A global aircraft-based wastewater genomic surveillance network for early warning of future pandemics.

International airports can have a key role in screening, detecting, and mitigating cross-border transmission of SARS-CoV-2 and potentially other infectious diseases. With aircraft passengers representing a subpopulation of a country or region, aircraft-based wastewater surveillance can be a promising approach to effectively identifying emerging viruses, tracing their evolution, and mapping global spread with international flights. Therefore, we propose the development of a global aircraft-based wastewater genomic surveillance network, with the busiest international airports as central nodes and continuing air travel journeys as vectors. This surveillance programme requires routinely collecting aircraft wastewater samples for microbiological analysis and sequencing and linking the resulting data with associated international air traffic information. With the creation of a strong international alliance between the airline industry and health authorities, this surveillance network will potentially complement public health systems with a true early warning ability to inform decision making for new variants and future global health risks.

Genomic evidence for divergent co-infections of co-circulating SARS-CoV-2 lineages.

Co-infection of RNA viruses may contribute to their recombination and cause severe clinical symptoms. However, the tracking and identification of SARS-CoV-2 co-infection persist as challenges. Due to the lack of methods for detecting co-infected samples in a large amount of deep sequencing data, the lineage composition, spatial-temporal distribution, and frequency of SARS-CoV-2 co-infection events in the population remains unclear. Here, we propose a hypergeometric distribution-based method named Cov2Coinfect with the ability to decode the lineage composition from 50,809 deep sequencing data. By resolving the mutational patterns in each sample, Cov2Coinfect can precisely determine the co-infected SARS-CoV-2 variants from deep sequencing data. Results from two independent and parallel projects in the United States achieved a similar co-infection rate of 0.3-0.5 % in SARS-CoV-2 positive samples. Notably, all co-infected variants were highly consistent with the co-circulating SARS-CoV-2 lineages in the regional epidemiology, demonstrating that the co-circulation of different variants is an essential prerequisite for co-infection. Overall, our study not only provides a robust method to identify the co-infected SARS-CoV-2 variants from sequencing samples, but also highlights the urgent need to pay more attention to co-infected patients for better disease prevention and control.

Spatiotemporal trends and impact of Covid-19 lockdown on eight sewage contaminants in Brisbane, Australia, from 2012 to 2020.

This study aims to investigate the spatiotemporal trends and impact of COVID-19 lockdowns to the profile of physiochemical parameters in the influent of wastewater treatment plants (WWTPs) around Brisbane, Australia. One 24-hr composite influent sample was collected from 10 WWTPs and analyzed for a range of physiochemical parameters per week (i.e., chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), ammonia, volatile suspended solid (VSS)) and per month (i.e., Ni and Cr) from 2012 to 2020, including the period of COVID-19 lockdowns in the region. The catchments studied were urban, with a mix of domestic and industrial activities contributing towards the contaminant profile. Statistical analysis identified that industrial and commercial land use, as well as population size had a large impact to the parameter loads and profile. Per capita mass loads of Cr in one catchment were 100 times higher than in others from one industrial point source. TP demonstrated a potential monotonic decrease over time due to practical reduction policies that have been implemented for phosphorous content in household detergents, except for one catchment where trade waste from food manufacturing industries contributed to an overall increase of 6.9%/year TP. The COVID-19 lockdown (March-April 2020) posed different impact on different catchments, either decrease (7-61%) or increase (2-40%) of most parameter loads (e.g., COD, TOC, TN, TP, VSS, Ammonia), which was likely driven by catchment characteristics (i.e., the proportion of residential, commercial, and industrial land uses). This study enhances our understanding of spatiotemporal trend of contaminants in the catchments for further effective source control.

covSampler: A subsampling method with balanced genetic diversity for large-scale SARS-CoV-2 genome data sets.

