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Register a new userThe effect of stay-at-home orders on COVID-19 cases and fatalities in the United States
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Governments issue stay at home orders to reduce the spread of contagious diseases, but the magnitude of such orders effectiveness is uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies effect with greater accuracy. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States. We estimate the effect of stay-at-home orders using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) reduction in weekly cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities where they were implemented.
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Since December 2019, the world has been witnessing the gigantic effect of an unprecedented global pandemic called Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) - COVID-19. So far, 38,619,674 confirmed cases and 1,093,522 confirmed deaths due to COVID-19 have been reported. In the United States (US), the cases and deaths are recorded as 7,833,851 and 215,199. Several timely researches have discussed the local and global effects of the confounding factors on COVID-19 casualties in the US. However, most of these studies considered little about the time varying associations between and among these factors, which are crucial for understanding the outbreak of the present pandemic. Therefore, this study adopts various relevant approaches, including local and global spatial regression models and machine learning to explore the causal effects of the confounding factors on COVID-19 counts in the contiguous US. Totally five spatial regression models, spatial lag model (SLM), ordinary least square (OLS), spatial error model (SEM), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR), are performed at the county scale to take into account the scale effects on modelling. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain the maximum model variances. For COVID-19 deaths, both (domestic and international) migration and income factors play a crucial role in explaining spatial differences of COVID-19 death counts across counties. The local coefficient of determination (R2) values derived from the GWR and MGWR models are found very high over the Wisconsin-Indiana-Michigan (the Great Lake) region, as well as several parts of Texas, California, Mississippi and Arkansas.
Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections with 15 other facilities. Controlling for demographic and other factors, a homes staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Results suggest that eliminating staff linkages between nursing homes could reduce COVID-19 infections in nursing homes by 44 percent.
In the first half of 2020, several countries have responded to the challenges posed by the Covid-19 pandemic by restricting their export of medical supplies. Such measures are meant to increase the domestic availability of critical goods, and are commonly used in times of crisis. Yet, not much is known about their impact, especially on countries imposing them. Here we show that export bans are, by and large, counterproductive. Using a model of shock diffusion through the network of international trade, we simulate the impact of restrictions under different scenarios. We observe that while they would be beneficial to a country implementing them in isolation, their generalized use makes most countries worse off relative to a no-ban scenario. As a corollary, we estimate that prices increase in many countries imposing the restrictions. We also find that the cost of restraining from export bans is small, even when others continue to implement them. Finally, we document a change in countries position within the international trade network, suggesting that export bans have geopolitical implications.
We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academics research productivity in social science. The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Our results indicate that, in the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35%, female academics productivity dropped by 13.9% relative to that of male academics. We also show that several disciplines drive such gender inequality. Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries. Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.
The COVID-19 pandemic has disrupted human activities, leading to unprecedented decreases in both global energy demand and GHG emissions. Yet a little known that there is also a low carbon shift of the global energy system in 2020. Here, using the near-real-time data on energy-related GHG emissions from 30 countries (about 70% of global power generation), we show that the pandemic caused an unprecedented de-carbonization of global power system, representing by a dramatic decrease in the carbon intensity of power sector that reached a historical low of 414.9 tCO2eq/GWh in 2020. Moreover, the share of energy derived from renewable and low-carbon sources (nuclear, hydro-energy, wind, solar, geothermal, and biomass) exceeded that from coal and oil for the first time in history in May of 2020. The decrease in global net energy demand (-1.3% in the first half of 2020 relative to the average of the period in 2016-2019) masks a large down-regulation of fossil-fuel-burning power plants supply (-6.1%) coincident with a surge of low-carbon sources (+6.2%). Concomitant changes in the diurnal cycle of electricity demand also favored low-carbon generators, including a flattening of the morning ramp, a lower midday peak, and delays in both the morning and midday load peaks in most countries. However, emission intensities in the power sector have since rebounded in many countries, and a key question for climate mitigation is thus to what extent countries can achieve and maintain lower, pandemic-level carbon intensities of electricity as part of a green recovery.
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