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Conferences are an integral part of academia, providing researchers with the opportunity to collaborate and disseminate their research. Traditionally these conferences have been in-person, but COVID-19 restrictions and growing recognition of systematic barriers have led many academic societies to reimagine the conference format. Social media platforms are often used to promote and broadcast in-person academic conferences, so with the development of online conferences they have naturally become hosting platforms. For example, during the peak of COVID-19 restrictions, when in-person meetings were not possible, Twitter conferences, like the first Global Animal Behaviour Conference (#AnimBehav2021), provided a much needed platform to collaborate and share science. Recently, conferences have returned to an in-person format. In addition, a change in ownership of Twitter led to widespread restructuring of the Twitter workforce and misinformation management policies that resulted in a massive exodus of academics from the platform. During these changes, we organized and held the second Global Animal Behaviour Twitter Conference (#AnimBehav2023) in January 2023. In this paper, we report (1) how engagement in the Twitter conference changed as a result of return to in-person conferences and changes in Twitter ownership, (2) how the community views online conferences in general and Twitter conferences in particular and (3) reflect on how online conferences can be organized moving forward. Given the widespread benefits of and need for online conferences, we urge academic societies and researchers to continue systematically exploring these alternative conference formats to ensure inclusivity and accessibility in academic spaces. • We evaluated the reach of the #AnimBehav2023 Twitter Conference. • We assessed engagement after in-person conferences returned and Twitter changed. • Presenting at a Twitter conference has greater reach than in-person conferences. • Global participation remained high despite alternative options and Twitter changes. • Despite continued enthusiasm, Twitter may no longer be the suitable platform. [ABSTRACT FROM AUTHOR]

Nonpharmaceutical interventions (NPI) are an important tool for countering pandemics such as COVID-19. Some are cheap; others disrupt economic, educational, and social activity. The latter force governments to balance the health benefits of reduced infection and death against broader lockdown-induced societal costs. A literature has developed modeling how to optimally adjust lockdown intensity as an epidemic evolves. This paper extends that literature by augmenting the classic SIR model with additional states and flows capturing decay over time in vaccine-conferred immunity, the possibility that mutations create variants that erode immunity, and that protection against infection erodes faster than protecting against severe illness. As in past models, we find that small changes in parameter values can tip the optimal response between very different solutions, but the extensions considered here create new types of solutions. In some instances, it can be optimal to incur perpetual epidemic waves even if the uncontrolled infection prevalence would settle down to a stable intermediate level. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a public health concern worldwide. Ensitrelvir (S-217622) has been evaluated as an antiviral treatment for COVID-19, targeting SARS-CoV-2 3C-like protease (3CLpro). Ensitrelvir has been reported to have comparable antiviral activity against some of the SARS-CoV-2 variants: alpha, beta, gamma, delta, and omicron (BA.1.18). In this paper, we describe that ensitrelvir is effective against newly emerging SARS-CoV-2 variants and globally prevalent 3CLpro mutations. Ensitrelvir exhibited comparable antiviral activity against SARS-CoV-2 variants, including recently emerging ones: omicron (BA1.1, BA.2, BA.2.75, BA.4, BA.5, BQ.1.1, XBB.1, and XE), mu, lambda, and theta. Genetic surveillance of SARS-CoV-2 3CLpro, the target of ensitrelvir, was conducted using a public database and identified 11 major 3CLpro mutations circulating globally (G15S, T21I, T24I, K88R, L89F, K90R, P108S, P132H, A193V, H246Y, and A255V). The 3CLpro mutation from proline to histidine at amino acid position 132 was especially identified in the omicron variant, with prevalence of 99.69%. Enzyme kinetic assay revealed that these 3CLpro mutants have enzymatic activity comparable to that of the wild type (WT). Next, we assessed the inhibitory effect of ensitrelvir against mutated 3CLpro, with it showing inhibitory effects similar to that against the WT. These in vitro data suggest that ensitrelvir will be effective against currently circulating SARS-CoV-2 variants, including omicron variants and those carrying 3CLpro mutations, which emerging novel SARS-CoV-2 variants could carry. • Ensitrelvir showed comparable antiviral activity against various SARS-CoV-2 variants. • Genetic surveillance of SARS-CoV-2 3CLpro revealed 11 major 3CLpro mutations. • The IC 50 s of ensitrelvir against these mutants were similar to those of the wild type. [ABSTRACT FROM AUTHOR]

The impact investing literature largely focuses on private equity investing and overlooks the investments made through debt financing that actually dominate the market. To address this research gap, this paper investigates whether impact financing is associated with financial benefits. By using COVID-19 as an exogenous shock to China's stock market, this paper applies fixed effects panel data analysis with a difference-in-differences research design to provide robust empirical outcomes. The results reveal those financial institutions that better integrated environmental impacts into their financing process experienced positive stock return changes in response to the shock. This study answers the question of how well an impact scales. The findings suggest that impact financing is an effective model, as the impacts incorporated in the debt can be scaled up compared to impact investing funds with low volumes. Impact financing has enormous potential for financial institutions to engage in the green transition since they can derive pecuniary utility while delivering environmental impacts. The revelation of financial benefits also contributes to overcoming the lack of knowledge about impact financing and helps to remove the barriers that advance industry growth. • This paper studies whether impact financing is associated with financial benefits. • Financial firms that better integrate environmental impact in financing have positive stock return changes during COVID-19. • Financial firms can derive pecuniary utility from pursuing impact financing. • The findings suggest that impact financing can be an effective model to bring impact to a substantial scale. [ABSTRACT FROM AUTHOR]