Phylogenetic analysis has been widely used to describe, display, and infer the evolutionary patterns of viruses. The unprecedented accumulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to post-subsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users' requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

covSampler: A Subsampling Method with Balanced Genetic Diversity for Large-Scale SARS-CoV-2 Genome Data Sets

Phylogenetic analysis has been widely used to describe, display and infer the evolutionary patterns of viruses. The unprecedented accumulation of SARS-CoV-2 genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to postsubsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users’ requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

Potential intervariant and intravariant recombination of Delta and Omicron variants.

Among numerous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concerns, Omicron is more infectious and immune-escaping, while Delta is more pathogenic. Here, we provide evidence for both intervariant and intravariant recombination of the rapidly evolving new SARS-CoV-2 genomes, including XD/XE/XF and BA.3, raising concerns of potential more infectious, immune-escaping, and disease-causing Omicron and Delta-Omicron variants.

Work readiness: Its determinants and association with work-related outcomes among new graduate nurses.

AIMS: This study aimed to identify determinants of work readiness and to assess the influences of work readiness on work-related outcomes in graduate nurses. BACKGROUND: Higher work readiness facilitates smoother role transitions of new graduate nurses. However, determinants of work readiness had not been fully examined. In addition, the relationships between work readiness and work-related outcomes, such as coping self-efficacy and occupational commitment, are also crucial but had not been assessed. METHODS: We recruited 794 graduate nurses and assessed their work readiness before working as nurses. After they commenced their work, we assessed their occupational commitment, coping self-efficacy and intention to remain. All assessments were conducted online. RESULTS: There were 728 (92%) female respondents. The mean scores and standard deviation (SD) of work readiness, coping self-efficacy, occupational commitment and intention to remain were 261.51 (SD: 45.40), 30.30 (SD: 6.13), 81.65 (SD: 11.56) and 11.01 (SD: 2.36), respectively. Based on a regression analysis, determinants of work readiness were positive school climate, student leadership experience, nursing as the primary choice of discipline and perceived influences of COVID-19 on the honorability of being a nurse and the willingness to be a nurse (p < .001). Moreover, after adjusted by all demographics and characteristics variables, higher work readiness would result in higher coping self-efficacy (estimated coefficient = 0.06, p < .001), occupational commitment (estimated coefficient = 0.06, p < .001) and intention to remain (estimated coefficient = 0.01, p = .002). CONCLUSION: Work readiness is a composite concept affected by psychosocial and environmental factors, which can predict new graduate nurses' future self-efficacy, occupational commitment and intention to remain. IMPLICATIONS FOR NURSING MANAGEMENT: The management of new graduate nurses when they begin to work could target their work readiness. Transition programmes that consider our identified determinants can be provided to those who show lower work readiness.

Monitoring of SARS-CoV-2 in sewersheds with low COVID-19 cases using a passive sampling technique.

Monitoring SARS-CoV-2 RNA in sewer systems, upstream of a wastewater treatment plant, is an effective approach for understanding potential COVID-19 transmission in communities with higher spatial resolutions. Passive sampling devices provide a practical solution for frequent sampling within sewer networks where the use of autosamplers is not feasible. Currently, the design of upstream sampling is impeded by limited understanding of the fate of SARS-CoV-2 RNA in sewers and the sensitivity of passive samplers for the number of infected individuals in a catchment. In this study, passive samplers containing electronegative membranes were applied for at least 24-h continuous sampling in sewer systems. When monitoring SARS-CoV-2 along a trunk sewer pipe, we found RNA signals decreased proportionally to increasing dilutions, with non-detects occurring at the end of pipe. The passive sampling membranes were able to detect SARS-CoV-2 shed by >2 COVID-19 infection cases in 10,000 people. Moreover, upstream monitoring in multiple sewersheds using passive samplers identified the emergence of SARS-CoV-2 in wastewater one week ahead of clinical reporting and reflected the spatiotemporal spread of a COVID-19 cluster within a city. This study provides important information to guide the development of wastewater surveillance strategies at catchment and subcatchment levels using different sampling techniques.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Standardising Training of Nurses in an Evidence-Based Psychosocial Intervention for Perinatal Depression: Randomized Trial of Electronic vs. Face-to-Face Training in China.

