One of the most widely implemented policy response to the novel coronavirus (SARS-CoV-2) pandemic has been the imposition of restrictions on mobility (1). These restrictions have included both incentives, encouraging working from home, supported by a wide range of online activities such as meetings, lessons, and shopping, and sanctions, such as stay at home orders, restrictions on travel, and closure of shops, offices, and public transport (2-5). The measures constitute a major component of efforts to control the COVID-19 pandemic. Compared to previous epidemic responses, they are unprecedented in both scale and scope (6).
The rationale underpinning these public health measures is that restricting normal activities decreases the number, duration, and proximity of interpersonal contacts and thus the potential for viral transmission. Transmission simulations using complex mathematical modelling have built on past experience such as the 1918 influenza epidemic (7), as well as assumptions about the contemporary scale and nature of contact in populations (8). However, the initial models were not always founded on empirical evidence from behavioral scientists on the feasibility or sustainability of mass social and behavior change in contemporary society. While reductions in interpersonal contact and increases in physical distancing are known to decrease respiratory infection spread (9), the paucity of recent examples of large-scale restrictions on mobility has limited the scope for research on their impact on transmission. Where restrictions have been imposed, as with Ebola, they have involved diseases with a different mode of transmission. Nonetheless, the rapidity of progression of this pandemic has forced many governments into trialing various approaches to containment with limited evidence of effectiveness (10).
More conventional public health prevention measures (such as quarantine of contacts, isolation of infected individuals and contact tracing) and control measures in health systems (such as patient flow segregation, negative pressure ventilation, and use of personal protective equipment) (11-14), have been applied widely to control the epidemic in many countries as part of a portfolio of policy responses. However, mobility restriction as a new large-scale mass behavioral and social prescription has incurred considerable costs (15, 16). Estimates suggest global GDP growth has fallen by as much as 10% (17), at least in part due to mobility restriction policies. Although views differ, not least because of the lack of information of what would happen if the disease was unchecked and the emerging evidence of persisting disability in survivors, some have argued that this is greater than would be accounted for by the economic impact of direct illness and deaths from COVID-19 (18, 19).
To inform decisions on large scale restrictions of mobility, there is an urgent need to assess their effectiveness in limiting pandemic spread. To this end, we examined the association of mobility with COVID-19 incidence in Organization of Economic Cooperation and Development (OECD) countries and equivalent economies such as Singapore and Taiwan.
Scholarly Commons Citation
Oh, Juhwan; Lee, Hwa-Young; Long, Khuong Quynh; Markuns, Jeffrey F.; Bullen, Chris; Artaza Barrios, Osvaldo Enrique; Hwang, Seung-sik; Seo, Young Sahng; McCool, Judith; Kachur, S. Patrick; Chan, Chang-Chung; Kwon, Soonman; Kondo, Naoki; Minh, Hoang Van; Moon, J. Robin; Rostila, Mikael; Norheim, Ole F.; You, Myoungsoon; Withers, Mellissa; Lil, Mu; Lee, Eun-Jeung; Benski, Caroline; Park, Soo Kyung; Nam, Eun-Woo; Gottschalk, Katie; Kavanagh, Matthew M.; Lee, Jong-Koo; McKee, Martin; Subramanian, S. V.; and Gostin, Lawrence O., "How Well Does Societal Mobility Restriction Help Control the COVID-19 Pandemic? Evidence from Real-Time Evaluation" (2020). Georgetown Law Faculty Publications and Other Works. 2354.