Search Results for « Mask Mandate »

Face masks are one of the cheapest and most effective non-pharmaceutical interventions available against airborne diseases such as COVID-19. Unfortunately, they have been met with resistance by a substantial fraction of the populace, especially in the U.S. In this study, we uncover the latent moral values that underpin the response to the mask mandate, and paint them against the country's political backdrop. We monitor the discussion about masks on Twitter, which involves almost 600k users in a time span of 7 months. By using a combination of graph mining, natural language processing, topic modeling, content analysis, and time series analysis, we characterize the responses to the mask mandate of both those in favor and against them. We base our analysis on the theoretical frameworks of Moral Foundation Theory and Hofstede's cultural dimensions. Our results show that, while the anti-mask stance is associated with a conservative political leaning, the moral values expressed by its adherents diverge from the ones typically used by conservatives. In particular, the expected emphasis on the values of authority and purity is accompanied by an atypical dearth of in-group loyalty. We find that after the mandate, both pro- and anti-mask sides decrease their emphasis on care about others, and increase their attention on authority and fairness, further politicizing the issue. In addition, the mask mandate reverses the expression of Individualism-Collectivism between the two sides, with an increase of individualism in the anti-mask narrative, and a decrease in the pro-mask one. We argue that monitoring the dynamics of moral positioning is crucial for designing effective public health campaigns that are sensitive to the underlying values of the target audience.

Background: A recent epidemiological analysis of staggered policy implementation reported a 29.4% reduction in COVID-19 cases by maintaining school mask mandates in the greater Boston area during the first half of 2022. The robustness of their results and the appropriateness of methodology are explored. Methods: Using data from the Massachusetts Department of Elementary and Secondary Education and the Centers for Disease Control and Prevention, we re-analyze differences in COVID-19 incidence in school districts that did and did not lift mask mandates using the same districts as the original study and expanded the analysis to the entire state of Massachusetts. We present changes in case rates and differences in prior immunity in areas with different mask lifting policies. Results: The Boston and Chelsea districts, which maintained mask mandates, were outliers in terms of size, demographics, and testing. We failed to find a notable change in student cases in mask mandate districts compared with the 70 districts in the original study (-0.08/1000; p=0.98) and found a slight increase compared with a statewide control group +3.63/1000 (p=0.291). Results were similar for students and staff combined. Districts that dropped mask mandates first experienced the largest decreases in cases (22% drop vs 12% in the masked districts). There was a moderate to strong relationship (R2 = 0.35-0.66; p-values <0.001) between prior community infection burden and district case rates in Spring 2022, with prior immunity alone explaining as much as two-thirds of the variation in case rates in Spring of 2022. Conclusions: We fail to find any consistent notable negative relationship between school mask mandates and infection rates in the Greater Boston Area or state of Massachusetts during the 2021-2022 academic year.

We quantify the effect of mask mandates in the United States. Our regression discontinuity design exploits county-level variation in COVID-19 cases, hospital admissions, and deaths across the border between states with and without mandates. We find a significant and substantial effect—mask mandates reduced new weekly COVID-19 cases, hospital admissions, and deaths by 55, 11 and 0.7 per 100,000 inhabitants on average. Crucially, we find that the effect of mask mandates depends on the attitudes toward mask wearing at the county level, with larger effects in counties more positively inclined towards mask wearing. Our results imply that mandates saved 87,000 lives through December 19, 2020, while a nationwide mandate could have saved 58,000 additional lives. These large effects suggest that mask mandates are a crucial tool to counter pandemics, particularly if accepted widely by the population. Our results are thus also relevant for countries who will not be able to immunize large swaths of their population in the short term.

Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence. Comparing to landmark-based sparse face shape representation, our method can produce the segmentation masks of individual facial components, which can better reflect their detailed shape variations. By integrating Convolutional LSTM (ConvLSTM) algorithm with Fully Convolutional Networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and takes advantage of the temporal correlation in video clips. In addition, we also propose a novel loss function, called Segmentation Loss, to directly optimise the Intersection over Union (IoU) performances. In practice, to further increase segmentation accuracy, one primary model and two additional models were trained to focus on the face, eyes, and mouth regions, respectively. Our experiment shows the proposed method has achieved a 16.99% relative improvement (from 54.50% to 63.76% mean IoU) over the baseline FCN model on the 300 Videos in the Wild (300VW) dataset.