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Is there a "missing device" for respiratory personal protection? Does it exist an easy-to-use device, allowing extensive use in everyday settings by the population, maximizing tolerability and low visual and physical invasiveness protecting from a wide range of threats including airborne pathogens, hence including the particle range of fine and ultrafine particles? Looking at the recent past, in the urgency of finding ready-to-use solutions for the respiratory protection of the population during the outbreak of the SARS-CoV-2 pandemic, devices for occupational safety have been used, such as filtering face masks. These are devices intended for workers operating during work shifts in environments characterized by potential high risk, known a priori, often directly sensible; this makes wearers motivated to tolerate discomfort for a given period to face a localized risk, and safety managers determined to supervise compliance with usage specifications. Their use by general population has implied known shortcomings, such as weak compatibility with relational work and activities, low tolerability during prolonged use, low compliance with the proper use of the device, all of this lessening actual protection. The need for a new perspective has emerged, targeting effectiveness in whole daily life, rather than punctual efficacy. Nasal filters are promising candidates to protect individuals throughout the day during the most varied activities, but they lack a systematic definition as a device and as a product; it follows that the high complexity needed to reach an effective performance envelop is generally underestimated. By reviewing available literature, the present paper draws on the experience from the pandemic and infers systematic product specifications and characterization methods for a new, effective personal respiratory protection device; these specifications are compared with the stringent constraints associated with the endonasal applications and, based on air filtration state of the art, quantifies the need for technology disruption and outlining possible new development paths. • The SARS-COV-2 pandemic indicated that an adequately performing nasal filter has the potential to play a protective role in the population's daily life. • A nasal filter poses unique technological challenges due to its miniaturisation. • To trap allergens, pathogens and harmful particulate matter entering the nose, it is necessary to filter out particles classified as fine and ultrafine. • Fibrous mesh filters are inadequate to filter fine and ultrafine particles in a nasal filter as small as a nostril, due to their low filtration capacity and high breathing resistance. • The development of effective nasal filters in the field of fine and ultrafine particles requires the adoption of new technologies. [ABSTRACT FROM AUTHOR]

This paper estimates the mortality impacts of ozone pollution in China and the moderating effects of two possible adaptation strategies. Using an instrument variable constructed from ozone concentrations of nearby upwind cities, we find that ozone pollution significantly increases all-cause, cardiovascular, and respiratory mortality. Healthcare service provision significantly decreases the impacts of ozone pollution on all-cause and cardiovascular mortality, but does not moderate the impact on respiratory mortality. The impact of ozone on RES mortality declines after COVID-19. Healthcare service provision also reduces the distributional impact of ozone across the elderly and younger groups. Projection shows that climate change would induce mortality costs of 0.08% of China's GDP through increasing ozone pollution. [ABSTRACT FROM AUTHOR]

The rapid spread of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emphasized the importance of understanding and adapting to the indoor remediation of transmissible diseases to decrease the risk for future pandemic threats. While there were many precautions in place to hinder the spread of COVID-19, there has also been a substantial increase of new research on SARS-CoV-2 that can be utilized to further mitigate the transmission risk of this novel virus. This review paper aims to identify the building parameters of indoor spaces that could have considerable influence on the transmission of SARS-CoV-2. The following building parameters have been identified and analyzed, emphasizing their link with the indoor transmission of SARS-CoV-2: temperature and relative humidity, temperature differences between rooms, ventilation rate and access to natural ventilation, occupant density, surface type and finish, airflow direction and speed, air stability, indoor air pollution, central air conditioning systems, capacity of air handling system and HVAC filter efficiency, edge sealing of air filters, room layout and interior design, and compartmentalization of interior space. This paper also explains the interactions of SARS-CoV-2 with indoor environments and its persistence. Furthermore, the modifications of the key building parameters have been discussed for controlling the transmission of SARS-CoV-2 in indoor spaces. Understanding the information provided in this paper is crucial to develop effective health and safety measures that will aid in infection prevention. [ABSTRACT FROM AUTHOR]

This paper employs systematic literature network analysis, including a literature review and bibliometric network analysis, to explore the COVID-19 and air pollution literature. A total of 1208 relevant documents from the Scopus database were analyzed using VOSviewer and SciMAT to examine author, keyword, and country interconnections. The paper addresses three research questions: (1) the latest studies on COVID-19 and air pollution, (2) influential authors, documents, and sources in the field, and (3) the study's findings as a roadmap for future research. Visual representations of author and country networks highlight influential entities based on citation rate. Keyword cluster analysis via VOSviewer summarizes connections based on link strength. The strategic diagram generated by SciMAT offers insights into future research directions in specific thematic keywords: (1) air pollution, (2) nitrogen dioxide, (3) epidemiology, and (4) atmospheric aerosol. This analysis enhances understanding of COVID-19 and air pollution and guides future research endeavors. • Conducts a systematic review of the literature on COVID-19 and air pollution. • Utilizes bibliometric network analysis on a selection of documents. • Collects 1208 Scopus database documents from 2020 to 2023. • Identifies themes or keywords for further study on COVID-19 and air pollution. [ABSTRACT FROM AUTHOR]

The world has witnessed the cruelty of COVID-19 disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The association of COVID-19 with other secondary and bacterial co-infections has tremendously contributed to lung infections. An increased probability of having a secondary lung infection was observed among the post-COVID patients. The treatment of antibiotics has ameliorated the mortality rate. However, the stewardship of antibiotic treatment was linked to increased organ failure. Therefore, the paper discusses the interactions between the virus and host through the ACE2 receptors that contribute to COVID-19 development. Furthermore, the paper provides an invaluable compendium history of SARS-CoV-2 genomic composition. It revolves around most classes of antibiotics used to treat COVID-19 disease and post-COVID lung infections with the complete mechanism. This binds with the exertion of the antibiotics for bacterial infection associated with COVID-19 patients and how beneficial and effective responses have been recorded for the treatment. The application of nanotechnology and possible approaches of nanomedicines is also discussed to its potential usage. • COVID-19 with other secondary and bacterial co-infections has contributed to lung infections. • The treatment of antibiotics has ameliorated the mortality rate. However, the stewardship of antibiotic treatment was linked to increased organ failure. • Most classes of antibiotics are used to treat COVID-19 disease and post-COVID lung infections with the complete mechanism. • The application of nanotechnology and possible approaches of nanomedicines is also discussed to its potential usage. [ABSTRACT FROM AUTHOR]

• A novel model called UGDAS is proposed for the biomedical-domain abstractive summarization task. • The model is consisted of a graph-network based denoiser and an auto-regressive generator, and it is more effective to capture the sentence relationship. • The denoiser utilizes domain knowledge to represent the rich information of the sentence in the biomedical domain. • The ranking process is optimized by the integration of sentence position information. • Our model achieves the state-of-the-art result on a recently-introduced CORD-19 dataset and outperforms the related abstractive models on the PubMed dataset. Abstractive summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset. [ABSTRACT FROM AUTHOR]

