Search

The effects of the COVID-19 pandemic on comprehensive maternal deaths in Brazil have not been fully explored. Using publicly available data from the Brazilian Mortality Information (SIM) and Information System on Live Births (SINASC) databases, we used two complementary forecasting models to predict estimates of maternal mortality ratios using maternal deaths (MMR) and comprehensive maternal deaths (MMRc) in the years 2020 and 2021 based on data from 2008 to 2019. We calculated national and regional standardized mortality ratio estimates for maternal deaths (SMR) and comprehensive maternal deaths (SMRc) for 2020 and 2021. The observed MMRc in 2021 was more than double the predicted MMRc based on the Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per 100,000 live births, respectively). We found persisting sub-national variation in comprehensive maternal mortality: SMRc ranged from 1.74 (95% confidence interval [CI] 1.64, 1.86) in the Northeast to 2.70 (95% CI 2.45, 2.96) in the South in 2021. The observed national estimates for comprehensive maternal deaths in 2021 were the highest in Brazil in the past three decades. Increased resources for prenatal care, maternal health, and postpartum care may be needed to reverse the national trend in comprehensive maternal deaths.

With over 16 million submitted genomic sequences, the SARS-CoV-2 (SC2) virus, the cause of the most recent worldwide COVID-19 pandemic, has become the most sequenced genome of all known viruses, revealing, for example, a vast number of expanding viral lineages. Since the pandemic phase appears to be over, we performed a retrospective re-examination of the demographic grouping pattern and their genomic characteristics during the entire pandemic period up to the peak of the last pandemic wave. For our study, we extracted from the NCBI only unique viral sequences and converted each sequence data to a relational vector, indicating the presence/absence of each variational event compared to a reference sequence. Our study revealed several genomic features that are unexpected or different from those of previous studies. For example, approximately 44,000 variants with unique sequences emerged during the pandemic period; they group into only four major viral-genomic groups and each has a set of mostly unique highly-conserved variant-genotypes (HCVGs); and a small set from the first (ancestral) group was inherited by the three (descendant) groups, suggesting that HCVGs in the next group may be predictable from the current group(s). Such a concept may be potentially important in designing panvalent vaccines against the current and future waves of viral infections.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the wisdom of crowds public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Previous studies relying on alcohol sales, alcohol-related injuries, and surveys have suggested that alcohol consumption increased during the COVID-19 pandemic. We sought to leverage over 1 million Breath Alcohol Concentration (BrAC) measurements from Bluetooth-enabled breathalyzers to conduct an objective and longitudinal assessment of alcohol use during the pandemic. Serial BrAC measurements revealed a decrease in drinking between January 1, 2020 and March 30, 2020, an increase between March 30, 2020 and May 25, 2020, a statistically insignificant decrease between May 25, 2020 and January 1, 2021, and an increase again between January 1, 2021 and June 4, 2021. No statistically significant relationships between shelter-in-place orders and alcohol consumption were detected. These findings demonstrate the complex relationship between the pandemic and alcohol consumption patterns, providing insights that may be relevant to the use of this commonly consumed substance with implications relevant to long-term effects from the patterns observed.

This study aims to evaluate the effect of social capital (SC), social support (SS), and social network formation (SNF) on the quality of life of American adults during COVID-19. Using a probability sample of American adults aged 49+, 2370 respondents were selected from the National Social Life Health and Aging Project (NSHAP) dataset for analysis using an integrated partial least squares based on structural equation modeling (PLS-SEM)-K-fold cross-validation approach. The analysis showed that social capital assessed using civic engagement, social cohesion, socioeconomic status (SES), social support, and social network formation were significantly and positively associated with American adults quality of life during the COVID-19 pandemic. Furthermore, the results showed that using the PLS-SEM and K-fold cross-validation approach produced a medium predictive power of the overall model, confirming the importance of SC, SS, and SNF in predicting quality of life-outcomes. These findings suggest that efforts to promote the well-being of American adults, especially older adults, during the pandemic should focus on strengthening social capital, social support and social network formation.

The emergence of long COVID during the ongoing COVID-19 pandemic has presented considerable challenges for healthcare professionals and researchers. The task of identifying relevant literature is particularly daunting due to the rapidly evolving scientific landscape, inconsistent definitions, and a lack of standardized nomenclature. This paper proposes a novel solution to this challenge by employing machine learning techniques to classify long COVID literature. However, the scarcity of annotated data for machine learning poses a significant obstacle. To overcome this, we introduce a strategy called medical paraphrasing, which diversifies the training data while maintaining the original content. Additionally, we propose a Data-Reweighting-Based Multi-Level Optimization Framework for Domain Adaptive Paraphrasing, supported by a Meta-Weight-Network (MWN). This innovative approach incorporates feedback from the downstream text classification model to influence the training of the paraphrasing model. During the training process, the framework assigns higher weights to the training examples that contribute more effectively to the downstream task of long COVID text classification. Our findings demonstrate that this method substantially improves the accuracy and efficiency of long COVID literature classification, offering a valuable tool for physicians and researchers navigating this complex and ever-evolving field.

