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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 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 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]

The coronavirus disease (COVID-19) pandemic which first broke out in Wuhan, China in December 2019 undoubtedly created a novel health challenge for countries around the world. The federal and some state governments in Nigeria in a bid to contain the spread of the virus and manage a looming health emergency pronounced lockdown measures at different times in 2020. The lockdown which affected the routine activities of citizens forced families and friends to stay indoors while it lasted. This was in compliance with the movement restriction order of the government which empowered law enforcement agencies to prosecute violators. While health authorities and other stakeholders made concerted efforts to control the coronavirus disease, some deviants and criminals exploited the situation to commit heinous crimes. The Nigerian media reported several cases of child and adolescent sexual abuse; including incest during the lockdown. This study used narrative review to examine the patterns of child and adolescent sexual abuse (CASA) during the COVID-19 lockdown in Nigeria. It found that CASA is often hidden, and can occurred online. It also revealed that CASA is perpetrated often by males as well as close relatives and have mainly females as victims. Furthermore, it found that perpetrators used manipulation, coercion and threats to facilitate the abuse. The study recommends strategies that may assist in forestalling the sexual exploitation of minors and young people during lockdowns and how to assist victims who may suffer post-traumatic stress disorder (PTSD) among other pains. • The paper demonstrates that child and adolescent sexual abuse escalates during COVID-19 pandemic; it is covert in nature and shrouded in secrecy and perpetrators may be close relatives. • Child and adolescent sexual abuse perpetrators may be high profile persons, often males, while girls are more targeted than boys and coercion or manipulation may be used by the perpetrator. • The increased access of children and adolescents to smartphones makes them more vulnerable to sexual abuse. [ABSTRACT FROM AUTHOR]

Without a doubt, the ongoing COVID-19 pandemic has profoundly impacted the landscape of human life across the globe. It may further lead to a substantial behavioral change in individuals, including the pattern of criminal victimization victimization. Although some previous studies suggested that there was a crime drop in Western societies after the pandemic outbreak, the effect of the pandemic on crime rates in the non-Western context was still vague. Therefore, the present study analyzed the police-recorded data in Hong Kong, a city in the south side of China that continued to enact a "dynamic zero covid" control of positive cases. In line with the previous studies, traditional crime, such as assaults in Hong Kong, dropped steadily after the pandemic outbreak. Yet, surprisingly, the overall crime rate alternatively increased amid the pandemic which is mainly attributed to the surge in cybercrime, especially deceptions and blackmail. This paper thus warrants more thorough investigations of the change in routine activities to the virtual world and the risks of cyber victimizations. Limitations and policy implications were discussed. [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]

• 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 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]

Transilience, the perceived capacity to persist, adapt flexibly, and positively transform in the face of an adversity, is a promising construct for understanding human adaptation to climate change risks. However, the question remains whether transilience is also relevant for adaptation to other adversities. In this paper, we investigate the role of transilience in the context of the COVID-19 pandemic, which posed a more urgent and acute threat to individuals compared to climate change. We conducted two studies in Italy and the Netherlands to examine whether people perceived transilience in the face of COVID-19 across different time points and countries, in which the severity of the pandemic and government measures varied. Furthermore, we studied the relationship between transilience and adaptive responses, including individual and collective adaptation behaviors, cognitive coping, well-being, and positive personal change. The results suggest that people perceive transilience in the face of COVID-19, and that higher transilience promotes adaptive responses and mental health in the face of COVID-19, in the Netherlands, but not in Italy. Moreover, longitudinal analyses indicate that transilience may be causally related to future behaviors and well-being. These findings suggest that transilience may be a robust predictor of adaptive responses and well-being in the context of different adversities, although this may depend on the specific context. Future research directions and theoretical implications are discussed. • Transilience reflects the perceived capacity to persist, adapt flexibly and positively transform in the face of an adversity. • People perceived transilience in the face of COVID-19. • Higher transilience is related to more adaptation behaviours and better mental health in the Netherlands. • The relationship between transilience and relevant outcomes may be context dependent. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The regulations introduced under the UK's Coronavirus Act 2020 to help curb the spread of COVID-19 caused considerable confusion due to conflation between what was regarded as 'guidance' and what was 'law'. The fast-paced nature of the pandemic meant that restrictions on public liberty and increased police powers to enforce the new 'rules' led to accusations of over-zealous enforcement behaviour of some officers, including a record number of Fixed Penalty Notices (FPNs) issued. This paper explores the experiences of police officers in a range of English police forces, using 28 h of qualitative interview data at two research points in time (2020 and 2022). The extracts presented here illustrate some of the challenges that officers faced enforcing the frequently changing rules and regulations during the COVID-19 pandemic in the UK. [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]