• An average case fatality ratio over the first six months after the outbreak of COVID-19 in Poland was 5.43% (95% CI: 5.08% – 5.73%), while among hospitalized cases 16.9% (15.8% – 17.9%). • The case fatality rate had a decreasing trend in time starting from 7.83% (7.08% – 8.61%) during first eight weeks after the outbreak (March – April), to reach 2.52% (2.06% – 3.02%) during the summer season (July – August). • Recent medical care as well as the identification of the source contact, independently of the age, sex and comorbidities, significantly impact an individual mortality risk. After more than one and a half year since the COVID-19 pandemics outbreak the scientific world is constantly trying to understand its dynamics. In this paper of the case fatality rates (CFR) for COVID-19 we study the historic data regarding mortality in Poland during the first six months of pandemic, when no SARS-CoV-2 variants of concern were present among infected. To this end, we apply competing risk models to perform both uni- and multivariate analyses on specific subpopulations selected by different factors including the key indicators: age, sex, hospitalization. The study explores the case fatality rate to find out its decreasing trend in time. Furthermore, we describe the differences in mortality among hospitalized and other cases indicating a sudden increase of mortality among hospitalized cases at the end of the 2020 spring season. Exploratory and multivariate analysis revealed the real impact of each variable and besides the expected factors indicating increased mortality (age, comorbidities) we track more non-obvious indicators. Recent medical care as well as the identification of the source contact, independently of the comorbidities, significantly impact an individual mortality risk. As a result, the study provides a twofold insight into the COVID-19 mortality in Poland. On one hand we explore mortality in different groups with respect to different variables, on the other we indicate novel factors that may be crucial in reducing mortality. The later can be coped, e.g. by more efficient contact tracing and proper organization and management of the health care system to accompany those who need medical care independently of comorbidities or COVID-19 infection. [ABSTRACT FROM AUTHOR]

• The M−CDC effectively integrates the advantages of CRISPR/Cas12a, magnetic beads-based separation, and AuNP bioprobes. • M−CDC is an easy-to-operated, sensitive, and specific biosensing platform for nucleic acid assay. • The M−CDC has a great potential to be used for large-scale population SARS-CoV-2 screening in resource-limited areas. The construction of a rapid, simple, and specific nucleic acid detection platform is of great significance to the control of the large-scale spread of infectious diseases. We have recently established a magnetic pull-down-assisted colorimetric method based on the CRISPR/Cas12a system (termed M−CDC), which effectively integrates the advantages of CRISPR/Cas12a, magnetic beads-based separation, and AuNP bioprobe to provide a simple and specific biosensing platform for nucleic acid assay. The M−CDC method is compatible with point-of-care testing and enables the detection of nucleic acid samples in less than an hour without relying on expensive and complex instruments. In this paper, step-by-step instructions for M−CDC assay, including recombinase polymerase amplification (RPA)/reverse transcription-polymerase chain reaction (RT-RPA) of DNA or RNA, Cas12a-mediated target recognition and cleavage, and subsequent magnetic beads-mediated colorimetric readouts are provided. In addition, the protocol for the expression and purification of Lachnospiraceae bacterium -Cas12a (LbCas12a) protein, the design and synthesis of high-efficient crRNA, and the preparation of AuNP bioprobe are also offered. [ABSTRACT FROM AUTHOR]

Rapid detection of nucleic acids is essential for clinical diagnosis of a wide range of infectious and non-infectious diseases. CRISPR-based diagnostic platforms are well-established for rapid and specific detection of nucleic acids but suffer from a low detection sensitivity without a target pre-amplification step. Our recently developed detection system, called CRISPR-ENHANCE, employs engineered crRNAs and optimized conditions to achieve a significantly higher sensitivity and enable femtomolar levels of nucleic acid detection even without target pre-amplification. Using the CRISPR-ENHANCE platform and following the methodology detailed in this paper, nucleic acid detection for low copy numbers can be achieved in less than an hour through either a fluorescence-based detection or a lateral flow assay. The step-by-step instructions provided, in addition to describing how to perform both assays, incorporate details on a LAMP/RT-LAMP-based target amplification step to enable detection of RNA, ssDNA and dsDNA. Furthermore, a protocol for in-house expression and purification of LbCas12a using CL7/lm7-based affinity chromatography, which has been used to achieve a high yield and purity of the enzyme in a single-step, is provided. [ABSTRACT FROM AUTHOR]

• The Spatio-temporal correlations improve the precision of COVID-19 lesion detection and segmentation. • The semantic segmentation in reasonable subareas can effectively mitigate the estimation matters caused by tiny lesions. • Our methods showed excellent robustness, even on samples with severe image augmentations. In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Presented a systematic approach to learn from imbalanced set of bio-medical images. • Developed a practical "survival of the fittest" approach for hyperparameter tuning of models. • Proposed a framework to use leftout imbalanced data for pseudo-testing purpose. • Provided a publicly available chest X-ray dataset on the Kaggle platform. • Outperformed global competitors in terms of F1 and recall scores on the given dataset. The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithms, methodology, as well as the source of medical image data, must be strictly scrutinized. As the COVID-19 pandemic has been gripping much of the world recently, there has been much efforts gone into developing affordable testing for the masses, and it has been shown that the established and widely available chest X-rays (CXR) images may be used as a screening criteria for assistive diagnosis purpose. Thanks to the dedicated work by various individuals and organizations, publicly available CXR of COVID-19 subjects are available for analytic usage. We have also provided a publicly available CXR dataset on the Kaggle platform. As a case study, this paper presents a systematic approach to learn from a typically imbalanced set of CXR images, which consists of a limited number of publicly available COVID-19 images. Our results show that we are able to outperform the top finishers in a related Kaggle multi-class CXR challenge. The proposed methodology should be able to help guide medical personnel in obtaining a robust diagnosis model to discern COVID-19 from other conditions confidently. [ABSTRACT FROM AUTHOR]

• Network-based approach improve SARS-CoV-2 drug repositioning performance • Aggregating local network information can promote non-linear feature interaction • Graph convolutional network utilize drug-disease-target reduce the data sparsity • Doxycycline, Cefuroxime, and Erythromycin can be used for SARS-CoV-2 treatment The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease. [ABSTRACT FROM AUTHOR]

In the pre-clinical phase, SARS-CoV-2 vaccines were tested in animal models, including exposure trials, to investigate protection against SARS-CoV-2. These studies paved the way for clinical development. The objective of our review was to provide an overview of published animal exposure results, focussing on the capacity of vaccines to reduce/prevent viral shedding. Using Medline, we retrieved eighteen papers on eight different vaccine platforms in four animal models. Data were extracted on presence/absence of viral RNA in nose, throat, or lungs, and neutralizing antibody levels in the blood. All vaccines showed a tendency of reduced viral load after exposure. Particularly nasal swab results are likely to give an indication about the impact on virus excretion in the environment. Similarly, the reduction or prevention of viral replication in the bronchoalveolar environment might be related with disease prevention, explaining the high efficacy in clinical trials. Although it remains difficult to compare the results directly, the potential for a strong reduction of transmission was shown, indicating that the animal models predicted what is observed in the field after large scale human vaccination. This merits further attention for standardization of exposure experiments, with the intention to speed up future vaccine development. • All SARS-CoV-2 vaccine candidates reduce viral load in different organs in animal models. • Viral load in nasal swab results provide an indication about the impact of vaccines on virus excretion. • Reduced viral replication in the bronchoalveolar environment might be related with disease prevention. • Standardization of exposure experiments may help to speed up future vaccine development. [ABSTRACT FROM AUTHOR]

