COVID-19 Contagion Risk Estimation Model for Indoor Environments.

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.

Party over pandemic: Polarized trust in political leaders and experts explains public support for COVID-19 policies

Two experiments examined the polarization of public support for COVID-19 policies due to people’s (lack of) trust in political leaders and nonpartisan experts. In diverse samples in the United States (Experiment 1;N = 1,802) and the United Kingdom (Experiment 2;N = 1,825), participants evaluated COVID-19 policies that were framed as proposed by ingroup political leaders, outgroup political leaders, nonpartisan experts, or, in the United States, a bipartisan group of political leaders. At the time of the study in April 2020, COVID-19 was an unfamiliar and shared threat. Therefore, there were theoretical reasons suggesting that attitudes toward COVID-19 policy may not have been politically polarized. Yet, our results demonstrated that even relatively early in the pandemic people supported policies from ingroup political leaders more than the same policies from outgroup leaders, extending prior research on how people align their policy stances to political elites from their own parties. People also trusted experts and ingroup political leaders more than they did outgroup political leaders. Partly because of this polarized trust, policies from experts and bipartisan groups were more widely supported than policies from ingroup political leaders. These results illustrate the potentially detrimental role political leaders may play and the potential for effective leadership by bipartisan groups and nonpartisan experts in shaping public policy attitudes during crises. [ FROM AUTHOR] Copyright of Group Processes & Intergroup Relations is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Politicians polarize and experts depolarize public support for COVID-19 management policies across countries.

Political polarization impeded public support for policies to reduce the spread of COVID-19, much as polarization hinders responses to other contemporary challenges. Unlike previous theory and research that focused on the United States, the present research examined the effects of political elite cues and affective polarization on support for policies to manage the COVID-19 pandemic in seven countries (n = 12,955): Brazil, Israel, Italy, South Korea, Sweden, the United Kingdom, and the United States. Across countries, cues from political elites polarized public attitudes toward COVID-19 policies. Liberal and conservative respondents supported policies proposed by ingroup politicians and parties more than the same policies from outgroup politicians and parties. Respondents disliked, distrusted, and felt cold toward outgroup political elites, whereas they liked, trusted, and felt warm toward both ingroup political elites and nonpartisan experts. This affective polarization was correlated with policy support. These findings imply that policies from bipartisan coalitions and nonpartisan experts would be less polarizing, enjoying broader public support. Indeed, across countries, policies from bipartisan coalitions and experts were more widely supported. A follow-up experiment replicated these findings among US respondents considering international vaccine distribution policies. The polarizing effects of partisan elites and affective polarization emerged across nations that vary in cultures, ideologies, and political systems. Contrary to some propositions, the United States was not exceptionally polarized. Rather, these results suggest that polarizing processes emerged simply from categorizing people into political ingroups and outgroups. Political elites drive polarization globally, but nonpartisan experts can help resolve the conflicts that arise from it.

When election expectations fail: Polarized perceptions of election legitimacy increase with accumulating evidence of election outcomes and with polarized media.

The present study, conducted immediately after the 2020 presidential election in the United States, examined whether Democrats' and Republicans' polarized assessments of election legitimacy increased over time. In a naturalistic survey experiment, people (N = 1,236) were randomly surveyed either during the week following Election Day, with votes cast but the outcome unknown, or during the following week, after President Joseph Biden was widely declared the winner. The design unconfounded the election outcome announcement from the vote itself, allowing more precise testing of predictions derived from cognitive dissonance theory. As predicted, perceived election legitimacy increased among Democrats, from the first to the second week following Election Day, as their expected Biden win was confirmed, whereas perceived election legitimacy decreased among Republicans as their expected President Trump win was disconfirmed. From the first to the second week following Election Day, Republicans reported stronger negative emotions and weaker positive emotions while Democrats reported stronger positive emotions and weaker negative emotions. The polarized perceptions of election legitimacy were correlated with the tendencies to trust and consume polarized media. Consumption of Fox News was associated with lowered perceptions of election legitimacy over time whereas consumption of other outlets was associated with higher perceptions of election legitimacy over time. Discussion centers on the role of the media in the experience of cognitive dissonance and the implications of polarized perceptions of election legitimacy for psychology, political science, and the future of democratic society.

A Non-Contact Integrated Body-Ambient Temperature Sensors Platform to Contrast COVID-19

An integrated sensors platform for non-contact temperature monitoring is proposed in this work. The adopted solution, based on the combined integration of an infrared thermometer and a capacitive humidity sensor, is able to provide a fast and accurate tool for remotely sensing both ambient and body temperature in the framework of pandemic situations, such as COVID-19, thus avoiding any direct contact with people. The information relative to the ambient temperature is successfully exploited to derive a correction formula for the accurate extraction of body temperature from the measurement provided by the standard infrared sensor. Full details on the design of the proposed platform are provided in the work, by reporting relevant simulation results on the variations of ambient temperature, relative humidity, and body temperature. Experimental validations are also discussed to provide a full assessment of the proposed approach.