This paper draws on event system theory and the literatures on career orientations and career shocks to examine the impact of the COVID-19 pandemic on employees' career orientations. Factor analyses in three samples allow us to group seven career orientations into two dimensions: needs-based career orientations (those related to security, lifestyle, and health) and talent- and value-based career orientations (related to job content). We use a three-wave survey of Chinese employees to examine how these two broad orientations evolved in two time windows—one representing high, the other low event strength. We find that the two types of career orientations evolved in different ways during the pandemic: employees' needs-based career orientations were more salient during the COVID crisis than their talent- and value-based career orientations, and the salience of needs-based career orientations did not decrease as event strength abated. Employees' personal exposure to the crisis was positively related to the salience of their needs-based career orientations, but not to the salience of talent- and value-based career orientations. We also show that the salience of needs-based career orientations differed across employee groups: it was weaker among more experienced and successful employees (those higher in the managerial hierarchy and with steeper past pay increases). • Needs-based orientations were very salient during the COVID pandemic. • The salience of needs-based orientations did not decrease as event strength abated. • Employees' personal exposure to the COVID crisis increases this salience. • This salience is weaker among more experienced and successful employees. • Personal exposure is not related to salience of talent- and value-based orientations. [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]

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]

The impact of the SARS-CoV pandemic has gone well beyond health concerns, reaching the maritime industry. The study on the environmental impact of shipping industry during COVID-19 pandemic can provide useful insights to propose new management policies regarding shipping operations, both in-port and on the route. We present a case study centred in the Port of Barcelona covering a 30 nautical miles range in the period March to July 2020, during which different levels of restrictions and stringent lockdown measures were enforced. In this paper, we assess the impact of COVID-19 on maritime traffic and its related emissions in port cities using real-time Automatic Identification System (AIS) data. Interestingly, results show that the decline in maritime traffic is not correlated with a decrease in maritime emissions due to changes in vessel operation. During lockdown (March to June 2020), we observed a 27.9% reduction in the number of port calls compared to the pre-lockdown scenario, whereas pollutant emissions show a moderate decrease (1.8% for CO 2), no significant reduction (SO 2 and PM) or a slight increase (1.3% for NO x). This can be directly assigned to changes in vessel operation mode, i.e. vessels switched from Underway to At Anchor or Moored status, during which auxiliary engines are used at higher loads. • The pollutant emissions evolution from maritime activity is computed from AIS data during COVID-19 restrictions in 2020. • A significant reduction in ship calls was observed, with a mild reduction in maritime traffic in the same area. • Passenger vessels were responsible for more than 40% of fuel consumption and emissions, but represented 17% of all vessels • Ships underway dominated the total emissions during lockdown, although theydecreased during this period. [ABSTRACT FROM AUTHOR]

This paper investigates at the world level the influence of climate on the transmission of the SARS-CoV-2 virus. For that purpose, panel regressions of the number of cases and deaths from 134 countries are run on a set of explanatory variables (air temperature, relative humidity, precipitation, and wind) along with control variables (government interventions and population size and density). The analysis is completed with a panel threshold regression to check for potential non-linearities of the weather variables on virus transmission. The main findings support the role of climate in the circulation of the virus across countries. The detailed analysis reveals that relative humidity reduces the number of cases and deaths in both low and high regimes, while temperature and wind reduce the number of deaths. [ABSTRACT FROM AUTHOR]

Counteracting the spread of the SARS-CoV-2 virus is a current and important problem. A crucial issue in this area is the disinfection of various surfaces, as well as the air itself. For this purpose, devices such as foggers, which have different designs, are used. The appropriate size of droplets and their distribution determines the effectiveness of disinfectants. The paper presents droplet size distributions and characteristic mean droplet diameters, which are obtained with the use of a conical pressure-swirl atomizer. For the purpose of the analysis, the laser diffraction method was used. The influence of gas pressure and the distance from the atomizer's orifice on the spray angle and the distance between the nozzles on the spray curtain was also demonstrated. • The laser diffraction method was used. • The droplet size distributions and droplet mean diameters were analyzed. • The effect of gas pressure and the distance from the orifice were presented. • The spray angle was determined. • The distribution of the nozzles on the curtain's support frame was analyzed. [ABSTRACT FROM AUTHOR]