Conflicts are inevitable, and so are refugees. Due to conflicts in Ukraine, the global refugee population has reached new highs. As people continue to flee Ukraine amid the ongoing pandemic in droves, their exposure to COVID-19 and infectious diseases that are common among the refugee population, such as tuberculosis, is on the rise as well. Also factoring in the fact that Ukraine has a large population living with communicable diseases like HIV and hepatitis C, along with other non-communicable conditions like diabetes and cancer, there is a pronounced need to protect these refugees and local residents from potential public health crises. In this paper, we investigate the challenges that health and government officials face in addressing refugees' health needs and preferences. Furthermore, we discuss the imperative to provide timely and effective health services to refugees, such as psychoneuroimmunology-based interventions that could help address refugees' multifactorial and multifaceted health needs and requirements. While conflicts are inevitable, public health crises are not. In light of the renewed imperative to safeguard shared humanity and solidify global solidarity, collaborative actions are needed to ensure fair, kind, and true public health environments are available to refugees of the current conflict and beyond. [ABSTRACT FROM AUTHOR]

Just weeks away from the Opening Ceremony of the Beijing 2022 Winter Olympics, the United States, followed by Australia, the United Kingdom, and Canada, has declared a diplomatic boycott of the Games. A diplomatic boycott stipulates that while government officials of these countries will not attend the event, the athletes' scheduled attendance will largely remain intact. An unintended consequence of the boycotts is that they force the attending athletes to cope with the stress and distress associated with the 2022 Winter Olympics in an unfamiliar environment on their own. It is important to underscore that many of the challenges the athletes could face amid the Games are either deep-rooted or unprecedented, ranging from stressors fuelled by the nonstop media reports, the competitions, to the Omicron scares. These insights combined, in turn, underscore the imperative for effective and preemptive mental health support for Olympic athletes. To shed light on the issue, this paper highlights the reasons why timely solutions are needed to adequately safeguard Olympic athletes' mental health and overall wellbeing, and underlines promising technology-based solutions that can be cost-effectively designed and developed for the athletes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3- d ]pyrimidines. • Computer-aided drug design (CADD) approaches for combat COVID-19. • Novel non-covalent inhibitors of the active site and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro. • Potential COVID-19 drug candidates. • Molecular docking and in silico ADMET studies. • Molecular docking (protein‒ligand) studies on the human body temperature-dependent MPro protein contain zincII (ZnII) ion between His41/Cys145 catalytic dyad in the active site. In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3- d ]pyrimidine-2,4(1 H ,3 H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert -butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl 2 •8H 2 O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3- d ]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (MPro) and papain-like protease (PLPro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3- d ]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3- d ]pyrimidine-2,4(1 H ,3 H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein–ligand-type molecular docking studies on the human body temperature-dependent MPro protein that surprisingly contains zincII (ZnII) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 MPro inhibitors, is performed for the first time in this paper, to the best of our knowledge. [ABSTRACT FROM AUTHOR]

After the new Coronavirus disease (COVID-19) emerged in the end of January 2020 in Germany, a large number of individuals suffered from severe symptoms and eventually needed intensive care in hospitals. Due to the rapid spread of the disease, the number of deceased individuals increased as well, which is a motivation to prevent as many new infections as possible. Therefore, the knowledge about the current evolution of the virus spread is crucial to predict its future behavior and to react with suitable interventions. In this paper, the evolution of the COVID-19 pandemic in Germany is forecasted by a network-based inference method, in which the interactions of individuals are taken into account using a contact matrix. Then the results are compared to the predictions without considering a contact matrix as well as to the logistic regression, which shows the advantage of incorporating the contact matrix. Furthermore, the basic reproduction number of the pandemic in Germany using a neural network approach is estimated and used for further predictions of the evolution of COVID-19 in Germany. In order to mathematically model the different compartments of the population in the considered regions, the classical SIR model is employed. In this work, we deploy the LASSO (Least Absolute Shrinkage and Selection Operator) for the unknown parameter estimation. Furthermore, we calculate and illustrate the MAPE (Mean Absolute Percentage Error) of the estimations to show the accuracy of the predictions. The results include model parameter estimation and model validation, as well as the outbreak forecasting using network-informed algorithms. Our findings show that the network-inference based approach outperforms the logistic regression as well as the neural network approach and the SIR model calibration without a contact network. Furthermore according to the results, the network-inference based approach is particularly suitable for short- to mid-term predictions, even when there is not much information about the new disease. Moreover, the predictions based on the estimation of the reproduction number in Germany can yield more reliable results with increasing the availability of data, but could not outperform the network-inference based algorithm. • The evolution of the COVID-19 pandemic in Germany and Bavaria state is forecasted. • The approach takes into account the interactions of individuals via a contact matrix. • The Mean Absolute Percentage Error (MAPE) of the estimation is calculated. • The prediction results are compared with the true data, NNs and logistic regression. • The predictions outperform the results of estimations without contact matrix. • The network-based approach is suitable for short- to mid-term predictions. • The predictions are informative for decision-makers to set protective measures. [ABSTRACT FROM AUTHOR]

Understanding the relationship between social factors and persistent COVID-19 health outcomes, such as onset of a disability after a SARS-CoV-2 (the virus that causes COVID-19) infection, is an increasingly important public health issue. The purpose of this paper is to examine associations between social vulnerability and new onset of a mobility disability post-COVID-19 diagnosis. We used data from the Michigan COVID-19 Recovery Surveillance Study, a population-based probability survey of adults with PCR-confirmed SARS-CoV-2 infection in Michigan between January 2020–May 2022 (n = 4295). We used the Minority Health Social Vulnerability Index (MHSVI), with high county-level social vulnerability defined at or above the 75th percentile. Mobility disability was defined as new difficulty walking or climbing stairs. We regressed mobility disability on the overall MHSVI, as well as sub-themes of the index (socioeconomic status, household composition/disability, minority and language, housing type, healthcare access, and medical vulnerability), using multivariable logistic regression, adjusting for age, race, sex, education, employment, and income. Living in a county with high (vs. low) social vulnerability was associated with 1.38 times higher odds (95% confidence interval [CI]:1.18–1.61) of reporting a new mobility disability after a COVID-19 diagnosis after adjustment. Similar results were observed for the socioeconomic status and household composition/disability sub-themes. In contrast, residents of highly racially diverse counties had lower odds (odds ratio 0.74, 95% CI: 0.61, 0.89) of reporting a new mobility disability compared to low diversity counties. Mitigating the effects of social vulnerabilities requires additional resources and attention to support affected individuals. • After a COVID-19 infection: Social vulnerability is associated with higher odds of new mobility disability. • Socially vulnerable housing conditions contribute to the association. • High area concentration of racial minorities had lower odds of mobility disability. [ABSTRACT FROM AUTHOR]

Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the return connectedness and multiscale spillovers between the Clean Energy Index and the grain commodity market around COVID-19. Using daily data from January 4, 2017 to July 1, 2022, a time-varying parametric vector autoregressive (TVP-VAR) connectedness approach is first used to reveal connectedness patterns before and during COVID-19. We further used Baruník and Křehlík (2018)'s frequency domain spillover method to assess connectedness in different domain horizons. Our results show spillover effects over time and frequency, with COVID-19 significantly affecting the connectedness of the whole system. Dynamic connectedness peaks significantly after sudden bursts of COVID-19, validating the reported uncertainty. We also documented higher spillover levels in the short term than in the medium and long term. In addition, We find that (i) most clean energy indices are positively affected by the COVID-19 outbreak; (ii) the NASDAQ OMX Bio/Clean Fuels Index and NASDAQ OMX Geothermal Index send spillovers to all grain commodities throughout the sample period, while the WilderHill Clean Energy Index and NASDAQ OMX Wind Energy Index indices are the largest recipients of spillovers from other markets regardless of time horizon; (iii) the OMX Bio/Clean Fuels and OMX Geothermal Energy indices dominate spillover shocks to grain commodity markets. This is the first study to analyse the connectedness and time-frequency dynamics of returns in the green energy index and the grain commodity market. These results provide valuable insights to investors and key policymakers, especially at a time of more significant uncertainty. • Examine the return connectedness between the Clean Energy Index and the grain commodity market. • Dynamic connectedness peaks significantly after sudden bursts of COVID-19. • Higher spillover levels in the short term than in the medium and long term. • OMX Bio/Clean Fuels and OMX Geothermal Energy indices dominate spillover shocks to grain commodity markets. [ABSTRACT FROM AUTHOR]

Even reaching the end of the year 2022, there is still a controversy on the origin of the SARS-CoV-2 virus. This Virtual Special Issue (VSI), focused on the "Scientific evidence on the origin of SARS-CoV-2", was launched some months ago with the aim of stimulating the submission of new high quality scientific research papers on the matter, to shed light on it. As indicated in the call for papers, the Editors involved in the VSI were aware of the difficulties of presenting concluding facts on that issue, however, bearing in mind that some teams of researchers had started investigations regarding this subject, a VSI like this (searching for stimulating the scientific controversy while requiring scientific evidence), could help to elucidate complicated aspects, going a step ahead in this way. The Editors made a call encouraging interested teams of researchers having solid results to submit high quality manuscripts dealing with this crucial theme. We thought -and we still think-that it is of maximum interest for the scientific community, as well as for the whole society, now and probably for the future. The VSI have received 50 submissions, which could be considered a limited number highlighting the difficulties of elaborating new high-quality manuscripts providing solid evidence on the matter. After a careful peer-review, those manuscripts considered to reach the highest scientific value were accepted for publication. The Editors think that the set of papers included in this VSI constitute interesting and high-quality contributions, providing further scientific knowledge on this issue. In this editorial piece, the Editors make some comments on the papers published, including some additional reflections. [ABSTRACT FROM AUTHOR]

More than two and a half years have already passed since the first case of COVID-19 was officially reported (December 2019), as well as more than two years since the WHO declared the current pandemic (March 2020). During these months, the advances on the knowledge of the COVID-19 and SARS-CoV-2, the coronavirus responsible of the infection, have been very significant. However, there are still some weak points on that knowledge, being the origin of SARS-CoV-2 one of the most notorious. One year ago, I published a review focused on what we knew and what we need to know about the origin of that coronavirus, a key point for the prevention of potential future pandemics of a similar nature. The analysis of the available publications until July 2021 did not allow drawing definitive conclusions on the origin of SARS-CoV-2. Given the great importance of that issue, the present review was aimed at updating the scientific information on that origin. Unfortunately, there have not been significant advances on that topic, remaining basically the same two hypotheses on it. One of them is the zoonotic origin of SARS-CoV-2, while the second one is the possible leak of this coronavirus from a laboratory. Most recent papers do not include observational or experimental studies, being discussions and positions on these two main hypotheses. Based on the information here reviewed, there is not yet a definitive and well demonstrated conclusion on the origin of SARS-CoV-2. • The origin of SARS-CoV-2 is not established, but most papers point out to a zoonotic origin of this coronavirus. • An important number of available papers on the origin of SARC-CoV-2 are not experimental studies. • The hypothesis of an unnatural origin of SARS-CoV-2 cannot be yet scientifically discarded. [ABSTRACT FROM AUTHOR]

Wastewater monitoring as a public health tool is well-established and the SARS-CoV-2 (COVID-19) pandemic has seen its widespread uptake. Given the significant potential of wastewater monitoring as a public health surveillance and decision support tool, it is important to understand what measures are required to allow the long-term benefits of wastewater monitoring to be fully realized, including how to establish and/or maintain public support. The potential for positive SARS-CoV-2 detections to trigger enforced, community-wide public health interventions (e.g., lockdowns and other impacts on civil liberties) further emphasises the need to better understand the role of public engagement in successful wastewater-based monitoring programs. This paper systematically reviews the processes of building and maintaining the social license to operate wastewater monitoring. We specifically explore the relationship between different stakeholder communities and highlight the information and actions that are required to establish a social license to operate and then prevent its loss. The paper adds to the literature on social license to operate by extending its application to new domains and offers a dynamic model of social license to help guide the agenda for researcher and practitioner communities. • The requirement for social license to undertake wastewater monitoring is systematically scrutinized. • A dynamic framework for social license to monitor for COVID-19 is developed and presented. • Important gaps and additional steps for maintaining social license are identified. [ABSTRACT FROM AUTHOR]

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures. • We developed a multi-strain model with asymptomatic transmission. • The model studies the competition between different strains. • Real data in the US are used for model parameterization and simulation. • The results improve the understanding of multi-strain disease spread dynamics. • The results help to formulate guidelines of control strategies. [ABSTRACT FROM AUTHOR]

