern and western United States, individual states are seeing new spikes in COVID-19 cases as they rapidly re-open. Additionally, the economic and social damages created by broad shut-downs have led to “quarantine fatigue,” and the US has become sharply divided, with the decision to wear or not wear a mask now serving as a symbol of partisan political membership. Internationally, other areas that were previously held up as models of viral control are now seeing new spikes in cases, such as Kerala state in India. The virus is not gone.

Broad shut-downs to enforce social distancing may be viewed as a blunt instrument; as we have learned more about SARS-COV-2, we understand that masking, hand hygiene, and physical distance are key practices that individuals should adopt to prevent infection. This along with continued improvement of testing and contact tracing regimens, new knowledge of treatment regimens, and potentially, a vaccine in the future, may serve as precision tools that can help avoid the use of such a blunt instrument as the shut down of society. Unfortunately, much uncertainty still exists, and it is yet unclear if humans can maintain long-term immunity to the virus after infection. Coupled with the social divisions and questionable leadership of the present, new closures may be necessary. Fortunately, they are probably an effective blunt tool. If the need arises, it is urgent for governments at all levels to prepare for mitigation of both the economic and social consequences of new shut-downs, because lives will be lost until broad testing, contact tracing, and widespread vaccination are available.

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Christopher P. Morley, PhD

Christopher P. Morley, PhD, is a medical social scientist, and Chair of the Department of Public Health & Preventive Medicine at SUNY Upstate Medical University, where he also serves as Vice Chair for Research in the Department of Family Medicine. Dr. Morley’s principle research interests are in health disparities, general population health & the medical social sciences, medical education and health workforce development,  behavioral health in primary care settings, ethics, and primary care practice improvement. The department he leads was substantially engaged in Upstate’s Incident Command, and in the regional response to the COVID-19 Pandemic.