Background: Health systems around the world continue to navigate through operational challenges surfaced by the coronavirus disease 2019 (COVID-19) pandemic; these have implications for access to healthcare. In this study, we estimate the prevalence and reasons for forgoing healthcare during the pandemic in Geneva, Switzerland; a country with a universal and mandatory private health insurance coverage.Methods: Participants from a randomly selected population-based sample of the adult population living in the Canton of Geneva completed an online socio-demographic and lifestyle questionnaire between November 2020 and January 2021. The prevalence and reasons for forgoing healthcare since the beginning of the COVID-19 pandemic were examined descriptively, and logistic regression models were used to assess determinants for forgoing healthcare.Results: The study included 5397 participants, among which 8.0% reported having forgone healthcare since the beginning of the COVID-19 pandemic; participants with a disadvantaged financial situation (OR = 2.04; 95% CI: 1.56-2.65), and those reporting an average (OR = 2.54; 95% CI: 1.94-3.31) or poor health (OR = 4.40; 95% CI: 2.39-7.67) were more likely to forgo healthcare. The most common reasons to forgo healthcare were appointment cancellations by healthcare providers (53.9%), fear of infection (35.3%), and personal organizational issues (11.1%).Conclusion: Our paper highlights the effects of the COVID-19 pandemic on access to healthcare and identifies population sub-groups at-risk for forgoing healthcare. These results necessitate public health efforts to ensure equitable and accessible healthcare as the COVID-19 pandemic continues. [ABSTRACT FROM AUTHOR]

COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health. This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany. Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included. An increase in the average long-term air pollution of 1 μg/m3 particulate matter PM 2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM 10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM 2.5 and PM 10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants). Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity. • COVID-19 infections are substantially positively correlated with long-term particulate matter concentration in German counties. • The correlation is robust to the inclusion of relevant control variables in multiple OLS regressions. • COVID-19 fatalities are also positively correlated with long-term particulate matter concentration. • The correlation between particulate matter concentration and COVID-19 fatalities was not robust. [ABSTRACT FROM AUTHOR]

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020–1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future. • Climate factors and air quality can trigger SARS-CoV-2 viral infection transmission. • Synoptic atmospheric circulation patterns are related to COVID-19 waves start-up. • 1st and 4th COVID-19 waves have similar correlations with environmental variables. • COVID-19 and climate driving factors have mutual seasonality patterns in Madrid. • Early health strategies are essential to control the COVID-19 pandemic in Madrid. [ABSTRACT FROM AUTHOR]

The Russian government took strong containment measures to prevent the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rigid hygiene protocols and restrictions on daily living, such as social distancing and closing businesses and schools. While these measures were crucial to stop the diffusion of SARS-CoV-2, numerous voices highlighted their disorderly psychological, social, and economic impacts on food consumption behavior and lifestyle. Therefore, this paper aims to understand how consumers' food-related habits in Russia have shifted due to the COVID-19 pandemic and potential problems and opportunities this might bring for the Russian food system. The study is based on an online cross-sectional survey using a structured questionnaire administered in Russian through the Survey Monkey platform. A total of 1297 valid answers was collected. The results reveal that diet and food shopping, preparation, and use have been widely affected. Indeed, the survey results suggested (i) a change in the modality of acquiring food, consumers reduced the number of shopping trips and buying more on each trip to minimize store visits; (ii) a surge of stockpiling of non-perishable food items; (iii) a shift toward healthier diets; (iv) an increase in culinary capabilities; (v) a decrease of food waste. The results are expected to inform current emergency plans as well as long-term food-related strategies in Russia. [ABSTRACT FROM AUTHOR]

In response to COVID-19, many U.S. states implemented stay-at-home orders to mitigate disease spread, causing radical changes across all facets of consumer behavior. In this paper, we explore how a stay-at-home (SAH) order impacted one aspect of behavior: the demand for water. Using a unique panel dataset of property-level water usage in Henderson, Nevada, we analyze changes in water usage from the SAH order, finding an initial and continuous decline in average daily usage for commercial and school users. In contrast, we find an initial increase in consumption by residential users with this effect increasing over time. Aggregated across all users, the SAH order led to an increase in net water usage between 32 and 59 million gallons over the first 30 days. [ABSTRACT FROM AUTHOR]

Since the appearance of the first cases of COVID-19 in 2019, an unprecedented number of documents on that disease have been published in a short space of time. The current available information covers a large number of topics related with COVID-19 and/or the coronavirus (SARS-CoV-2) responsible of the disease. However, only a limited number of publications have been focused on a controversial issue: the origin of the SARS-CoV-2. In this paper, the scientific literature on the origin of SARS-CoV-2 has been reviewed. Documents published during 2020 and 2021 (January 1-July 19) in journals that are indexed in PubMed and/or Scopus has been considered. The revised studies were grouped according to these two potential origins: natural and unnatural. The analyses of the conclusions of the different documents here assessed show that even considering the zoonotic hypothesis as the most likely, with bats and pangolins being possibly in the origin of the coronavirus, today's date the intermediate source species of SARS-CoV-2 has not been confirmed yet. On the other hand, some researchers point to an unnatural origin of this coronavirus, but their conclusions are not strongly supported by a clear scientific evidence. Given the tremendous severity of the current pandemic, investigations to establish clearly and definitively the origin of SARS-CoV-2, are basic and essential in order to prevent potential future pandemics of similar nature. [ABSTRACT FROM AUTHOR]