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables. • COVID-19 had a big impact in Italy, mainly in the northern part of the Country. • Reduction of land take and ecosystem services' loss improve environmental quality. • Environmental, climatic and land management can be leveraged for pandemic policies. • Geospatial representation of risks is a useful approach to support decision-makers. [ABSTRACT FROM AUTHOR]

The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the "Coswara" project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled "Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge". The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs. • Presents the DiCOVA dataset, the challenge task, and the baseline system. • Summarizes the systems and performances of the participating teams. • Analyzes systems of the top performing teams. • Discusses the lessons learnt and future directions. [ABSTRACT FROM AUTHOR]

Since the rise of the Covid-19 pandemic, several researchers stated the possibility of a positive relationship between Covid-19 spread and climatic parameters. An ecological study in 12 Iranian cities using the report of daily deaths from Covid-19 (March to August 2020) and validated data on air pollutants, considering average concentrations in each city in the last year used to analyze the association between chronic exposure to air pollutants and the death rate from Covid-19 in Iran. Poisson regression models were used, with generalized additive models and adjustment variables. A significant increase of 2.7% (IC(95%) 2.6–4.4) was found in the mortality rate due to Covid-19 due to an increase of 1 μg/m3 of NO 2. The results suggest an association between Covid-19 mortality and NO 2 exposure. As a risk approximation associated with air pollution, more precise analysis is done. The results also show a good consistency with studies from other regions; this paper's results can be useful for the public health policymakers and decision-making to control the Covid-19 spread. • A reliable Covid-19 data from Iran was used in this paper. • 12 cities were considered from all of the minor climate conditions of the country to minimize the climate bias. • Correlation of the air pollution and the other most effective diseases on the Covid-19 is also studied. • Nox was the most effective particle and had a significant impact on the Covid-19 mortality rate. • For a 1% increase in the NO 2 concentration, there will be a 2.7% increase in the Covid-19 mortality rate. [ABSTRACT FROM AUTHOR]

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". • We present Dynamical Survival Analysis (DSA) method to model an epidemic with incomplete data. • DSA does not require size of the susceptible population or the overall prevalence of the disease. • DSA was applied to COVID-19 in Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA). [ABSTRACT FROM AUTHOR]

The use of electronic nicotine delivery systems (ENDS), or vaping, is a relatively recent phenomenon, and there are various gaps in our current knowledge regarding the specific effects of e-cigarettes, such as their immunological effects. The importance of this question became even more relevant in light of the COVID-19 pandemic. This literature review examines the relationship between the use of electronic nicotine delivery systems (ENDS) and immunological effects to examine available information and identify gaps in the current knowledge. Our search strategy included studies focusing on the effects of ENDS on the immune response during infectious respiratory diseases such as COVID-19 and pneumonia. Peer-reviewed studies presenting quantitative data published from 2007, the year that e-cigarettes were introduced to the US market until 2022 have been included. All studies were indexed in PubMed. We excluded papers on THC and EVALI (E-cigarette, or Vaping Product, Use Associated Lung Injury) as we wanted to focus on the effects of nicotine devices. Among the 21 articles that assessed the relationship between ENDS and immunological health effects, we found eight studies based on cell models, two articles based on both cell and mouse models, five articles based on mouse models, and six studies of human populations. Most of the articles identified in our review demonstrated a potential association between vaping and adverse immunological health effects. Overall, the evidence from the cell and animal studies indicates that there is a positive, statistically significant association between vaping and adverse immune response during infectious respiratory diseases. The evidence from human studies is not conclusive. [ABSTRACT FROM AUTHOR]

We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period. • We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding log data • The paper examine how market participants reacted to the spreading news of Covid-19 • Volumes, sell-prediction spreads, and behavioral measures change on the lockdown date • Voluntary behavioral changes prior to lockdown are visible in social mobility. • The reopening reversed the lockdown effect on prices. [ABSTRACT FROM AUTHOR]

• Small landlords and landlords of color disproportionately struggled with rent collection both prior to and during the pandemic. • Pandemic-induced rental revenue declines alone cannot explain landlords' changing business practices. • Landlords were more likely to take punitive actions against tenants in communities of color than in white communities holding constant rental payment rates, both before and during the pandemic. • Elevated rates of property disinvestment during the pandemic have implications for the viability and affordability of the housing stock for years to come. This paper uses a survey of over 2500 rental property owners in ten cities across the United States to determine the impact of the COVID-19 pandemic on landlords' rent collection and business behavior. Our findings show that yearly rent collection was down significantly in 2020 relative to 2019—both within and across rental markets—and that an increasing number of owners have a large share of their portfolio behind on rent. Small owners and owners of color faced the highest exposure to deep tenant arrears in 2020, challenges they were also more likely to face prior to pandemic. Our findings show that owner business practices changed dramatically in 2020, with a higher share of landlords granting tenants rent extensions or forgiving back rent during the pandemic relative to prior. However, many owners also disinvested in their rental properties through deferred maintenance, missed mortgage payments, and property sale listings. Landlords of color pursued disinvestment strategies during the pandemic at an elevated rate compared to white landlords. Owners of properties in neighborhoods with more non-white residents were both more likely to experience decreased rent collection and more likely to pursue evictions and rental late fees holding constant rental payment rates, implying the pandemic has disproportionately affected renters in communities of color. Overall, our findings highlight the strain the pandemic has placed on the housing stock, which has implications for the long-term viability and affordability of many of these units. More concerningly, our results show that households of color—which have been disproportionately affected by the pandemic in other domains—were more likely to face punitive measures from landlords in both 2019 and 2020, suggesting the pandemic has exacerbated existing racial inequality in housing markets. [ABSTRACT FROM AUTHOR]

