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Background Since COVID-19 first appeared in the United States (US) in January 2020, US states have pursued a wide range of policies to mitigate the spread of the virus and its economic ramifications. Without unified federal guidance, states have been the front lines of the policy response. Main text We created the COVID-19 US State Policy ( CUSP ) database ( https://statepolicies.com/ ) to document the dates and components of economic relief and public health measures issued at the state level in response to the COVID-19 pandemic. Documented interventions included school and business closures, face mask mandates, directives on vaccine eligibility, eviction moratoria, and expanded unemployment insurance benefits. By providing continually updated information, CUSP was designed to inform rapid-response, policy-relevant research in the context of the COVID-19 pandemic and has been widely used to investigate the impact of state policies on population health and health equity. This paper introduces the CUSP database and highlights how it is already informing the COVID-19 pandemic response in the US. Conclusion CUSP is the most comprehensive publicly available policy database of health, social, and economic policies in response to the COVID-19 pandemic in the US. CUSP documents widespread variation in state policy decisions and implementation dates across the US and serves as a freely available and valuable resource to policymakers and researchers.

This work used event study to examine the impact of three policies (shutdowns, reopening, and mask mandates) on changes in the daily COVID-19 infection growth rate at the state level in the US (February through August 2020). The results show the importance of early intervention: shutdowns and mask mandates reduced the COVID-19 infection growth rate immediately after being imposed statewide. Over the longer term, mask mandates had a larger effect on flattening the curve than shutdowns. The increase in the daily infection growth rate pushed state governments to shut down, but reopening led to significant increases in new cases 21 days afterward. The results suggest a dynamic social distancing approach: a shutdown for a short period followed by reopening, combined with universal mask wearing. We also found that the COVID-19 growth rate increased in states with higher percentages of essential workers (during reopening) and higher percentages of minorities (during the mask mandate period). Health insurance access for low-income workers (via Medicaid expansion) helped to reduce COVID-19 cases in the reopening model. The implications for public health show the importance of access to health insurance and mask mandates to protect low-income essential workers, but minority groups still face a higher risk of infection during the pandemic.

This paper uses survey data at the county level to explore the factors determining mask-wearing behavior in the USA during the COVID-19 pandemic. Empirical results provide evidence that the tendency to wear a mask while in public is significantly lower in counties where then-candidate Donald Trump found strong support during the 2016 presidential election. In addition, states with mask-wearing mandates tend to witness greater mask-wearing behavior.

Social distance is known to impact interpersonal behaviors. We examine the potential consequences of mandated masking, which increases social distance, on social behavior. A controlled laboratory experiment was conducted to systematically impose a mask mandate in the treatment group, and to measure how this mandate affected other-regarding behavior within various social interactions. We find that behavior in the mandatory masking condition is less other-regarding compared to the control group with zero mask wearing. Particularly, we document less altruism, more sabotaging, and less cooperation. Our result suggests that mandatory masking has the potential to have broad behavioral consequences in the form of people generally becoming more selfish. Our results are found to be more pronounced among females than males.

Elevated face temperature due to mask wearing can cause discomfort and skin irritation, making mask mandates challenging. When thermal discomfort becomes intolerable, individuals instinctively or unknowingly loosen or remove their facemasks, compromising the mask’s protective efficacy. The objective of this study was to numerically quantify the microclimate under the mask and facial thermoregulation when wearing a surgical mask with different levels of misfit. An integrated ambient–mask–face–airway computational model was developed with gaps of varying sizes and locations and was validated against complementary experiments. The low Reynolds number (LRN) k-ω turbulence model with porous media was used to simulate transient respiratory flows. Both skin convective heat transfer and tissue heat generation were considered in thermoregulation under the facemask, besides the warm air exhaled from the body and the cool air inhaled from the ambient. The results of this study showed that when wearing a surgical mask with a perfect fit under normal breathing, the temperature at the philtrum increased by 4.3 °C compared to not wearing a mask. A small gap measuring 0.51 cm2 (gap A) at the nose top resulted in 5.6% leakage but reduced the warming effect by 28% compared to zero gap. Meanwhile, a gap of 4.3 cm2 (R1L1) caused 42% leakage and a 62% reduction in the warming effect. Unique temporospatial temperature profiles were observed at various sampling points and for different gap sizes, which correlated reasonably with the corresponding flow dynamics, particularly close to the gaps. The temperature change rate also exhibited patterns unique to the gap site and sampling point, with distinctive peaks occurring during the inspiratory–expiratory flow transitions. These results have the significant implications that by using the temporospatial temperature profiles at several landmark points, the gap location can potentially be pinpointed, and the gap size and leakage fractions can be quantified.

Questions of whether to enforce COVID-related mask mandates are complex. While enforced mandates are more effective at controlling community spread, government imposed behavioral controls have met significant opposition in conservative states, where a political bloc on the right is skeptical that COVID presents a significant and immediate threat. The authors conduct a split sample survey in order to examine how inclusion of a fine provision attached to mask mandates affects support. The survey was conducted in Idaho (a Republican dominated state) at a time when a mask mandate was a central debate. Unsurprisingly, respondents were more supportive of a mask mandate if a fine was not included. Further investigation indicates this is primarily a result of shifting Republican attitudes, which highlights the complex political situation in conservative states as leaders consider best mechanisms for battling COVID.