Intertemporal decisions are crucial to survival, especially when humans are exposed to ecological threats. However, it remains unclear whether and how pandemic threats impact intertemporal choices across cultures. We conducted two studies in two cultural contexts (N = 1180). Study 1a found that Chinese who perceived more pandemic threats showed higher temporal discounting rates (i.e., preferred immediate smaller over delayed larger rewards), and this relationship was mediated by negative emotions. Study 1b, using threat priming, revealed that Chinese participants primed with a high-threat condition (depicting a pandemic threat) showed higher temporal discounting rates compared to those primed with a low-threat condition (describing the flu). Differently, Study 2a revealed that perceived pandemic threat levels of Americans did not directly predict temporal discounting rates. Using similar threat priming, Study 2b further confirmed that Americans in the high-threat priming condition showed no significant differences in temporal discounting rates compared to those in the low-threat priming condition. The current research deepens the understanding of the cultural difference in the impact of pandemic threats on intertemporal decision-making and further informs the development of interventions that promote individual future-oriented thinking in the face of pandemics. [ABSTRACT FROM AUTHOR]

The increasing abundance of animal species thriving in urban environments is a source of conflicts with managers and users of public spaces. Although opportunistic urban species often use resources originating from human food leftovers, the potential impact of a reduction in these resources on their demography is hard to quantify. The COVID-19 epidemic, which led many countries to set up lockdowns, gave us the opportunity to estimate the impact of a drastic reduction in such food resources and human activities on the demography of an urban bird population. Based on 7 years (2015–2021) of capture-mark-recapture of carrion crows (Corvus corone) in the city of Paris, France, we used multi-state models to examine the intra-annual (3-month time steps) apparent survival and movement patterns of crows during and outside COVID-19 lockdowns. We showed that the apparent survival of juvenile carrion crows decreased down during lockdown, while adult movements increased during this period, with more adult crows moving out of the urban district. Lockdown modified the demography of this urban crow population, suggesting that the reduction in food resources was sufficient to affect fitness and reduce carrying capacity. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff. Predicting deterioration in mild patients could alleviate these problems. A novel scoring system was proposed for predicting the deterioration of patients whose condition may worsen rapidly and those who all still mild or asymptomatic. Retrospective cohorts of 954 and 2,035 patients that quarantined in the Residential Treatment Center were assembled for derivation and external validation of mild COVID-19, respectively. Deterioration was defined as transfer to a local hospital due to worsening condition of the patients during the 2-week isolation period. A total of 15 variables: sex, age, seven pre-existing conditions (diabetes, hypertension, cardiovascular disease, respiratory disease, liver disease, kidney disease, and organ transplant), and five vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body temperature, and oxygen saturation (SpO2)) were collected. A scoring system was developed using seven variables (age, pulse rate, SpO2, SBP, DBP, temperature, and hypertension) with significant differences between the transfer and not transfer groups in logistic regression. The proposed system was compared with existing scoring systems that assess the severity of patient conditions. The performance of the proposed scoring system to predict deterioration in patients with mild COVID-19 showed an area under the receiver operating characteristic (AUC) of 0.868. This is a statistically significant improvement compared to the performance of the previous patient condition assessment scoring systems. During external validation, the proposed system showed the best and most robust predictive performance (AUC = 0.768; accuracy = 0.899). In conclusion, we proposed a novel scoring system for predicting patients with mild COVID-19 who will experience deterioration which could predict the deterioration of the patient's condition early with high predictive performance. Furthermore, because the scoring system does not require special calculations, it can be easily measured to predict the deterioration of a patients' condition. This system can be used as effective tool for early detection of deterioration in mild COVID-19 patients. [ABSTRACT FROM AUTHOR]

Abstract The increasing abundance of animal species thriving in urban environments is a source of conflicts with managers and users of public spaces. Although opportunistic urban species often use resources originating from human food leftovers, the potential impact of a reduction in these resources on their demography is hard to quantify. The COVID-19 epidemic, which led many countries to set up lockdowns, gave us the opportunity to estimate the impact of a drastic reduction in such food resources and human activities on the demography of an urban bird population. Based on 7 years (2015–2021) of capture-mark-recapture of carrion crows (Corvus corone) in the city of Paris, France, we used multi-state models to examine the intra-annual (3-month time steps) apparent survival and movement patterns of crows during and outside COVID-19 lockdowns. We showed that the apparent survival of juvenile carrion crows decreased down during lockdown, while adult movements increased during this period, with more adult crows moving out of the urban district. Lockdown modified the demography of this urban crow population, suggesting that the reduction in food resources was sufficient to affect fitness and reduce carrying capacity.

