Prevention and control of coronavirus disease 2019 (COVID-19) in public places

Back to Normal? Harnessing Long Short-term Memory Network to Examine the Associations Between Adolescent Social Interactions and Depressive Symptoms During Different Stages of COVID-19

Adolescence is a developmental period in which social interactions are critical for mental health. While the onset of COVID-19 significantly disrupted adolescents' social environments and mental health, it remains unclear how adolescents have adapted to later stages of the pandemic. We harnessed a machine learning architecture of Long Short-Term Memory recurrent networks (LSTM) with gradient-based feature importance, to model the association among daily social interactions and depressive symptoms during three stages of the pandemic. A year before COVID-19, 148 adolescents reported social interactions and depressive symptoms, every day for 21 days. One hundred sixteen of these youths completed a 28-day diary after schools closed due to COVID-19. Seventy-nine of these youths and additional 116 new participants completed a 28-day diary approximately a year into the pandemic. Our results show that LSTM successfully predicted depressive symptoms from at least a week of social interactions for all three waves (r2 > .70). Our study shows the utility of using an analytic approach that can identify temporal and nonlinear pathways through which social interactions may confer risk for depression. Our unique analysis of the importance of input features enabled us to interpret the association between social interactions and depressive symptoms. Collectively, we observed a return to pre-pandemic patterns a year into the pandemic, with reduced gender and age differences during the pandemic closures. This pattern suggests that the system of social influences in adolescence was affected by COVID-19, and that this effect was attenuated in more chronic stages of the pandemic.

Public Opinion Through Art: Exploring Chinese University Students' Perspectives on COVID-19 Mass Nucleic Acid Testing

In the context of the global COVID-19 pandemic, this study focuses on Chinese university students, employing graphic elicitation as a qualitative research method to analyze their hand-drawn paintings and related descriptions. Augmented by A/r/tography and metacognitive methods, the research aims to unveil the participants' collective memory, as well as the perspectives and responses of these students to policies related to the pandemic. By specifically examining this particular demographic, the study incorporates Fairclough's ethical theory, applying deontological ethics, consequentialist ethics, and virtue ethics to establish a comprehensive framework for evaluating adjustments to pandemic response policies. This research not only enhances our understanding of how these university students perceive and adapt to COVID-19 policies but also provides valuable insights for decision-makers. The particular methodology, combining graphic elicitation and metacognitive research, contributes to policy assessment and ethical analysis, offering a nuanced perspective on the interplay between individual perceptions, policy responses, and ethical considerations amid the complexities of a public health crisis.

Biocidal products: Opportunities in risk assessment, management, and communication

In the coronavirus disease 2019 era, biocidal products are increasingly used for controlling harmful organisms, including microorganisms. However, assuring safety against adverse health effects is a critical issue from a public health standpoint. This study aimed to provide an overview of key aspects of risk assessment, management, and communication that ensure the safety of biocidal active ingredients and products. The inherent characteristics of biocidal products make them effective against pests and pathogens; however, they also possess potential toxicities. Therefore, public awareness regarding both the beneficial and potential adverse effects of biocidal products needs to be increased. Biocidal active ingredients and products are regulated under specific laws: the Federal Insecticide, Fungicide, and Rodenticide Act for the United States; the European Union (EU) Biocidal Products Regulation for the EU; and the Consumer Chemical Products and Biocide Safety Management Act for the Republic of Korea. Risk management also needs to consider the evidence of enhanced sensitivity to toxicities in individuals with chronic diseases, given the increased prevalence of these conditions in the population. This is particularly important for post-marketing safety assessments of biocidal products. Risk communication conveys information, including potential risks and risk-reduction measures, aimed at managing or controlling health or environmental risks. Taken together, the collaborative effort of stakeholders in risk assessment, management, and communication strategies is critical to ensuring the safety of biocidal products sold in the market as these strategies are constantly evolving.

