Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people's lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account. © 2023 by American Association of Geographers.

The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

The preventive effect and mechanism of Dayuanyin in hypoxic pulmonary hypertension through NF-kappaB signaling pathway.

Dayuanyin (DYY) has been shown to reduce lung inflammation in both coronavirus disease 2019 (COVID-19) and lung injury. This experiment was designed to investigate the efficacy and mechanism of action of DYY against hypoxic pulmonary hypertension (HPH) and to evaluate the effect of DYY on the protection of lung function. Animal welfare and experimental procedures are approved and in accordance with the provision of the Animal Ethics Committee of the Institute of Materia Medica, Chinese Academy of Medical Science. Male C57/BL6J mice were randomly divided into 4 groups: control group, model group, DYY group (800 mg.kg-1), and positive control sildenafil group (100 mg.kg-1). The animals were given control solvents or drugs by gavage three days in advance. On day 4, the animals in the model group, DYY group and sildenafil group were kept in a hypoxic chamber containing 10% +/- 0.5% oxygen, and the animals in the control group were kept in a normal environment, and the control solvent or drugs continued to be given continuously for 14 days. The right ventricular systolic pressure, right ventricular hypertrophy index, organ indices and other metrics were measured in the experimental endpoints. Meantime, the expression levels of the inflammatory factors in mice lung tissues were measured. The potential therapeutic targets of DYY on pulmonary hypertension were predicted using network pharmacology, the expression of nuclear factor kappa B (NF- kappaB) signaling pathway-related proteins were measured by Western blot assay. It was found that DYY significantly reduced the right ventricular systolic pressure, attenuated lung injury and decreased the expression of inflammatory factors in mice. It can also inhibit hypoxia-induced activation of NF- kappaB signaling pathway. DYY has a protective effect on lung function, as demonstrated by DYY has good efficacy in HPH, and preventive administration can slow down the disease progression, and its mechanism may be related to inhibit the activation of NF-kappaB and signal transducer and activator of transcription 3 (STAT3) by DYY.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.

The application and development of Smart city technologies in public health during the Covid-19 pandemic

Over the past few years, during the Cov-19 pandemic, a great deal of smart city technologies have been used by the public health sector to fight against the common enemy of humanity. This paper studies the challenges faced by public hospitals and public monitoring. Meanwhile, it also introduces the application and development of smart city technologies, such as AI and IoT, in the field of public health during the pandemic. From a practical standpoint, the author believes that this pandemic has provided numerous opportunities for testing smart city technologies in public health. In the future, public health must be integrated with smart city technologies in a wider range to improve cities' ability to deal with major outbreaks and provide public health care. © 2023 SPIE.

The preventive effect and mechanism of Dayuanyin in hypoxic pulmonary hypertension through NF-kappaB signaling pathway

Dayuanyin (DYY) has been shown to reduce lung inflammation in both coronavirus disease 2019 (COVID-19) and lung injury. This experiment was designed to investigate the efficacy and mechanism of action of DYY against hypoxic pulmonary hypertension (HPH) and to evaluate the effect of DYY on the protection of lung function. Animal welfare and experimental procedures are approved and in accordance with the provision of the Animal Ethics Committee of the Institute of Materia Medica, Chinese Academy of Medical Science. Male C57/BL6J mice were randomly divided into 4 groups: control group, model group, DYY group (800 mg.kg-1), and positive control sildenafil group (100 mg.kg-1). The animals were given control solvents or drugs by gavage three days in advance. On day 4, the animals in the model group, DYY group and sildenafil group were kept in a hypoxic chamber containing 10% +/- 0.5% oxygen, and the animals in the control group were kept in a normal environment, and the control solvent or drugs continued to be given continuously for 14 days. The right ventricular systolic pressure, right ventricular hypertrophy index, organ indices and other metrics were measured in the experimental endpoints. Meantime, the expression levels of the inflammatory factors in mice lung tissues were measured. The potential therapeutic targets of DYY on pulmonary hypertension were predicted using network pharmacology, the expression of nuclear factor kappa B (NF- kappaB) signaling pathway-related proteins were measured by Western blot assay. It was found that DYY significantly reduced the right ventricular systolic pressure, attenuated lung injury and decreased the expression of inflammatory factors in mice. It can also inhibit hypoxia-induced activation of NF- kappaB signaling pathway. DYY has a protective effect on lung function, as demonstrated by DYY has good efficacy in HPH, and preventive administration can slow down the disease progression, and its mechanism may be related to inhibit the activation of NF-kappaB and signal transducer and activator of transcription 3 (STAT3) by DYY.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance. © Wolters Kluwer Health, Inc. All rights reserved.