Prevention of COVID-19 with vaccine requires multiple doses and updated boosters to maintain protection; however currently there are no tests that can measure immunity and guide clinical decisions about timing of booster doses. This study examined the association between the risk of COVID-19 breakthrough infections and receptor binding domain (RBD) antibody levels and receipt of booster of COVID-19 vaccines. A community sample of Los Angeles County adults were surveyed between 2021 and 2022 to determine if they had a self-reported breakthrough infection. Predictors included RBD antibody levels, measured by binding antibody responses to the ancestral strain at baseline and self-reported booster shot during the study period. Of the 859 participants, 182 (21%) reported a breakthrough infection. Irrespective of the level of antibodies, the risk of breakthrough infection was similar, ranging from 19 to 23% (P = 0.78). The risk of breakthrough infections was lower among participants who had a booster shot (P = 0.004). The protective effect of a booster shot did not vary by antibody levels prior to receiving the booster. This study found no association between RBD antibody levels and risk of breakthrough infections, while the receipt of booster was associated with lower risk of breakthrough infections, which was independent of pre-booster antibody levels. Therefore, antibody levels might not be a useful guide for clinical decisions about timing of booster doses.

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

The coronavirus 2019 (COVID-19) pandemic has had significant impacts on health systems, population dynamics, public health awareness, and antibiotic stewardship, which could affect antibiotic resistant bacteria (ARB) emergence and transmission. In this study, we aimed to compare knowledge, attitudes, and practices (KAP) of antibiotic use and ARB carriage in Ecuadorian communities before versus after the COVID-19 pandemic began. We leveraged data collected for a repeated measures observational study of third-generation cephalosporin-resistant E. coli (3GCR-EC) carriage among children in semi-rural communities in Quito, Ecuador between July 2018 and September 2021. We included 241 households that participated in surveys and child stool sample collection in 2019, before the pandemic, and in 2021, after the pandemic began. We estimated adjusted Prevalence Ratios (aPR) and 95% Confidence Intervals (CI) using logistic and Poisson regression models. Child antibiotic use in the last 3 months declined from 17% pre-pandemic to 5% in 2021 (aPR: 0.30; 95% CI 0.15, 0.61) and 3GCR-EC carriage among children declined from 40 to 23% (aPR: 0.48; 95% CI 0.32, 0.73). Multi-drug resistance declined from 86 to 70% (aPR: 0.32; 95% CI 0.13; 0.79), the average number of antibiotic resistance genes (ARGs) per 3GCR-EC isolate declined from 9.9 to 7.8 (aPR of 0.79; 95% CI 0.65, 0.96), and the diversity of ARGs was lower in 2021. In the context of Ecuador, where COVID-19 prevention and control measures were strictly enforced after its major cities experienced some of the worlds the highest mortality rates from SARS-CoV-2 infections, antibiotic use and ARB carriage declined in semi-rural communities of Quito from 2019 to 2021.

Abstract The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff. Predicting deterioration in mild patients could alleviate these problems. A novel scoring system was proposed for predicting the deterioration of patients whose condition may worsen rapidly and those who all still mild or asymptomatic. Retrospective cohorts of 954 and 2,035 patients that quarantined in the Residential Treatment Center were assembled for derivation and external validation of mild COVID-19, respectively. Deterioration was defined as transfer to a local hospital due to worsening condition of the patients during the 2-week isolation period. A total of 15 variables: sex, age, seven pre-existing conditions (diabetes, hypertension, cardiovascular disease, respiratory disease, liver disease, kidney disease, and organ transplant), and five vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body temperature, and oxygen saturation (SpO2)) were collected. A scoring system was developed using seven variables (age, pulse rate, SpO2, SBP, DBP, temperature, and hypertension) with significant differences between the transfer and not transfer groups in logistic regression. The proposed system was compared with existing scoring systems that assess the severity of patient conditions. The performance of the proposed scoring system to predict deterioration in patients with mild COVID-19 showed an area under the receiver operating characteristic (AUC) of 0.868. This is a statistically significant improvement compared to the performance of the previous patient condition assessment scoring systems. During external validation, the proposed system showed the best and most robust predictive performance (AUC = 0.768; accuracy = 0.899). In conclusion, we proposed a novel scoring system for predicting patients with mild COVID-19 who will experience deterioration which could predict the deterioration of the patient’s condition early with high predictive performance. Furthermore, because the scoring system does not require special calculations, it can be easily measured to predict the deterioration of a patients’ condition. This system can be used as effective tool for early detection of deterioration in mild COVID-19 patients.