Effects of the COVID-19 pandemic on mental health and sleep quality in the Chinese general populations during sporadic and outbreak periods

The different coronavirus disease 2019 (COVID-19) outbreak patterns may have different impact on the mental health, but there is no such report at present. This study was aimed to investigate general populations mental health and sleep quality during the sporadic and outbreak of COVID-19 in Tianjin of China. Two cross-sectional studies included 1090 participants during the COVID-19 sporadic period from 28th January to 28th February 2022 and 2036 participants during the COVID-19 outbreak period from 1st January to 1st February 2023. We used the propensity score matching method to match the 2 samples (sporadic and outbreak group) with a ratio of 1:1. Finally, 1087 participants were included in each group. The demographics, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, Connor-Davidson Resilience Scale, and Pittsburgh Sleep Quality Index were collected from all participants. There was a significant difference between the sporadic and outbreak groups for COVID-19-infected status, anxiety levels, sleep quality, and phycological resilience (all P < .05). Logistic regression analysis showed that female (P < .05) and depression status (P < .05) were the risk factors for the incidence rate of anxiety and poor sleep quality in sporadic group, and outbreak group, while phycological resilience were robust related to the sleep quality (P < .01). Poor sleep quality (P < .01) was related to the incidence of anxiety in sporadic group but not the outbreak group; anxiety levels (P < .01) were related to sleep quality in sporadic group but not the outbreak group. During the COVID-19 pandemic, general populations experienced high anxiety and poor sleep quality incidence rates, especially in the outbreak period. Different risk models for anxiety and sleep quality were found in the different outbreak patterns.

Adherence to Physical Distancing and Health Beliefs About COVID-19 Among Patients With Cancer

BACKGROUND

This study aimed to validate questionnaires on adherence to physical distancing and health beliefs about coronavirus disease 2019 (COVID-19) among patients with cancer and explore their interaction with depression or viral anxiety among them.

METHODS

Through an online survey, data from 154 cancer patients (female: 82.5%, breast cancer: 66.2%, current cancer treatment, presence: 65.6%) were collected from March to June 2022. The survey gathered responses to questionnaires on adherence to physical distancing, health beliefs about COVID-19, perceived social norms, Stress and Anxiety to Viral Epidemics-6 items, and Patient Health Questionnaire-2. Confirmatory factor analysis (CFA) for construct validity and structural equation model (SEM) were performed.

RESULTS

The CFA showed a good model fit for adherence to physical distancing (comparative fit index [CFI] = 1.000, Tucker-Lewis index [TLI] = 0.930, root-mean-square-error of approximation [RMSEA] = 0.000, and standardized root-mean-square residual [SRMR] = 0.050) and a satisfactory model fit for health beliefs about COVID-19 (CFI = 0.978, TLI = 0.971, RMSEA = 0.061, and SRMR = 0.089). Through SEM, we found that personal injunctive norms were the main mediators linking health beliefs with physical distancing in patients with cancer. Depression also mediated the effects of viral anxiety and perceived severity on physical distancing (χ² = 20.073, df = 15, P = 0.169; CFI = 0.984; RMSEA = 0.047).

CONCLUSION

The questionnaires are reliable and valid. Patients with cancer may be able to adhere to physical distancing by addressing perceived severity, viral anxiety, perceived benefits, self-efficacy, perceived barriers, as well as personal injunctive norms.

Highly Effective Salt-Activated Alcohol-Based Disinfectants with Enhanced Antimicrobial Activity

Surfaces contaminated with pathogens raise concerns about the increased risk of disease transmission and infection. To clean biocontaminated surfaces, alcohol-based disinfectants have been predominantly used for disinfecting high-touch areas in diverse settings. However, due to its limited antimicrobial activities and concern over the emergence of alcohol-tolerant strains, much effort has been made to develop highly efficient disinfectant formulations. In this study, we hypothesize that the addition of a physical pathogen inactivation mechanism by salt recrystallization (besides the existing chemical inactivation mechanism by alcohol in such formulations) can improve inactivation efficiency by preventing the emergence of alcohol tolerance. To this end, we employed the drying-induced salt recrystallization process to implement the concept of highly efficient alcohol-based disinfectant formulations. To identify the individual and combined effects of isopropyl alcohol (IPA) and NaCl, time-dependent morphological/structural changes of various IPA solutions containing NaCl have been characterized by optical microscopy/X-ray diffraction analysis. Their antimicrobial activities have been tested on surfaces (glass slide, polystyrene Petri dish, and stainless steel) contaminated with Gram-positive/negative bacteria (methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella enterica subsp. enterica Typhimurium) and viruses (A/PR8/34 H1N1 influenza virus and HCoV-OC43 human coronavirus). We found that additional salt crystallization during the drying of the alcohol solution facilitated stronger biocidal effects than IPA-only formulations, regardless of the types of solid surfaces and pathogens, including alcohol-tolerant strains adapted from wild-type Escherichia coli MG1655. Our findings can be useful in developing highly effective disinfectant formulations by minimizing the use of toxic antimicrobial substances to improve public health and safety.