This review discusses the techniques available for detecting and inactivating of pathogens in municipal wastewater, landfill leachate, and solid waste. In view of the current COVID-19 pandemic, SARS-CoV-2 is being given special attention, with a thorough examination of all possible transmission pathways linked to the selected waste matrices. Despite the lack of works focused on landfill leachate, a systematic review method, based on cluster analysis, allows to analyze the available papers devoted to sewage sludge and wastewater, allowing to focalize the work on technologies able to detect and treat pathogens. In this work, great attention is also devoted to infectivity and transmission mechanisms of SARS-CoV-2. Moreover, the literature analysis shows that sewage sludge and landfill leachate seem to have a remote chance to act as a virus transmission route (pollution-to-human transmission) due to improper collection and treatment of municipal wastewater and solid waste. However due to the incertitude about virus infectivity, these possibilities cannot be excluded and need further investigation. As a conclusion, this paper shows that additional research is required not only on the coronavirus-specific disinfection, but also the regular surveillance or monitoring of viral loads in sewage sludge, wastewater, and landfill leachate. The disinfection strategies need to be optimized in terms of dosage and potential adverse impacts like antimicrobial resistance, among many other factors. Finally, the presence of SARS-CoV-2 and other pathogenic microorganisms in sewage sludge, wastewater, and landfill leachate can hamper the possibility to ensure safe water and public health in economically marginalized countries and hinder the realization of the United Nations' sustainable development goals (SDGs). • Pathogens including SARS-CoV-2 can be detected in municipal wastewater and solid waste. • SARS-CoV-2 seems pose remote infection risk through pollution-to-human transmission mechanism. • Standardized detection method is needed for environmental surveillance of SARS-CoV-2. • Disinfection needs to be optimized for SARS-CoV-2 and minimizing negative impacts. • The pandemic cause new challenges in achieving the UN SDGs related to water. [ABSTRACT FROM AUTHOR]

Various categories of coronavirus disease 19 (COVID-19) patients have exhibited major mortality rate differences and symptoms. Some papers have recently explained these differences in mortality rates and symptoms as a consequence of this virus infection acting in synergy with one or more latent pathogen infections in some patients. A latent pathogen infection likely to be involved in millions of these patients is the protozoan parasite Toxoplasma gondii , which infects approximately one third of the global human population. However, other papers have concluded that latent protozoan parasite infections can reduce the severity of viral infections. The aims and purposes of this paper include providing explanations for the contradictions between these studies and introducing a significant new category of T-cell exhaustion. Latent pathogens can have different genetic strains with great differences in their effects on a second pathogen infection. Furthermore, depending on the timing and effectiveness of drug treatments, pathogen infections that become latent may or may not later induce immune cell dysfunctions, including T-cell exhaustion. Concurrent multiple pathogen T-cell exhaustion is herein called "polyspecific T-cell exhaustion." • COVID-19 patients exhibit mortality rate differences probably from latent pathogen infections. • Toxoplasma gondii infects about one third of the global human population and it is likely involved. • Latent pathogens have different genetic strains & different effects on later pathogen infections. • Drug treatments affect pathogen infections causing immune dysfunctions, e.g. T-cell exhaustion. • "Polyspecific T-cell exhaustion" enables concurrent multiple pathogen T-cell exhaustion. [ABSTRACT FROM AUTHOR]

• Coordination of scientific research communities supported mathematical modelling of COVID-19. • Pre-existing research collaborations contributed to the success of coordination initiatives. • National infrastructure is key to support nationwide research networks. • Evidence synthesis important in supporting scientists providing advice to government. In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". [ABSTRACT FROM AUTHOR]

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green–blue space indicator in m2/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas. • Five waves of COVID-19 in Poland during 800 days of the pandemic were compared. • Green–blue spaces were negatively associated with COVID-19 cases and deaths. • There were many more COVID-19 incidences in urban areas in Poland than in rural areas. • Green–blue spaces can be used in the prevention of COVID-19 disease. [ABSTRACT FROM AUTHOR]

Violence against women is rampant in China. Even though meaningful strides have been made in the country, it remains disturbingly common for men to assault women—verbally or physically, who may or may not be their partners—in broad daylight in China. To make the situation worse, COVID-19, along with its restrictions, has both undermined women's ability to escape from abuse or violence and society's ability to provide timely help to victims. In light of the rising violence against women post-COVID, in this paper, we discuss the policy imperatives for countries like China to establish effective guardrails and support systems to protect women from the dehumanizing and destabilizing crime that is violence against women—a social malaise that not only harms and undermines the safety of society's daughters, mothers, and grandmothers, but also the integrity of local communities and social contract, let along shared humanity and global solidarity at large. [ABSTRACT FROM AUTHOR]

We develop a lattice-based, hybrid discrete-continuum modeling framework for SARS-CoV-2 exposure and infection in the human lung alveolar region, or parenchyma, the massive surface area for gas exchange. COVID-19 pneumonia is alveolar infection by the SARS-CoV-2 virus significant enough to compromise gas exchange. The modeling framework orchestrates the onset and progression of alveolar infection, spatially and temporally, beginning with a pre-immunity baseline, upon which we superimpose multiple mechanisms of immune protection conveyed by interferons and antibodies. The modeling framework is tunable to individual profiles, focusing here on degrees of innate immunity, and to the evolving infection–replication properties of SARS-CoV-2 variant strains. The model employs partial differential equations for virion, interferon, and antibody concentrations governed by diffusion in the thin fluid coating of alveolar cells, species and lattice interactions corresponding to sources and sinks for each species, and multiple immune protections signaled by interferons. The spatial domain is a two-dimensional, rectangular lattice of alveolar type I (non-infectable) and type II (infectable) cells with a stochastic, species-concentration-governed, switching dynamics of type II lattice sites from healthy to infected. Once infected, type II cells evolve through three phases: an eclipse phase during which RNA copies (virions) are assembled; a shedding phase during which virions and interferons are released; and then cell death. Model simulations yield the dynamic spread of, and immune protection against, alveolar infection and viral load from initial sites of exposure. We focus in this paper on model illustrations of the diversity of outcomes possible from alveolar infection, first absent of immune protection, and then with varying degrees of four known mechanisms of interferon-induced innate immune protection. We defer model illustrations of antibody protection to future studies. Results presented reinforce previous recognition that interferons produced solely by infected cells are insufficient to maintain a high efficacy level of immune protection, compelling additional mechanisms to clear alveolar infection, such as interferon production by immune cells and adaptive immunity (e.g., T cells). This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". • Stochastic hybrid discrete continuum platform for SARS-CoV-2 alveolar infection. • Infection outcomes of pre-immune and innate immune protection. • Incorporation of autocrine and paracrine interferon immune effects. • Interferon driven virion and infected cell elimination by immune cells. • Adaptable modeling framework to other alveolar virus infections. [ABSTRACT FROM AUTHOR]