Abstract To determine the disease prevalence rate and clinical characteristics of Vogt–Koyanagi–Harada (VKH) disease among new patients before and after the declaration of a state of emergency (April 7, 2020) in Japan. New patients and patients with newly diagnosed VKH disease were categorized into “Before” and “After” groups based on the initial visit. The prevalence rate, sex ratio, and age of patients newly diagnosed with VKH were compared between the groups. Best-corrected visual acuity (BCVA) and recurrence rates were compared among 59 patients observed for > 12 months after receiving pulse steroid therapy. For reference, we also examined the prevalence rate of patients newly diagnosed with acute angle closure (AAC) in the Before and After groups. The prevalence rates of VKH disease among newly diagnosed patients (P

When evidence-based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence-based policymaking, which we call transparency of reasoning. Such transparency is critical to the success of the evidence-based policy movement, as without it, we will be unable to tell whether in any instance a policy was in fact based on evidence. [ABSTRACT FROM AUTHOR]

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework, MGLEP, that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators. [ABSTRACT FROM AUTHOR]

To determine the disease prevalence rate and clinical characteristics of Vogt–Koyanagi–Harada (VKH) disease among new patients before and after the declaration of a state of emergency (April 7, 2020) in Japan. New patients and patients with newly diagnosed VKH disease were categorized into "Before" and "After" groups based on the initial visit. The prevalence rate, sex ratio, and age of patients newly diagnosed with VKH were compared between the groups. Best-corrected visual acuity (BCVA) and recurrence rates were compared among 59 patients observed for > 12 months after receiving pulse steroid therapy. For reference, we also examined the prevalence rate of patients newly diagnosed with acute angle closure (AAC) in the Before and After groups. The prevalence rates of VKH disease among newly diagnosed patients (P < 0.05) or patients with AAC (P < 0.001) were significantly higher in the After group. No significant differences in sex ratio or age of VKH disease were observed in both groups. BCVA and recurrence rates showed no significant differences. The COVID-19 pandemic increased the prevalence of VKH disease among new patients compared with that of AAC. However, the clinical features of VKH disease were unlikely affected by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

It is without question that the COVID-19 pandemic has taken its toll on the U.S. economy. Stay-at-home orders led to reduced vehicular traffic and widespread declines in anthropogenic emissions (e.g., nitrogen oxides (NOx)). This study is the first to explore the potential consequences of O3 changes resulting from the economic shutdown in the United States on soybean crop yields for 2020. The pandemics impact on surface O3 is quantified using the NOAAs National Air Quality Forecasting Capability (NAQFC), which is based on the Community Multi-Scale Air Quality (CMAQ) model for May-July 2020. The would-be, 2020 level business-as-usual (BAU) emissions are compared to a simulation that uses representative COVID-19 (C19) emissions. For each emissions scenario, crop exposures are calculated using the AOT40 cumulative exposure index and then combined with county-level soybean production totals to determine regional yield losses. Exposure changes ranged between - 2 and 2 ppmVhr-1. It was further shown that increased exposures (0.5 to 1.10 ppmVhr-1) in the Southeast U.S. counteracted decreased exposures (0.8 to 0.5 ppmVhr-1) in the other soybean-producing regions. As a result, corresponding yield improvements counteracted yield losses around the Mississippi River Valley and allowed for minimal improvements in soybean production loss totaling $6.5 million over CONUS.

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

People vary both in their embrace of their society's traditions, and in their perception of hazards as salient and necessitating a response. Over evolutionary time, traditions have offered avenues for addressing hazards, plausibly resulting in linkages between orientations toward tradition and orientations toward danger. Emerging research documents connections between traditionalism and threat responsivity, including pathogen-avoidance motivations. Additionally, because hazard-mitigating behaviors can conflict with competing priorities, associations between traditionalism and pathogen avoidance may hinge on contextually contingent tradeoffs. The COVID-19 pandemic provides a real-world test of the posited relationship between traditionalism and hazard avoidance. Across 27 societies (N = 7844), we find that, in a majority of countries, individuals' endorsement of tradition positively correlates with their adherence to costly COVID-19-avoidance behaviors; accounting for some of the conflicts that arise between public health precautions and other objectives further strengthens this evidence that traditionalism is associated with greater attention to hazards.

As concerns related to the COVID-19 pandemic continue, it is critical to understand the impact of vaccination type on neutralizing antibody response durability as well as to identify individual difference factors related to decline in neutralization. This was a head-to-head comparison study following 498 healthy, community volunteers who received the BNT162b2 (n = 287), mRNA-1273 (n = 149), and Ad26.COV2.S (n = 62). Participants completed questionnaires and underwent blood draws prior to vaccination, 1 month, and 6 months after the vaccination series, and neutralizing antibody (nAB) titers at 1- and 6-months post vaccination were quantified using a high-throughput pseudovirus assay. Over 6 months of follow-up, nABs declined in recipients of BNT162b2 and mRNA-1273, while nABs in recipients of Ad26.COV2.S showed a significant increase. At the 6-month time point, nABs to Ad26.COV2.S were significantly higher than nABs to BNT162b2 and equivalent to mRNA-1273. Irrespective of follow-up timing, being older was associated with lower nAB for participants who received BNT162b2 and Ad26.COV2.S but not for those who received mRNA-1273. A higher baseline BMI was associated with a lower nAB for Ad26.COV2.S recipients but not for recipients of other vaccines. Women and non-smokers showed higher nAB compared to men and current smokers, respectively. The durability of neutralizing antibody responses differed by vaccine type and several sociodemographic factors that predicted response. These findings may inform booster recommendations in the future.