Many environmental justice communities face elevated exposures to multiple stressors, given biases in urban and environmental policy and planning. This paper aims to evaluate sound level exposure in a densely populated environmental justice city in close proximity to major roadways, a nearby airport and high levels of industrial activity. In this study we collected various sound level metrics to evaluate the loudness and frequency composition of the acoustical environment in Chelsea, Massachusetts, USA. A total of 29 week-long sites were collected from October 2019 to June 2020, a time period that also included the influence of the COVID-19 pandemic, which drastically altered activity patterns and corresponding sound level exposures. We found that Chelsea is exposed to high levels of sound, both day and night (65 dB (A), and 80 dB and 90 dB for low frequency, and infrasound sound levels). A spectral analysis shows that 63 Hz was the dominant frequency. Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 dB lower. Our results demonstrate the importance of noise exposure assessments in environmental justice communities and the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sound levels. It also provides a snapshot of how much quieter communities can be with careful and intentional urban and environmental policy and planning. • We conducted a noise exposure assessment before and during COVID-19 stay-at-home advisories in an environmental justice and sanctuary city. • Like many EJCs, this city is in close proximity to major rail, road, and air networks and industrial operations.Pre-pandemic, Chelsea was exposed to similar sound levels, both day and night, across all metrics. • A spectral analysis shows that 63 Hz was the dominant frequency.Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. • Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 decibels lower. • Comprehensive noise assessments in EJCs are particularly important as they house our most vulnerable residents. • We also show the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sources. • We also provide a shot of how much quieter EJCs can be with careful and intentional urban and environmental policy and planning. [ABSTRACT FROM AUTHOR]

One of the problems hardly clarified in Coronavirus Disease 2019 (COVID-19) pandemic crisis is to identify factors associated with a lower mortality of COVID-19 between countries to design strategies to cope with future pandemics in society. The study here confronts this problem by developing a global analysis based on more than 160 countries. This paper proposes that Gross Domestic Product (GDP) per capita, healthcare spending and air pollution of nations are critical factors associated with fatality rate of COVID-19. The statistical evidence seems in general to support that countries with a low average COVID-19 fatality rate have high expenditures in health sector >7.5% of GDP, high health expenditures per capita >$2,300 and a lower exposure of population to days exceeding safe levels of particulate matter (PM 2.5). Another relevant finding here is that these countries have lower case fatality rates (CFRs) of COVID-19, regardless a higher percentage of population aged more than 65 years. Overall, then, this study finds that an effective and proactive strategy to reduce the negative impact of future pandemics, driven by novel viral agents, has to be based on a planning of enhancement of healthcare sector and of environmental sustainability that can reduce fatality rate of infectious diseases in society. • Expenditures in health sector >7.5% of GDP can reduce COVID-19 mortality between countries. • Health expenditures >$2,300 per capita can lessen COVID-19 mortality between countries. • Lower exposure of population to days having high levels of air pollution reduces COVID-19 mortality. • High health expenditures reduce COVID-19 mortality, though a high share of older population. • Efficient healthcare sector and environmental sustainability minimize case fatality rates of COVID-19. [ABSTRACT FROM AUTHOR]

The coronavirus has come to the world and spread with great wide among the countries of the world and has resulted in numerous infections that exceeded 167,181,023 million patients and are close to 3.5 deaths by September 2021. It also brought with it panic and fear, halted many activities, and led to the decline of the global economy. It changed human behavior and forced people to change their lifestyles to avoid infection. One of the most sectors that must be taken into consideration through pandemic coronavirus (COVID-19) around the globe is the air conditioning systems. The HVAC systems depend on the air as a heat transfer medium. The air contains a group of pollutants, viruses, and bacteria, and it affects and destroys human life. The air filter plays a major role as an important component in the air conditioning systems. Thus, it requires more effort by researchers to improve its design to prevent the ultra-size of particles loaded with coronavirus (COVID-19). This paper provides insight into the design of existing combined air-conditioners on their suitability and their impact on the spread of the hybrid coronavirus epidemic and review efforts to obtain a highly efficient air filter to get rid of super-sized particles for protection against epidemic infection. In addition, important guideline recommendations have been made to limit the spread of the COVID-19 virus and to obtain indoor air quality in air-conditioned places. • Indoor air quality is important in our society and provides healthy human comfort. • Increasing the temperatures and humidity dampen the activity of the COVID-19. • The air carries suspended particles of the COVID-19, which leads to an increase in infected cases. • Improper use of filters leads to spread of the bacterial and viruses' indoor environment. • Mandatory recommendations are drawn to benefit the global community to limit the spread of the deadly COVID-19. [ABSTRACT FROM AUTHOR]