The infinity vaccine war: linguistic regularities and audience engagement of vaccine debate on Twitter

Purpose: As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers' tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences' engagement. Design/methodology/approach: Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement. Findings: In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers' tweets, the topics of "Child protection” and "COVID-19 situation” are positively predicting audiences' engagement. For anti-vaxxers, the topics of "Supporting Trump,” "Injured children,” "COVID-19 situation,” "Media propaganda” and "Community building” are more appealing to audiences. Originality/value: This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery. Peer review: The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186 © 2023, Emerald Publishing Limited.

Quantifying Mobility Perturbation in America's Cities during COVID-19: A Network-Based Approach

The pandemic of COVID-19 has caused severe disruptions in urban lives. Understanding and quantifying these disruptions is important to inform the development of targeted and effective measures to control the pandemic and its impact. One way of achieving this object is to measure the urban mobility perturbation caused by the pandemic. In this study, we built mobility-based networks for seven major metropolitan statistical areas (MSAs) across the United States in the years of 2019 and 2020, respectively. We quantified the disruptions of urban mobility by computing and comparing a set of network-based metrics before and during the pandemic. The proposed approach is able to uncover the impact of COVID-19 in cities and provides new insights into the resilience of cities when facing large-scale disasters.

Effects of Precipitation on the Air Quality Index, PM2.5 Levels and on the Dry Deposition of PCDD/Fs in the Ambient Air

The effects of 9 precipitation events in Suzhou City in Anhui Province, China, on the air quality index (AQI), PM2.5, and dry deposition flux of PCDD/Fs (polydibenzo-p-dioxins and polydibenzofurans) were investigated. A total of 7 precipitation events were positive contributes to the reduction of AQI;among them, the AQI were between 23 and 216, with an average of 75, the PM2.5 concentrations were between 5.0 and 169 mu g m-3, with an average of 25 mu g m-3, while the total-PCDD/F-TEQ dry deposition flux ranged from 149 to 1034 pg WHO2005-TEQ m-2 day-1 and averaged 315 pg WHO2005-TEQ m-2 day-1. By comparing the average AQI and PM2.5, respectively, during and after rainfall with that before rainfall, the results indicated that the average reduction fractions of AQI were 26% and 44%, respectively, while those of PM2.5 were 58% and 43%. In addition, the effect of precipitation on the average reduction fraction of total PCDD/F-TEQ dry deposition flux was 31%. However, in the other 2 AQI elevation events, the AQI were between 23 and 100, and averaged 51;when comparing the average AQI and PM2.5 concentrations, during and after the rain with that before the rain, the increases in AQI were 42% and 49%, respectively, while the increases in PM2.5 concentration were 26% and 29%, respectively. The above results show that, on the whole, rain and snow improved the air quality. This is because rainwater removes particles or dissolved gaseous pollutants from the atmosphere and brings aerosols to the ground. However, in some cases, the increase of source emissions and atmospheric vertical convection, the effect of precipitation or air humidity increased the AQI and elevated the concentration of PM2.5, and dry deposition flux of PCDD/Fs. The results of this study provide useful information for both scientific communities and air quality management.