The emergence of mutant strains of COVID-19 reduces the effectiveness of vaccines in preventing infection, but remains effective in preventing severe illness and death. This paper established a heterogeneous mixing model of age groups with pharmaceutical and non-pharmaceutical interventions by analyzing the transmission mechanism of breakthrough infection caused by the heterogeneity of protection period under the action of vaccine-preventable infection with the original strain. The control reproduction number R c of the system is analyzed, and the existence and stability of equilibrium are given by the comparison principle. Numerical simulation was conducted to evaluate the vaccination program and intervention measures in the customized scenario, demonstrating that the group-3 coverage rate p 3 plays a key role in R c. It is proposed that accelerating the rate of admission and testing is conducive to epidemic control by further fitting data of COVID-19 transmission in real scenarios. The findings provide a general modeling idea for the emergence of new vaccines to prevent infection by mutant strains, as well as a solid theoretical foundation for mainland China to formulate future vaccination strategies for new vaccines. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". • A modeling framework of heterogeneous mixing for COVID-19 with NPIs and vaccines. • Breakthrough infection and vaccines to reduce infection were incorporated into model. • Dynamical properties are analyzed, including threshold and stabilities of equilibria. • The vaccination strategies in custom scenario were explored with multiple indicators. [ABSTRACT FROM AUTHOR]

The title of the Virtual Special Issue (VSI) "SARS-CoV-2 and other pathogenic microorganisms in the environment", clearly indicates a main focus not only on the virus causing the current pandemic, but also on other pathogenic microorganisms and their spatial and temporal dynamics in environmental compartments. Overall, the VSI has received more than 100 submissions relating to most of the possible fields connected to the pandemic, many of them of high scientific value. A rigorous peer-reviewing process has been carried out, with a panel of experts making a great work to evaluate that important number of submissions. As a result, those manuscripts reaching the highest scientific standards were selected for publication. We think that the papers included constitute a set of high-quality contributions, which should help to improve the overall scientific perspective regarding this crucial issue. In this piece, the Editors comment some issues on the papers accepted for publication, and include additional reflections. [ABSTRACT FROM AUTHOR]

We analyse the paper "The spread of SARS-CoV-2 in Spain: Hygiene habits, sociodemographic profile, mobility patterns and comorbidities" authored by Rodríguez-Barranco et al. (2021), published in Environmental Research, vol.192, January 2021. The study was carried out under challenging conditions and provides original data of great value for exploratory purposes. Nevertheless, we found that the authors have not considered the potential effect of the multiple hypothesis testing carried out until obtaining the final model on the increased occurrence of false discoveries by mere chance. After adjusting the results provided in the paper for the effects of multiple testing, we conclude that only one of the five factors cited as statistically significant and relevant in the article, living with someone who has suffered from COVID-19, remained significantly related to the relative prevalence of COVID-19. Therefore, the preeminent role given in the analysed work to walking the dog as one of the main transmission routes of COVID-19 probably does not correspond to an actual effect. Instead, until replicated by other studies, it should be considered a spurious discovery. [ABSTRACT FROM AUTHOR]

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected. • This paper develops a temporal model to estimate daily weather fluctuation's effects on growth rate of COVID-19 infection. • Marginal increases in temperature, relative humidity and wind speed reduces COVID-19 growth rate. • The effect of weather fluctuations on COVID-19 growth rate is non-linear. • The impact of weather changes differ by geography. [ABSTRACT FROM AUTHOR]

In this paper we assess if two protective mechanisms for mental health - social support and social engagement - are associated with lower risk of reporting worsening mental health as a result of the pandemic. Using a demographically representative sample of working age adults in the United States (N = 4014) collected in February and March of 2021, we use logistic regression models to predict self-reported worsening mental health as a result of the pandemic using social support - measured as instrumental and emotional support - and social engagement. We use additional stratified models to determine if these relationships are consistent across rural-urban areas. Results indicate that among urban working age adults, emotional support, high levels of instrumental support, and some types of social engagement were associated with significantly lower risk of worsening mental health. However, among rural working age adults, only emotional support and high levels of instrumental support were significantly associated with lower odds of worsening mental health. Findings suggest that while emotional support may be effective for working age adults in lowering risk of worsening mental health from the pandemic, social engagement may not be for rural residents. The results support use of mental health promotion and prevention approaches that bolster emotional support through familial and local social networks, and raises caution about the efficacy of social engagement approaches in rural contexts. [ABSTRACT FROM AUTHOR]

• An SEIR model on an adaptive network with clustering is proposed. • The impacts of different types of social distancing interventions are considered. • A threshold-based intervention captures rich behavior in the prevalence curve. • The most effective interventions start early and are restrictive. The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect. [ABSTRACT FROM AUTHOR]

This paper examines and projects the water use and wastewater generation during and after the SARS-CoV-2 (COVID-19) in China, and discussed the water use/wastewater generation pattern changes among different sectors. Existing studies on the impact of pandemic spread-prevention measures on water consumption and wastewater treatment during the pandemic are reviewed. The water use and wastewater discharge in China through the COVID-19 period are then projected and analyzed using Multivariate Linear Regression. The projection is carried out for years 2019–2023 and covers an (estimated) full process of pre-pandemic, pandemic outbreak, and recovery phase and provides essential information for determining the complete phase impact of the COVID-19. Two scenarios, i.e. the recovery scenario and the business as usual scenario, are set to investigate the water use and wastewater generation characteristics after the pandemic. The results imply that in both scenarios, the water use in China shows a V-shaped trend from 2019 to 2023 and reached a low point in 2020 of 5,813✕108 m3. The wastewater discharge shows an increasing trend throughout the COVID period in both scenarios. The results are also compared with the water consumption and wastewater generation during the SARS-CoV-1 period. The implication for policymakers is the possible increase of water use and wastewater discharge in the post COVID period and the necessity to ensure the water supply and control of water pollution and wastewater discharge. • The impact of the SARS-CoV-2 pandemic on the water sector are reviewed. • Water use and wastewater discharge throughout the COVID-19 period are projected. • Water use in China shows a V-shaped trend from 2019 to 2023. • Wastewater discharge shows an increasing trend throughout the COVID period. • Water use and wastewater discharge during the SARS and the COVID-19 are compared. [ABSTRACT FROM AUTHOR]

This paper considers the problem of joint change detection and identification assuming multiple composite post-change hypotheses. We propose a multihypothesis changepoint detection-identification procedure that controls the probabilities of false alarm and wrong identification. We show that the proposed procedure is asymptotically minimax and pointwise optimal, minimizing moments of the detection delay as probabilities of false alarm and wrong identification approach zero. The asymptotic optimality properties hold for general stochastic models with dependent and nonidentically distributed observations. We illustrate general results for detection-identification of changes in multistream Markov ergodic processes. We consider several examples, including an application to rapid detection-identification of COVID-19 in Italy. Our proposed sequential algorithm allows much faster detection of COVID-19 than standard methods. [ABSTRACT FROM AUTHOR]