Heat waves accelerate the spread of infectious diseases

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

The association between COVID-19 preventive behaviors and mental health conditions

BACKGROUND

An unintended consequence of COVID-19 quarantine preventive measures, is the increased prevalence of anxiety and depression. The purpose of this study was to examine the association between COVID-19 preventive behaviors and mental health conditions.

METHODS

A cross-sectional study was conducted using secondary data collected weekly from US adults aged 18 and older nationwide as part of the COVID-19 Household Impact Survey (CIS) from the University of Chicago. Logistic regression examined associations between COVID-19 preventive behaviors (wearing a face mask, washing or sanitizing hands, and keeping six-feet distance from those outside their household), mental health conditions (self-reporting feeling nervous, anxious, or on edge, feeling lonely, and feeling hopeless about the future and a history of a mental health condition) and demographic factors.

RESULTS

Majority of study participants were under 60 years (62.2%), female (55.8%), and non-Hispanic White (72.2%). Overall, participants more likely to have followed all three COVID-19 measures were those who reported high psychological distress compared to those with low distress for feeling anxious (adj. OR 1.16, 95% CI: 1.06-1.28, p = 0.002), lonely (adj. OR 1.12, 95% CI: 1.02-1.23, p = 0.019) or hopeless (adj. OR 1.10, 95% CI: 1.00-1.21, p = 0.043) for more than a day during the past 7 days.

CONCLUSION

Our findings highlight that individuals with mental health conditions reported more psychological distress. Specifically, feeling depressed, anxious, lonely, and hopeless were triggered and exacerbated as a result of the pandemic and may have long-term effects on general well-being and productivity. Therefore, our findings have important implications on the need to include mental health promotion as part of pandemic response efforts. This includes developing policies and allocating funding so as to ensure sustainable mental health interventions and support, public and provider education on the importance of screening for mental health issues.

Spatiotemporal variations and the ecological risks of organophosphate esters in Laizhou Bay waters between 2019 and 2021: Implying the impacts of the COVID-19 pandemic

Organophosphate esters (OPEs) are a group of synthetic chemicals used in numerous consumer products such as plastics and furniture. The Coronavirus Disease 2019 (COVID-19) pandemic significantly slowed anthropogenic activities and reduced the emissions of pollutants. Meanwhile, the mismanagement of large quantities of disposable plastic facemasks intensified the problems of plastic pollution and leachable pollutants in coastal waters. In this study, the joint effects of the COVID-19 outbreak on the occurrence of 12 targeted OPEs in the waters of Laizhou Bay (LZB) were investigated. The results showed that the median total OPE concentrations were 725, 363, and 109 ng L^-1 in the sewage treatment plant effluent, river water, and bay water in 2021, decreased significantly (p < 0.05) by 67%, 68%, and 70%, respectively, compared with those before the COVID-19 outbreak. The release potential of targeted OPEs from disposable surgical masks in the LZB area was ∼0.24 kg yr^-1, which was insufficient to increase the OPE concentration in the LZB waters. The concentrations of most individual OPEs significantly decreased in LZB waters from 2019 to 2021, except for TBOEP and TNBP. Spatially, a lower concentration of OPEs was found in the Yellow River estuary area in 2021 compared with that before the COVID-19 pandemic due to the high content of suspended particulate matter in the YR. A higher total OPE concentration was observed along the northeastern coast of LZB, mainly owing to the construction of an artificial island since 2020. The ecological risks of the OPE mixture in LZB waters were lower than those before the COVID-19 outbreak. However, TCEP, TNBP, and BDP should receive continuous attention because of their potential ecological risks to aquatic organisms.

Effect of wearing medical protective masks on treadmill running performance in the postpandemic era: a randomised trial

BACKGROUND

In the postpandemic era, wearing protective masks in public places will still be an important means of blocking popular viruses in the future. The purpose of this study was to explore whether sports performance was affected by mask wearing and exercise duration during 15-min treadmill running at a speed of 75% maximal aerobic speed.

METHODS

Thirty-six males were randomly divided into mask and nonmask groups. The kinematic and kinetic data were obtained at four time points (RN_0-1 min, RN_5-6 min, RN_9-10 min, and RN_14-15 min) during running. Two-way mixed ANOVA was applied to examine the effects between groups and times with Bonferroni post hoc comparison and independent samples t-test.