This paper presents the results from a hypothetical set of questions related to mask-wearing behavior and opinions that were asked of a nationally representative sample of over 4,000 participants in early 2022.Mask mandates were hotly debated in public discourse, and though much research exists on benefits of masks, there has been no research thus far on the distribution of perceived costs of compliance.As is common in economic research that aims to assess the value to society of non-market activities, we use survey valuation methods and ask how much participants would be willing to pay to be exempted from rules of mandatory community masking.The survey asks specifically about a 3 month exemption.We find that the majority of respondents (56%) are not willing to pay to be exempted from mandatory masking.However, the average person was willing to pay $525, and a small segment of the population (0.9%) stated they were willing to pay over $5,000 to be exempted from the mandate.Younger respondents stated higher willingness to pay to avoid the mandate than older respondents.Combining our results with standard measures of the value of a statistical life, we estimate that a 3 month masking order was perceived as cost effective through willingness-to-pay questions only if at least 13,333 lives were saved by the policy.

Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in recent years, most state-of-the-art (SOTA) methods still suffer from considerable language-image modality gap at the pixel and word level. These methods generally 1) rely on sentence-level language features for language-image alignment and 2) lack explicit training supervision for fine-grained visual grounding. Consequently, they exhibit weak object-level correspondence between visual and language features. Without well-grounded features, prior methods struggle to understand complex expressions that require strong reasoning over relationships among multiple objects, especially when dealing with rarely used or ambiguous clauses. To tackle this challenge, we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features, by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects. Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore, to holistically address the modality gap, we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques, our comprehensive approach culminates in MagNet (Mask-grounded Network), an architecture that significantly outperforms prior arts on three key benchmarks (RefCOCO, RefCOCO+ and G-Ref), demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released.

Abstract Mask mandates were controversial policies during the pandemic. Although there is considerable research on the benefits of masks, there has been no research on the distribution of perceived costs of compliance with mask mandates. This article presents the results from a hypothetical set of questions related to mask-wearing behavior and opinions that were asked of a nationally representative sample of over 4,000 participants in early 2022. We use survey valuation methods to assess how much participants would be willing to pay to be exempted from rules of mandatory community masking. The survey asks specifically about a 3-month exemption. We find that the majority of respondents (56%) are not willing to pay to be exempted from mandatory masking. However, the average person was willing to pay $525, and a small segment of the population (0.9%) stated they were willing to pay over $5,000 to be exempted from the mandate. Younger respondents stated higher willingness to pay to avoid the mandate than older respondents. Combining our results with standard measures of the value of a statistical life, we estimate that a 3-month masking order was perceived as cost-effective through willingness to pay questions only if at least 13,333 lives were saved by the policy.

We introduce Mask-ToF, a method to reduce flying pixels (FP) in time-of-flight (ToF) depth captures. FPs are pervasive artifacts which occur around depth edges, where light paths from both an object and its background are integrated over the aperture. This light mixes at a sensor pixel to produce erroneous depth estimates, which can adversely affect downstream 3D vision tasks. Mask-ToF starts at the source of these FPs, learning a microlens-level occlusion mask which effectively creates a custom-shaped sub-aperture for each sensor pixel. This modulates the selection of foreground and background light mixtures on a per-pixel basis and thereby encodes scene geometric information directly into the ToF measurements. We develop a differentiable ToF simulator to jointly train a convolutional neural network to decode this information and produce high-fidelity, low-FP depth reconstructions. We test the effectiveness of Mask-ToF on a simulated light field dataset and validate the method with an experimental prototype. To this end, we manufacture the learned amplitude mask and design an optical relay system to virtually place it on a high-resolution ToF sensor. We find that Mask-ToF generalizes well to real data without retraining, cutting FP counts in half.

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.