Social distancing to slow the U.S. COVID-19 epidemic: longitudinal pretest-posttest comparison group study

BackgroundSocial distancing measures to address the U.S. coronavirus disease 2019 (COVID-19) epidemic may have notable health and social impacts.Methods and FindingsWe conducted a longitudinal pretest-posttest comparison group study to estimate the change in COVID-19 case growth before versus after implementation of statewide social distancing measures in the U.S. The primary exposure was time before (14 days prior to, and up to 3 days after) versus after (beginning 4 days after, and up to 21 days after) implementation of the first statewide social distancing measures. Statewide restrictions on internal movement were examined as a secondary exposure. The primary outcome was the COVID-19 case growth rate. The secondary outcome was the COVID-19-attributed mortality growth rate. All states initiated social distancing measures between March 10-25, 2020. The mean daily COVID-19 case growth rate decreased beginning four days after implementation of the first statewide social distancing measures, by 0.9% per day (95% confidence interval [CI], −1.3% to −0.4%; P<0.001). We did not estimate a statistically significant difference in the mean daily case growth rate before versus after implementation of statewide restrictions on internal movement (0.1% per day; 95% CI, −0.04% to 0.3%, P=0.14), but there is significant difficulty in disentangling the unique associations with statewide restrictions on internal movement from the unique associations with the first social distancing measures. Beginning seven days after social distancing, the COVID-19-attributed mortality growth rate decreased by 1.7% per day (95% CI, −3.0% to −0.7%; P<0.001). Our analysis is susceptible to potential bias resulting from the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states.ConclusionsStatewide social distancing measures were associated with a decrease in the COVID-19 epidemic case growth rate that was statistically significant and a decrease in the COVID-19-attributed mortality growth rate that was not statistically significant.Author SummaryWhy was the study doneThere are few empirical data about the population health benefits of imposing statewide social distancing measures to reduce transmission of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19).What did the researchers findWe compared data from each state before vs. after implementation of statewide social distancing measures to estimate changes in mean COVID-19 daily case growth rates. Growth rates declined by approximately 1% per day beginning four days (approximately one incubation period) after statewide social distancing measures were implemented. Stated differently, our model implies that social distancing reduced the total number of COVID-19 cases by approximately 1,600 reported cases at 7 days after implementation, by approximately reported 55,000 cases at 14 days after implementation, and by approximately reported 600,000 cases at 21 days after implementation.What do these findings meanStatewide social distancing measures were associated with a reduction in the growth rate of COVID-19 cases in the U.S. However, our analysis is susceptible to potential bias resulting from the aggregate nature of the data, potential confounding by other changes that occurred during the study period (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states.

Petrographic and Chemical–Mineralogical Characterization of Mortars from the Cistern at Amaiur Castle (Navarre, Spain)

Mortars from the cistern in Amaiur Castle (Navarre, Spain) were analysed to assess the mortar manufacturing process and application techniques. To this end, optical microscopy, scanning electron microscopy (SEM), thermogravimetric analysis, X-ray fluorescence (XRF), X-ray diffraction (XRD), Raman microspectroscopy, and Fourier transform infrared spectroscopy (FTIR) were performed. The study of both structural and plaster mortars from Amaiur Castle cistern show patterns/rules in the mortar manufacture according to the specific construction requirements. A multilayer application technique was used for the construction of the cistern tank. Deliberate selection of the aggregate nature and grading contributed to mortar impermeability. Ceramic and silico-aluminous rock fragments were used as aggregates in the cistern tank to confer hydraulicity to the mortars, instead of carbonated aggregates as used in the vault. Aluminosilicated phases present in the aggregates led to the formation of amesite, which is a magnesium aluminosilicate hydrate (M-A-S-H) phase conferring hydraulicity to the mortar. Two types of additives were identified in the outer pigmented layer of plaster. Beeswax was the identified organic additive used to improve the impermeability of mortar, while hematite was the identified inorganic additive giving rise to the reddish colour of the layer.

Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Impacts of Model Resolution on Transportation Network Criticality Rankings

Objective rankings of the criticality of transportation network infrastructure are essential for efficiently allocating limited adaptation resources and must account for network connectivity and travel demand. Road link criticality can be quantified by the total travel delay caused when the capacity of a road segment or link is disrupted or removed. These methods can use standard travel demand models, but the exclusion of lower-volume roads and the aggregate nature of traffic analysis zones may distort resulting criticality rankings. To test the impact of link exclusion and demand aggregation, the authors applied the network robustness index, a well-established link criticality measure, to a hypothetical network with varying levels of network resolution and demand aggregation. The results show a statistically significant change in criticality rankings when demand is aggregated and especially when links are excluded from the network, suggesting that criticality rankings may be distorted when estimated with typical demand models. Application to a road network in Vermont supports the finding on the impact of network resolution on criticality rankings.

Leadership Systems Model: An Integration Of People, Process, And Behaviors In A Dynamic And Evolving Environment

Defining, observing, and measuring leadership skills, styles and approaches are far from being a new effort. Although research has provided much information regarding leadership, the classical leadership theories and models, processes, and behavioral views must be further integrated in order to provide a richer and wider view of leadership. Furthermore, organizations are not static; they are constantly changing and evolving over time. The purpose of this paper is to provide a conceptual model integrating these views using a systems level theory to understand the aggregate nature of leadership.

Changes in the Interaction between Tropical Convection, Radiation, and the Large-Scale Circulation in a Warming Environment

This paper explores the response of the tropical hydrologic cycle to surface warming through the lens of large-domain cloud-system-resolving model experiments run in a radiative–convective equilibrium framework. Simulations are run for 55 days and are driven with fixed insolation and constant sea surface temparatures (SSTs) of 298 K, 300 K, and 302 K. In each experiment, convection organizes into coherent regions of large-scale ascent separated by areas with relatively clear air and troposphere-deep descent. Aspects of the simulations correspond to observed features of the tropical climate system, including the transition to large precipitation rates above a critical value of total column water vapor, and an increase in convective intensity with SST amidst weakening of the large-scale overturning circulation. However, the authors also find notable changes to the interaction between convection and the environment as the surface warms. In particular, organized convection in simulations with SSTs of 298 and 300 K is inhibited by the presence of a strong midtropospheric stable layer and dry upper troposphere. As a result, there is a decrease in the vigor of deep convection and an increase in stratiform precipitation fraction with an increase in SST from 298 to 300 K. With an increase in SST to 302 K, moistening of the middletroposphere and increase in lower-tropospheric buoyancy serve to overcome these limitations, leading to an overall increase in convective intensity and larger increase in upper-tropospheric relative humidity. The authors conclude that, while convective intensity increases with SST, the aggregate nature of deep convection is strongly affected by the details of the thermodynamic environment in which it develops. In particular, the positive feedback between increasing SST and a moistening upper troposphere found in the simulations, operates as a nonmonotonic function of SST and is modulated by a complex interaction between deep convection and the environmental relative humidity and static stability profile. The results suggest that projected changes in convection that assume a monotonic dependence on SST may constitute an oversimplification.