Impact of policy interventions and social distancing on SARS-CoV-2 transmission in the United States

Abstract

Background: Policymakers have employed various non-pharmaceutical interventions (NPIs)such as stay-at-home orders and school closures to limit the spread of Coronavirus disease . However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states. Methods:In order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables. Results:States that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002).Discussion: Few studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics.

Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes

“…The timeseries show that the epidemic curve is flattened and delayed as closure becomes more efficacious, which reduces peak demand for intensive care beds and buys time for developing pharmaceutical interventions like vaccines and antiviral drugs, improving testing capacity, and establishing novel approaches to patient care. For the remainder of our analysis, to determine C 0 it was assumed that C 0 should be large enough to bring the effective reproduction number R eff below 1, reflecting the observed success in multiple jurisdictions where physical distancing and closure have maintained R eff < 1 [6][7][8]. Hence we chose C 0 = 1 − 1/R 0 based on the elimination threshold for the SEIR model [30].…”

“…Mathematical models of SARS-CoV-2 transmission and control show that physical distancing can mitigate the pandemic [2][3][4][5] and this has subsequently been backed up by empirical analyses of case notification data. These analyses show how mitigation measures have reduced the effective reproduction number of SARS-CoV-2 below one, meaning that each infected case infects less than one person on average [6][7][8]. However, the population's willingness to support school and workplace closures could wane over time, as the economic costs of closure accumulate [9].…”

“…The interactions between disease dynamics and behavioral dynamics in COVID-19 are emphasized by research showing that the perceived risk of SARS-CoV-2 infection is a predictor of adherence to physical distancing measures [20] and moreover that individuals respond to the presence of COVID-19 cases in their population by increasing their physical distancing efforts [21]. In turn, physical distancing has been shown to reduce the number of cases [6], completing the loop of coupled behavior-disease dynamics. Some models have already begun exploring this interaction between disease dynamics and individual behavior and/or public health policy decision-making for COVID-19 [5,16,[22][23][24][25][26].…”

Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries

“…The need now is to retrospectively assess the true impact of NPIs on COVID-related morbidity and mortality, and to plan their withdrawal using empirical evidence. However, little work to date has conducted retrospective analyses of the possible mitigating effects of NPIs on the COVID death toll [11][12][13][14][15][16] . In this paper, we use an up-to-date time series of COVID-related mortality data from 13 comparable Western European countries.…”

Impacts of people’s learning behavior in fighting the COVID-19 epidemic

“…Many flu-based epidemic models [6][7][8][9][10][11][12][13][14][15] have been used to model the current pandemic, but because they do not take social distancing into account, these models have missed predicting the rate of spread and peak time of new infections. Recent studies [16][17][18][19] have attempted to address this issue. In this letter, we apply a novel epidemic mathematical model recently developed by Cheng and Wang [5] to quantify the impacts of social distancing and people's learning behavior (isolating, wearing face masks, washing hands, avoiding gathering in groups, etc.).…”