The COVID-19 pandemic has had a significant impact on mental health. Identifying risk factors and susceptible subgroups will guide efforts to address mental health concerns during the pandemic and long-term management and monitoring after the pandemic. We aimed to examine associations of insecurity (concerns about food, health insurance, and/or money), social support, and change in family relationships with poor mental health and to explore disparities in these associations. An online survey was collected from 3952 US adults between May and August 2020. Symptoms of anxiety, depression, stress, and trauma-related disorders were assessed by the Generalized Anxiety Disorder 7-item scale, the Patient Health Questionnaire-9, the Perceived Stress Scale-4, and the Primary Care Post-Traumatic Stress Disorder Screen, respectively. Social support was measured by the Oslo Social Support Scale. Logistic regression was used and stratified analyses by age, race/ethnicity, and sex were performed. We found a higher prevalence of poor mental health among those who were younger, female, with lower socioeconomic status, and racial/ethnic minorities. Participants who were worried about money, health insurance, or food had higher odds of symptoms of anxiety (OR = 3.74, 95% CI: 3.06-4.56), depression (OR = 3.20, 95% CI: 2.67-3.84), stress (OR = 3.08, 95% CI: 2.67-3.57), and trauma-related disorders (OR = 2.93, 95% CI: 2.42-3.55) compared to those who were not. Compared to poor social support, moderate and strong social support was associated with lower odds of all four symptoms. Participants who had changes in relationships with parents, children, or significant others had worse mental health. Our findings identified groups at higher risk for poor mental health, which offers insights for implementing targeted interventions.

Overuse of antibiotics during coronavirus disease 2019 (COVID-19) in an attempt to reduce COVID-19 mortality in the short term may have contributed to long-term mortality from antimicrobial resistance (AMR). The aim of this study was to evaluate the impact of the COVID-19 pandemic on AMR in Egypt and map the distribution of multidrug-resistant (MDR) and extensive drug-resistant (XDR) across Egypt. Through a multicenter cross-sectional study 2430 culture results were collected in 2019 and 2022 pre and post-COVID-19 pandemic in Egypt, including 400 Klebsiella pneumoniae, 760 Escherichia coli, 650 Acinetobacter baumannii, and 620 Methicillin-resistantstaphylococcus aureus (MRSA) culture results. MDR and XDR culture results distribution across Egypt was highlighted through the geographic information system. Mixed effect logistic regression models and sub-group analysis were performed according to the type of specimens to test the impact of COVID-19 on resistance. Adjusted analysis demonstrated K. pneumoniae resistance has increased against quinolones and carbapenems (P < 0.001). Resistance of E. coli has increased significantly against imipenem and meropenem. While E.coli susceptibility has increased to cefoxitin, levofloxacin, and ciprofloxacin. A. baumannii resistance has increased more than double against ceftazidime, cefepime, and piperacillin-tazobactam (P < 0.001). MRSA reserved its susceptibility to vancomycin and linezolid. MDR K. pneumoniae and A. baumannii have increased post-COVID-19 from 67% to 94% and from 79% to 98%, respectively (P < 0.001). XDR K. pneumoniae and A. baumannii have increased from 6% to 46%, and from 47% to 69%, respectively (P < 0.001). COVID-19 has changed the profile of AMR in Egypt so that urgent action is required to mitigate this threat and preserve our capacity to face infections in future decades. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and associated countermeasures had an immensely disruptive impact on people's lives. Due to the lack of systematic pre-pandemic data, however, it is still unclear how individuals' psychological health has been affected across this incisive event. In this study, we analyze longitudinal data from two healthy samples (N = 307) to provide quasi-longitudinal insight into the full trajectory of psychological burden before (baseline), during the first peak, and at a relative downturn of the COVID-19 pandemic. Our data indicated a medium rise in psychological strain from baseline to the first peak of the pandemic (d = 0.40). Surprisingly, this was overcompensated by a large decrease of perceived burden until downturn (d = − 0.93), resulting in a positive overall effect of the COVID-19 pandemic on mental health (d = 0.44). Accounting for this paradoxical positive effect, our results reveal that the post-pandemic increase in mental health is driven by individuals that were already facing psychological challenges before the pandemic. These findings suggest that coping with acute challenges such as the COVID-19 pandemic can stabilize previously impaired mental health through reframing processes. [ABSTRACT FROM AUTHOR]