RESULTS

The results showed that there was no difference between mask and nonmask group during running (p > 0.05). As running time increased, hip joint ROM, hip joint flexion/extension max, and ankle joint plantarflexion max angles increased; knee joint flexion min and ankle joint dorsiflexion max angles decreased; average peak vertical ground reaction forces (PVGRF) increased after 9 min-running (p < 0.05).

CONCLUSIONS

Wearing a medical protective mask does not affect the joint angle and touchdown PVGRF of lower extremities during treadmill running while affected by running time and changed after 9 min-treadmill running. Future studies will examine the effects of wearing masks during the pandemic on muscle activation and blood biochemical values during exercise.

TRIAL REGISTRATION NO

ChiCTR2000040535 (date of registration on December 1, 2020). Prospectively registered in the Chinese Clinical Trial Registry.

Environmental impacts of the widespread use of chlorine-based disinfectants during the COVID-19 pandemic

Chlorinated disinfectants are widely used in hospitals, COVID-19 quarantine facilities, households, institutes, and public areas to combat the spread of the novel coronavirus as they are effective against viruses on various surfaces. Medical facilities have enhanced their routine disinfection of indoors, premises, and in-house sewage. Besides questioning the efficiency of these compounds in combating coronavirus, the impacts of these excessive disinfection efforts have not been discussed anywhere. The impacts of chlorine-based disinfectants on both environment and human health are reviewed in this paper. Chlorine in molecular and in compound forms is known to pose many health hazards. Hypochlorite addition to soil can increase chlorine/chloride concentration, which can be fatal to plant species if exposed. When chlorine compounds reach the sewer/drainage system and are exposed to aqueous media such as wastewater, many disinfection by-products (DBPs) can be formed depending on the concentrations of natural organic matter, inorganics, and anthropogenic pollutants present. Chlorination of hospital wastewater can also produce toxic drug-derived disinfection by-products. Many DBPs are carcinogenic to humans, and some of them are cytotoxic, genotoxic, and mutagenic. DBPs can be harmful to the flora and fauna of the receiving water body and may have adverse effects on microorganisms and plankton present in these ecosystems.

Remain or return? An empirical study of influencing factors on the return of Chinese international students during the COVID-19 pandemic

Background

COVID-19 is now a global public health crisis with unprecedented political, economic, and social consequences affecting nations across the world. It also has a profound impact on the mobility of international students. When the COVID-19 was under control in China, and it was spreading dramatically in the United Kingdom, Chinese international students studying in the United Kingdom have been caught in a double bind over whether to return home or not.

Objective

This study aims to explore the factors that influenced Chinese international students' choices of return during the COVID-19 pandemic when the COVID-19 was under control in China, while it was spreading dramatically in the United Kingdom.

Methods

Taking Chinese international students studying in the United Kingdom as an empirical case, this study used qualitative and quantitative research methods to explore the factors that influenced their choices of return. Based on the Rational Choice Theory and qualitative analysis of text data, this paper constructed the influencing factors model of returning to China. On this basis, we developed a questionnaire and collected data from 1,333 students in late April and early May 2020. Binary Logistic Regression with 95% CI for odds ratio (OR) was used to identify significant factors.

Results

The reserve of epidemic prevention supplies (OR = 0.712), transportation expenses (OR = 0.618), and quarantine expenses (OR = 0.702) negatively affected the return choice of overseas students. The supply of daily necessities (OR = 1.495), the anti-epidemic policy of the United Kingdom (OR = 1.684), and the demand for job hunting after graduation (OR = 1.661) had positive effects.

Conclusion

The institutional rationality had the biggest promoting effect, replaced development rationality, and became the most important factor for overseas students to return to China during the COVID-19 pandemic. Economic rationality, which has a significant negative effect, is the biggest obstacle to returning home. These conclusions have policy implications for governments' response to the COVID-19 epidemic and improvement of the quality of services for overseas students.