Consumer Intention to Switch from Cash to Mobile Payment in Restaurants During and After Pandemic

During the Covid-19, more and more consumers complete transactions on mobile devices and enjoy their orders at home. However, it is unknown whether mobile payment will become normal when they return to restaurants. Results show that the perceived inconvenience of cash, the perceived convenience of mobile payment and deal proneness facilitate the intention of consumers to switch to mobile payments. The perceived security and privacy, perceived health risk, consumer innovativeness, and social influence have a direct bearing on consumer intent to switch to mobile payment and moderate switching intention. This study also provides theoretical and managerial implications. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Macroeconomic conditions and investment stimuli

Based on a calibrated real options model, this paper examines a tax-subsidy program offered by a government to stimulate corporate investment under business cycles. We derive and discuss optimal incentive policies for different states of the economy. We find that it is optimal for the government to offer a combination of tax cuts and lump-sum subsidy for stimulating levered firms' investment under business cycles. Furthermore, the government should adopt counter-cyclical tax-subsidy policy, namely a higher (lower) tax cuts and a larger (smaller) lump-sum subsidy during recessions (booms). In particular, we provide a possible explanation why many governments around the world have reduced and even implemented negative interest rates to stimulate the economy during the COVID-19 pandemic in 2020. Finally, our conclusions also predict that the break-even tax-subsidy program always provides effective investment stimulus under business cycles. © 2023 Elsevier Inc.

Privacy preserving via multi-key homomorphic encryption in cloud computing

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment. © 2023 Elsevier Ltd

Impact of short-term ambient air pollution exposure on the risk of severe COVID-19

Ecological studies suggested a link between air pollution and severe COVID-19 outcomes, while studies accounting for individual-level characteristics are limited. In the present study, we aimed to investigate the impact of short-term ambient air pollution exposure on disease severity among a cohort of 569 laboratory confirmed COVID-19 patients admitted to designated hospitals in Zhejiang province, China, from January 17 to March 3, 2020, and elucidate the possible biological processes involved using transcriptomics. Compared with mild cases, severe cases had higher proportion of medical conditions as well as unfavorable results in most of the laboratory tests, and manifested higher air pollution exposure levels. Higher exposure to air pollutants was associated with increased risk of severe COVID-19 with odds ratio (OR) of 1.89 (95% confidence interval (CI): 1.01, 3.53), 2.35 (95% CI: 1.20, 4.61), 2.87 (95% CI: 1.68, 4.91), and 2.01 (95% CI: 1.10, 3.69) for PM2.5, PM10, NO2 and CO, respectively. OR for NO2 remained significant in two-pollutant models after adjusting for other pollutants. Transcriptional analysis showed 884 differentially expressed genes which mainly were enriched in virus clearance related biological processes between patients with high and low NO2 exposure levels, indicating that compromised immune response might be a potential underlying mechanistic pathway. These findings highlight the impact of short-term air pollution exposure, particularly for NO2, on COVID-19 severity, and emphasize the significance in mitigating the COVID-19 burden of commitments to improve air quality. © 2022

Assessing the Impact of the Covid-19 on the US Airline Market Using an Instrumental Variable and a DID Empirical Strategy

In this paper, we examine the affects of COVID-19 and related policies on the aviation industry. Using archival data from the John Hopkins Coronavirus Resource Center, Department of Transportation Statistics, and the COVID-19 U.S. State Policy database, and an instrumental variable and a difference-in-differences empirical strategy, we find that COVID-19 severity is negatively correlated with both the mean ticket price and the number of passengers for the four major airlines in the US, and that the implementation of COVID-19 control policies is positively correlated with the mean ticket price, while negatively correlated with the number of passengers. © 2022 ACM.

Travel route planning method to avoid epidemic hot-spots in the post-epidemic era