Abstract This study examined the impact of the COVID-19 pandemic on marriage, divorce, birth, and death rates using the Poisson regression model and an interrupted time-series Poisson regression model. Before the pandemic, marriage and birth rates were decreasing, while divorce and death rates were increasing, with only the trend in birth rates being statistically significant. The immediate effect of the pandemic was a significant decrease in the divorce rate, but there were non-significant effects on birth and marriage rates. However, in the months following the onset of the pandemic, there was a statistically significant sustained effect on increasing death and divorce rates. Forecasts based on pre-pandemic data showed that by the end of 2020, marriage, divorce, death, and birth rates were higher compared to pre-pandemic levels. In conclusion, the pandemic has greatly impacted society, particularly in terms of death and divorce rates. Birth rates were not immediately affected to the time lag between decisions and actual births. Fear of COVID-19 may have increased death rates as people avoided seeking medical help. Vaccination and effective treatment strategies are vital in reducing the pandemic's impact on mortality. Supporting families financially is important due to the role of economic issues in couples’ decisions.

The pandemic has had very negative effects on the mental health of the population, especially in people with autism spectrum disorders (ASD) and intellectual disability (ID). We analyzed whether social communication, quality of life, and anxiety explain changes in the emotional impact of the pandemic in 60 adults with ASD and ID. Correlations between the study variables were analyzed and subsequently a multiple regression analysis was performed. The results show that communication writing, leisure and well-being index, explain 31% of the dependent variable. The well-being index (PWI) contributes significantly to improving the fit of the model, as indicated by β value. The remaining variables, communication writing and leisure socialization, do not contributed significantly to improving the fit of the model. Quality of life is the only variable that can explain changes in the emotional impact of the pandemic in the study population. This finding should guide future psychoeducational interventions and services for adults. [ABSTRACT FROM AUTHOR]

This study examined the impact of the COVID-19 pandemic on marriage, divorce, birth, and death rates using the Poisson regression model and an interrupted time-series Poisson regression model. Before the pandemic, marriage and birth rates were decreasing, while divorce and death rates were increasing, with only the trend in birth rates being statistically significant. The immediate effect of the pandemic was a significant decrease in the divorce rate, but there were non-significant effects on birth and marriage rates. However, in the months following the onset of the pandemic, there was a statistically significant sustained effect on increasing death and divorce rates. Forecasts based on pre-pandemic data showed that by the end of 2020, marriage, divorce, death, and birth rates were higher compared to pre-pandemic levels. In conclusion, the pandemic has greatly impacted society, particularly in terms of death and divorce rates. Birth rates were not immediately affected to the time lag between decisions and actual births. Fear of COVID-19 may have increased death rates as people avoided seeking medical help. Vaccination and effective treatment strategies are vital in reducing the pandemic's impact on mortality. Supporting families financially is important due to the role of economic issues in couples' decisions. [ABSTRACT FROM AUTHOR]

How does threat from disease shape our cooperative actions and the social norms that guide such behaviour? To study these questions, we draw on a collective-risk social dilemma experiment that we ran before the emergence of the Covid-19 pandemic (Wave 1, 2018) and compare this to its exact replication, sampling from the same population, that we conducted during the first wave of the pandemic (Wave 2, 2020). Tightness-looseness theory predicts and evidence generally supports that both cooperation and accompanying social norms should increase, yet, we mostly did not find this. Contributions, the probability of reaching the threshold (cooperation), and the contents of the social norm (how much people should contribute) remained similar across the waves, although the strength of these social norms were slightly greater in Wave 2. We also study whether the results from Wave 1 that should not be affected by the pandemic—the relationship between social norms and cooperation and specific behavioural types—replicate in Wave 2 and find that these results generally hold. Overall, our work demonstrates that social norms are important drivers of cooperation, yet, communicable diseases, at least in the short term, have little or no effects on either. [ABSTRACT FROM AUTHOR]

Seventh pandemic Vibrio choleare O1 El Tor strain is responsible for the on-going pandemic outbreak of cholera globally. This strain evolved from non-pathogenic V. cholerae by acquiring seventh pandemic gene (VC 2346), pandemic Islands (VSP1 and VSP2), pathogenicity islands (VP1 and VP2) and CTX prophage region. The cholera toxin production is mainly attributed to the presence of ctx gene in these strains. However, several variants of this strain emerged as hybrid strains or atypical strains. The present study aimed to assess the aquatic environment of Cochin, India, over a period of 5 years for the emergence of multidrug resistant V. cholerae and its similarity with seventh pandemic strain. The continuous surveillance and monitoring resulted in the isolation of ctx negative, O1 positive V. cholerae isolate (VC6) from coastal water, Cochin, Kerala. The isolate possessed the biotype specific O1 El Tor tcpA gene and lacked other biotype specific ctx, zot, ace and rst genes. Whole genome analysis revealed the isolate belongs to pandemic sequence type (ST) 69 with the possession of pandemic VC2346 gene, pathogenic island VPI1, VPI2, and pandemic island VSP1 and VSP2. The isolate possessed several insertion sequences and the SXT/R391 family related Integrative Conjugative Elements (ICEs). In addition to this, the isolate genome carried virulence genes such as VgrG, mshA, ompT, toxR, ompU, rtxA, als, VasX, makA, and hlyA and antimicrobial resistance genes such as gyrA, dfrA1, strB, parE, sul2, parC, strA, VC1786ICE9-floR, and catB9. Moreover, the phylogenetic analysis suggests that the isolate genome is more closely related to seventh pandemic V.cholerae O1 N16961 strain. This study reports the first incidence of environmental ctx negative seventh pandemic V. choleare O1 El Tor isolate, globally and its presence in the aquatic system likely to induce toxicity in terms of public health point of view. The presence of this isolate in the aquatic environment warns the strict implementation of the epidemiological surveillance on the occurrence of emerging strains and the execution of flagship program for the judicious use of antibiotics in the aquatic ecosystem. [ABSTRACT FROM AUTHOR]