Cheminformatics-Based Discovery of Potential Chemical Probe Inhibitors of Omicron Spike Protein

During the past two decades, the world has witnessed the emergence of various SARS-CoV-2 variants with distinct mutational profiles influencing the global health, economy, and clinical aspects of the COVID-19 pandemic. These variants or mutants have raised major concerns regarding the protection provided by neutralizing monoclonal antibodies and vaccination, rates of virus transmission, and/or the risk of reinfection. The newly emerged Omicron, a genetically distinct lineage of SARS-CoV-2, continues its spread in the face of rising vaccine-induced immunity while maintaining its replication fitness. Efforts have been made to improve the therapeutic interventions and the FDA has issued Emergency Use Authorization for a few monoclonal antibodies and drug treatments for COVID-19. However, the current situation of rapidly spreading Omicron and its lineages demands the need for effective therapeutic interventions to reduce the COVID-19 pandemic. Several experimental studies have indicated that the FDA-approved monoclonal antibodies are less effective than antiviral drugs against the Omicron variant. Thus, in this study, we aim to identify antiviral compounds against the Spike protein of Omicron, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor and facilitates virus invasion. Initially, docking-based virtual screening of the in-house database was performed to extract the potential hit compounds against the Spike protein. The obtained hits were optimized by DFT calculations to determine the electronic properties and molecular reactivity of the compounds. Further, MD simulation studies were carried out to evaluate the dynamics of protein–ligand interactions at an atomistic level in a time-dependent manner. Collectively, five compounds (AKS-01, AKS-02, AKS-03, AKS-04, and AKS-05) with diverse scaffolds were identified as potential hits against the Spike protein of Omicron. Our study paves the way for further in vitro and in vivo studies.

A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan

Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.

A transfer learning based deep learning model to diagnose covid-19 CT scan images

To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient’s load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26–230) and Otsu’s algorithm. On comparative analysis of all these methods, it is found that the Otsu’s algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu’s segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu’s segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu’s segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).

One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era

This paper proposes a sustainable management and decision-making model for COVID-19 control in schools, which makes improvements to current policies and strategies. It is not a case study of any specific school or country. The term one-size-fits-all has two meanings: being blind to the pandemic, and conducting inflexible and harsh policies. The former strategy leads to more casualties and does potential harm to children. Conversely, under long-lasting strict policies, people feel exhausted. Therefore, some administrators pretend that they are working hard for COVID-19 control, and people pretend to follow pandemic control rules. The proposed model helps to alleviate these problems and improve management efficiency. A customized queue model is introduced to control social gatherings. An indoor–outdoor tracking system is established. Based on tracing data, we can assess people’s infection risk, and allocate medical resources more effectively in case of emergency. We consider both social and technical feasibility. Test results demonstrate the improvements and effectiveness of the model. In conclusion, the model has patched up certain one-size-fits-all strategies to balance pandemic control and normal life.

Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.

Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R²-score = 0.44% for COVID-19 cases and R² = 60% for COVID-19 deaths. The regression model with an R²-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.

Viral Anxiety Mediates the Influence of Intolerance of Uncertainty on Adherence to Physical Distancing Among Healthcare Workers in COVID-19 Pandemic

INTRODUCTION

The aims of this study were to examine the mediation effect of viral anxiety of healthcare workers on the influence of their intolerance of uncertainty on the adherence to physical distancing during the COVID-19 pandemic.

METHODS

An online survey was conducted among 329 healthcare workers (female: 81.4%, nursing professionals: 59.0%, and shift workers: 22.3%) on November 29, 2021. Participants responded to questionnaires on adherence to physical distancing, health beliefs, and perceived social norms, and rating scales of the Stress and Anxiety to Viral Epidemics-6 items (SAVE-6), Patient Health Questionnaire-9 items (PHQ-9), and the Intolerance of Uncertainty-12 items (IUS-12) scale.

RESULTS

Adherence to physical distancing of healthcare workers was predicted by perceived benefits of physical distancing (β = 0.13, p = 0.01), personal injunctive norms (β = 0.32, p < 0.001), SAVE-6 score (β = 0.13, p = 0.02), and IUS-12 score (β = 0.10, p = 0.045) (adjusted R² = 0.21, F = 22.3, p < 0.001). Viral anxiety mediated the association between intolerance of uncertainty and adherence to physical distancing but not the influence of perceived benefits and personal injunctive norms on adherence to physical distancing.

CONCLUSION

We observed that viral anxiety of healthcare workers mediated the association between intolerance of uncertainty and adherence to physical distancing. During this pandemic, exploring adherence to physical distancing and its predicting factors will be helpful for the safety of healthcare workers and the patients for whom they care.