The Chinese government adhered to the "dynamic clearance” epidemic prevention strategy from August 2021 to December 7, 2022, during the post-epidemic era (this study started in March 2022 and ended in September 2022). People are gradually resuming their daily routines, and demand for travel is rising again. Nonetheless, the epidemic occasionally breaks out on a smaller scale, causing social concern. As a social reaction, the essential issue is how to avoid COVID-19 hot-spots effectively by offering secure travel options for local residents who tend to travel privately. Two travel route planning models are proposed to avoid COVID-19 hot-spots based on the invalid road sections which are affected by epidemic. Specifically, the static model aims at generating the shortest travel distance after detours, with the constraint of avoiding COVID-19 hot-spots;the dynamic model takes real-time data into account, which includes epidemic risk levels, road grades, and real-time traffic information on road selection. Shenzhen, China, is illustrated as an example of the research area in this paper. To assess the effectiveness and efficiency of the suggested approaches, data regarding the road network, the prevalence of epidemics, and traffic congestion are collected. The experimental results demonstrate that 1) the proposed two route planning models can effectively bypass areas with high levels of epidemic risk. 2) The static route planning model increases the travel distance by 12.24% and 13.03%, while the dynamic route planning model increases the travel distance by 24.33% and 27.69% compared with the conventional shortest route, given the same origin and destination and the same impact radii of the COVID-19 hot-spots (300 and 500 m respectively). When taking detour routes to avoid COVID-19 hot-spots, the average increase in trip distance does not surpass 50%, which is acceptable psychologically for travelers. 3) The static travel route planning model is suitable for the severe epidemic situation for it can strictly avoid the epidemic hot-spots;The dynamic travel route planning model is applicable to the situation where the epidemic situation is relatively mild. Ultimately, the route planning models can be utilized to develop a framework to provide travelers with detour options, which would make a practical difference to ensure travelers' safety during traveling and contribute to preventing the spread of the epidemic. © 2023 The Author(s)

Research progress on the analysis of active ingredients and elements in Fritillaria ussuriensis Bulbus

Fritillaria ussuriensis Bulbus, a genuine medicinal material of Northeast China, is the dry bulb of Fritillaria ussuriensis Maxim. It contains various active ingredients, such as alkaloids, alkaloids glycosides, adenosines, polysaccharides, and trace elements . It has antitussive, eliminating phlegm, antiasthmatic, antiulcer, antiplatelet aggregation, and anti-inflammatory. The qualitative and quantitative analysis of alkaloids, polysaccharides, nucleosides, and trace elements in Fritillaria ussuriensis Bulbus were reviewed, which is helpful for its cultivation and accurate application, and would provide a new choice for the treatment of coronavirus disease 2019 (COVID-19). © 2022

Urticaria after SARS-CoV-2 vaccination.

Background: Infections are considered the most common known trigger of acute urticaria. Vaccinations can mimic infections and are also able to trigger urticaria. Method(s): This paper describes four exemplary patients with urticaria after SARSCoV-2 vaccination and discusses the occurrence of this adverse event with recent findings from the literature. Result(s): After SARS-CoV-2 vaccination, both acute (case 1) and chronic spontaneous urticaria (CSU) may occur for the first time (case 2) but a stable well-adjusted CSU may also worsen (case 3) or a pre-existing CSU may recur (case 4). The underlying pathomechanisms are not fully elucidated but activation of the immune system by vaccination seems plausible, similar to infection-triggered urticaria. Subsequent vaccinations are recommended even in the presence of a positive history of urticaria after SARS-CoV-2 vaccination and is often well tolerated;prophylactic administration of antihistamines should be considered. Allergy to vaccine components such as polyethylene glycol (PEG), which can lead to anaphylaxis, is comparatively rare. In cases of urticaria after SARS-CoV-2 vaccination, an accurate history allows risk assessment and provides the indication for further allergy diagnostic workup. Conclusion(s): Various forms of urticaria are a relatively common cutaneous side effect of SARS-CoV-2 vaccination in cases of pre-existing predisposition. They are not a contraindication to further vaccination. There is no need for further allergy diagnosis in the majority of cases.Copyright ©2023 Dustri-Verlag Dr. K. Feistle.

[Progress on specific and non-specific immune effects of Bacille Calmette-Guerin].

Currently, Bacille Calmette-Guerin(BCG) is still the only admitted vaccine to prevent tuberculosis around the world. The target population is infants and children, but its protective efficacy is limited. As more and more studies have shown that re-vaccination with BCG protects against tuberculosis in adults, BCG can also induce non-specific immunity against other respiratory diseases and some chronic diseases by training immunity, especially the immune effects against COVID-19. At present, the epidemic of COVID-19 has not been effectively contained, and it is worth considering whether BCG vaccine can be used as an intervention to prevent COVID-19. The WHO and China do not have a policy to support BCG revaccination, and as more and more BCG vaccines are discovered, whether selective revaccination can be carried out in some high-risk populations and whether the vaccine can be used more widely have led to intense discussions. This article reviewed the effects of specific immunity and non-specific immunity of BCG on tuberculosis and non-tuberculous diseases.