The pandemic produced by SARS-CoV-2 generated various impacts on public health, the environment and other anthropogenic activities. The purpose of this study was to evaluate the reduction of air pollution due to P M 2.5 and P M 10 particulate matter in Cusco city at the beginning of the pandemic. Social confinement in Peru began on March 16, 2020, until the end of June. These health measures caused strict isolation that resulted in a significant decrease in vehicle flow on the streets and avenues of the city of Cusco. In the first days of May, even during the time of confinement, we managed to measure air quality at a sampling point located on the campus of the Universidad Nacional de San Antonio Abad de Cusco; a reduction in air pollution due to particulate matter was observed. The evaluation was carried out using an high-volume (HiVol) 3000 particulate matter sampler and the mass of particulate matter adhered to the filters was determined by gravimetry. The concentrations of particulate matter P M 2.5 and P M 10 obtained pre-pandemic were compared with those recorded at the beginning of the pandemic. The results revealed a significant average reduction in the concentration of P M 10 and P M 2.5 , reaching − 57.43% and − 59.52%, respectively, compared to pre-pandemic values. At the same time, its relationship with meteorological parameters and Google mobility data was evaluated and it was concluded that these parameters did not significantly affect the reduction of particulate matter. This study reveals the positive effects of the pandemic in reducing air pollution and the confinement measures had as a secondary effect on the decrease in air pollution in Cusco City. [ABSTRACT FROM AUTHOR]

Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation's data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran's I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran's I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with r 2 of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response. [ABSTRACT FROM AUTHOR]

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances. [ABSTRACT FROM AUTHOR]

The persistent SARS-CoV-2 pandemic, spanning over three years, has profoundly impacted daily life worldwide. Crucial measures like interpersonal distance (IPD) and mask-wearing have become paramount in preventing infection. With SARS-CoV-2 now resembling an endemic condition similar to influenza, it is vital to assess the changes in IPD influenced by relevant factors during and after the pandemic. This study concentrated on two specific stages (the pandemic stage and the post-pandemic era) and investigated variations in IPD with different test combinations. Variables taken into account encompassed the pandemic stage, participant gender, target gender, and mask-wearing status. We examined IPD data from 100 young individuals (50 males and 50 females) at each stage, with a one-year interval between tests. The results highlighted the substantial impact of all variables on perceived IPD during the pandemic phase (all p < 0.001). However, in the post-pandemic stage, only mask-wearing demonstrated a notable effect on IPD (p < 0.001). As the SARS-CoV-2 epidemic subsides, the enduring influence of mask usage on IPD persists. Nevertheless, the gap between the two mask-wearing scenarios diminishes, contracting from around 50 cm during the epidemic phase to 20 cm in the post-epidemic phase. Across these two pandemic stages, there was an overall reduction of approximately 90 cm in IPD, indicating a noteworthy decrease in perceived personal space and a consequential shortening of social proximity during the post-pandemic stage. This decrease in IPD may suggest the successful socio-cultural adaptation of the young Taiwanese individuals in our study during the post-pandemic era. [ABSTRACT FROM AUTHOR]

Previous studies have reported an association between the COVID-19 pandemic, social distancing regulations and longing for touch (LFT; i.e., a discrepancy between actual touch frequency and one's desire to be touched). However, less is known about the prevalence and severity of LFT in the general population in the absence of social distancing regulations. The aim of this study was therefore to exploratively compare data collected during and after the pandemic. Pandemic data was collected online in an international sample (n = 1982), of which a matched subsample (n = 115) was used in the reported analyses. Post-pandemic data was collected one week after social distancing regulations restrictions were lifted in the Netherlands (n = 60) and when virtually no restrictions were in place (n = 55). The severity of LFT was significantly higher during the pandemic than afterwards. Although there were no significant differences in the general prevalence of LFT, significantly more participants reported high levels of LFT (score of 75–100) during the pandemic. We cautiously conclude that, although LFT may have peaked during the pandemic, a large portion of the general population desires to experience more interpersonal touch, even in the absence of social distancing regulations. [ABSTRACT FROM AUTHOR]