Although mask wearing has been demonstrated to be an effective strategy to combat the COVID-19 pandemic, it has become a contentious issue. This is evident in the policy shift regarding mask wearing during the pandemic and the varying mask mandates across different states in the United States. This study investigates the relationship between mask wearing and COVID-19-associated discrimination (CAD) over the course of the pandemic (March 2020 through May 2021), and differences between states with and without mask mandates. This study utilized three-level longitudinal analyses to analyze a longitudinal panel data from a nationally representative sample of U.S. adults enrolled in the Understanding America Study (UAS). The experiences of CAD were much higher for those wearing a mask than those not wearing a mask before August 2020, but this pattern was reversed afterward. Another notable finding was that mask wearers reported greater CAD in states with no mask-wearing mandate than the ones in states with mask mandates. In contrast, the pattern was reversed for nonmask wearers. The findings highlight the importance of inclusiveness and openness when relatively new health practice is introduced during a public health crisis.

Background Over the course of the COVID-19 pandemic, colleges and universities have focused on creating policies, such as mask mandates, to minimize COVID-19 transmission both on their campuses and in the surrounding community. Adherence to and opinions about these policies remain largely unknown. Methods The Centers for Disease Control and Prevention (CDC) developed a cross-sectional study, the Mask Adherence and Surveillance at Colleges and Universities Project (MASCUP!), to objectively and inconspicuously measure rates of mask use at institutes of higher education via direct observation. From February 15 through April 11, 2021 the University of Colorado Boulder (CU, n = 2,808 observations) and Colorado State University Fort Collins (CSU, n = 3,225 observations) participated in MASCUP! along with 52 other institutes of higher education (n = 100,353 observations) spanning 21 states and the District of Columbia. Mask use was mandatory at both Colorado universities and student surveys were administered to assess student beliefs and attitudes. Results We found that 91.7%, 93.4%, and 90.8% of persons observed at indoor locations on campus wore a mask correctly at University of Colorado, Colorado State University, and across the 52 other schools, respectively. Student responses to questions about masking were in line with these observed rates of mask use where 92.9% of respondents at CU and 89.8% at CSU believe that wearing masks can protect the health of others. Both Colorado universities saw their largest surges in COVID-19 cases in the fall of 2020, with markedly lower case counts during the mask observation window in the spring of 2021. Conclusion High levels of mask use at Colorado’s two largest campuses aligned with rates observed at other institutes across the country. These high rates of use, coupled with positive student attitudes about mask use, demonstrate that masks were widely accepted and may have contributed to reduced COVID-19 case counts. This study supports an emerging body of literature substantiating masks as an effective, low-cost measure to reduce disease transmission and establishes masking (with proper education and promotion) as a viable tactic to reduce respiratory disease transmission on college campuses.

COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace. The epidemiological models used in the literature have considerable drawbacks in the assumption of homogeneous mixing among the populace. Moreover, the effect of mitigation strategies such as mask mandate and stay at home orders cannot be efficiently accounted for in these models. In this work, we propose a novel data driven approach using LSTM (Long Short Term Memory) neural network model to form a functional mapping of daily new confirmed cases with mobility data which has been quantified from cell phone traffic information and mask mandate information. With this approach no pre-defined equations are used to predict the spread, no homogeneous mixing assumption is made, and the effect of mitigation strategies can be accounted for. The model learns the spread of the virus based on factual data from verified resources. A study of the number of cases for the state of New York (NY) and state of Florida (FL) in the USA are performed using the model. The model correctly predicts that with higher mobility the cases would increase and vice-versa. It further predicts the rate of new cases would see a decline if a mask mandate is administered. Both these predictions are in agreement with the opinions of leading medical and immunological experts. The model also predicts that with the mask mandate option even a higher mobility would reduce the daily cases than lower mobility without masks. We additionally generate results and provide RMSE (Root Mean Square Error) comparison with ARIMA based model of other published work for Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Our model reports lower RMSE than the ARIMA based work for all eight countries which were tested. The proposed model would provide administrations with a quantifiable basis of how mobility, mask mandates are related to new confirmed cases; so far no epidemiological models provide that information. It gives fast and relatively accurate prediction of the number of cases and would enable the administrations to make informed decisions and make plans for mitigation strategies and changes in hospital resources.

A recent modelling paper on the coronavirus disease 2019 (COVID-19) epidemic in the US (Bartsch et al.) suggested that maintaining face mask use until a high vaccine coverage (70-90%) is achieved is generally cost-effective or even cost-saving in many of the scenarios considered. Their conclusion was based on the assumed effectiveness of continued face mask use, cited from a study that reported an 18% reduction in the effective reproduction number associated with the introduction of state-level mask mandate policies in the US in the summer of 2020. However, using this value implicitly assumes that the effect of face mask use in 2021 through 2022 is the same as that of summer 2020, when stringent nonpharmaceutical interventions were in place. The effectiveness of universal mask wearing in 2021-2022 is probably more uncertain than considered in Bartsch et al. and rigorous sensitivity analysis on this parameter is warranted.