People worldwide use SARS-CoV-2 (COVID-19) visualizations to make life and death decisions about pandemic risks. Understanding how these visualizations influence risk perceptions to improve pandemic communication is crucial. To examine how COVID-19 visualizations influence risk perception, we conducted two experiments online in October and December of 2020 (N = 2549) where we presented participants with 34 visualization techniques (available at the time of publication on the CDC's website) of the same COVID-19 mortality data. We found that visualizing data using a cumulative scale consistently led to participants believing that they and others were at more risk than before viewing the visualizations. In contrast, visualizing the same data with a weekly incident scale led to variable changes in risk perceptions. Further, uncertainty forecast visualizations also affected risk perceptions, with visualizations showing six or more models increasing risk estimates more than the others tested. Differences between COVID-19 visualizations of the same data produce different risk perceptions, fundamentally changing viewers' interpretation of information.

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in

Outpatient care patterns have changed markedly during the COVID-19 pandemic. In this population-based retrospective cohort study, we compared the frequency of outpatient care (whether in-person or virtual) and continuity of care for all community-dwelling adults in Alberta between March 1, 2019 and February 29, 2020 (pre-pandemic) versus March 1, 2020 to February 28, 2021 (pandemic). We calculated provider continuity using Breslau's Usual Provider Continuity (UPC) for patients with at least 2 outpatient encounters. In 2019–20, 594,350 (98.4%) of 603,877 community-dwelling adults with ambulatory care sensitive conditions (ACSC) had ≥ 1 outpatient visit (median 8 visits, mean UPC score 0.61, SD 0.23), compared to 566,569 (98.6%) of 574,613 (median 8 visits, mean UPC score 0.67, SD 0.23) during the first year of the pandemic. Similar patterns were seen for adults without ACSC: 2,207,710 (93.9%) of 2,350,147 had ≥ 1 outpatient visit (median 3 visits, mean UPC score 0.61, SD 0.24) pre-pandemic compared to 2,113,239 (93.5%, median 4 visits, mean UPC 0.67, SD 0.24) in the first year of the pandemic. Thus, the COVID-19 pandemic did not impact frequency of follow-up while continuity of care improved both for patients with or without ACSC in Alberta, Canada. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death. [ABSTRACT FROM AUTHOR]

In 2020, the COVID-19 pandemic followed a two-wave pattern in most countries. Hospital admission for COVID-19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS-CoV-2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI-COVID-19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID-19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) − 3.2%; 95% confidence interval (95% CI) − 4.4 to − 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD − 7.6%; 95% CI − 9.7 to − 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID-19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully. [ABSTRACT FROM AUTHOR]

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and subsequent travel restrictions led to a considerable reduction in tourism and human activity on Maui, presenting a unique opportunity to study debris accumulation on local beaches during changing levels of human activities. Standardized daily debris accumulation surveys were completed at two beach sites in Maui, Hawai 'i before (2017) as well as throughout the initial year of the pandemic (2020–2021) and allowed for the assessment of pandemic-related restrictions on marine debris accumulation trends. Throughout the pandemic, reduced beach use due to higher lockdown levels had significant impacts on debris accumulation at both sites, but only one of the two sites experienced a significant decrease (~ 90% reduction) in debris accumulation rates when compared to the same months in 2017. Daily accumulation rates across two sites increased from an average of 16 items/100 m during peak lockdown levels to 43 items/100 m when restrictions eased. The observed fluctuations in debris accumulation rates, driven by changes in tourism and travel restrictions during the COVID-19 pandemic emphasize the importance of proactive measures to protect the natural environment, including source reduction and effective legislation for waste prevention. By addressing both local and remote sources of debris and focusing on reducing waste at its source, it is possible to mitigate the impacts of debris accumulation on coastal environments and marine life in Hawaiʻi. [ABSTRACT FROM AUTHOR]

This study proposes the Structured Multi-criteria Model of Self-Managed Motivation in Organizations based on happiness at work. Employees need to be motivated in order to be efficient at doing a particular task at the workplace. As young people born between 1995 and 2004, called Generation Z, enter the labour market, it is essential to consider how employees' motivation might be affected. In the article a quantitative approach was used to collect new data from 200 Polish respondents completing an online survey. The research was conducted before and during the pandemic time. We report and analyze the survey results conducted in Poland among the representatives of Generation Z, who had been employed for at least 6 months. We developed and validated a new approach to motivation using methodologies called Factor Analysis. Based on empirical verification, we found a new tool that connects employee motivation and selected areas of the Hygge concept called Hygge Star Model, which has the same semantics before and during Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

COVID-19's high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced.

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google's mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

In the early stages of an outbreak, the term pandemic can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of pandemic, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters-such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions-on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term pandemic, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.