BACKGROUND: Rates of perinatal depression in China are high. The Thinking Healthy Programme is a WHO-endorsed, evidence-based psychosocial intervention for perinatal depression, requiring five days of face-to-face training by a specialist trainer. Given the paucity of specialist trainers and logistical challenges, standardized training of large numbers of nurses is a major challenge for scaling up. We developed an electronic training programme (e-training) which eliminates the need for specialist-led, face-to-face training. The aim of this study was to evaluate the effectiveness of the e-training compared to conventional face-to-face training in nursing students. METHODS: A single blind, non-inferiority, randomized controlled trial was conducted. One hundred nursing students from two nursing schools were randomly assigned to either e-training or conventional face-to-face training. RESULTS: E-training was not inferior to specialist-led face-to-face training immediately post-training [mean ENhancing Assessment of Common Therapeutic factors (ENACT) score (M) 45.73, standard deviation (SD) 4.03 vs. M 47.08, SD 4.53; mean difference (MD) -1.35, 95% CI; (-3.17, 0.46), p = 0.14]. There was no difference in ENACT scores at three months [M = 42.16, SD 4.85 vs. M = 42.65, SD 4.65; MD = -0.481, 95% CI; (-2.35, 1.39), p = 0.61]. CONCLUSIONS: E-training is a promising tool with comparative effectiveness to specialist-led face-to-face training. E-training can be used for training of non-specialists for evidence-based psychosocial interventions at scale and utilized where there is a shortage of specialist trainers, but practice under supervision is necessary to maintain competence. However, continued practice under supervision may be necessary to maintain competence.

Coping strategies, stigmatizing attitude, and cyberbullying among Chinese college students during the COVID-19 lockdown.

The ongoing COVID-19 pandemic provides hotbed for hatred and violence, which could be especially true among college students, the most active users of internet and social media. Based on a national sample of Chinese college students (N = 1,673), the present study aims to explore the clustered nature of stress coping strategies, as well as its associations with the participants' stigmatizing attitude and cyberbullying behaviors towards people in Hubei Province, the place where the first COVID-19 case was reported and recognized as China's epicenter of the pandemic. Four latent subgroups were first identified among the participants based on type and comparative adoption rate of their coping strategies, namely the emotional coping group, the inactive coping group, the support-seeking and positive coping group, and the independent and positive coping group. The significant associations between coping strategy patterns and stigmatizing attitude and cyberbullying behaviors were reported, respectively. The two were most likely to happen among the participants using emotional coping while the least likely among the independent and positive coping group. This study provides empirical supports for combating the secondary disasters of the pandemic, namely stigma and cyberbullying, by identifying the role of emotional and positive coping strategies.

Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19.

China's efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM2.5) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM2.5 concentrations and questioned if the previous PM2.5 declining trend was sustained or not. This study took a deep dive into the PM2.5 trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM2.5 data notably adjusted for a key measurement method change. We find that mean PM2.5 concentrations in northern China increased by 5.16 µg/m3 in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m3 from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM2.5 rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM2.5 concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM2.5 emission reductions, with an emphasis on key regions during key pollution periods.

Appetite loss, death anxiety and medical coping modes in COVID-19 patients: a cross-sectional study.

AIM: This study aims to explore the current level and associated factors of appetite and death anxiety amongst COVID-19 patients and also to identify correlation between the two variables. DESIGN: A cross-sectional study. METHODS: Demographic and characteristic questionnaires, templer death anxiety scale, council on nutrition of appetite questionnaire and medical coping modes questionnaire were utilized. RESULTS: The average scores of appetite and death anxiety were 19.46 (4.09) and 6.58, (3.21) respectively. 76 patients (100%) suffered from appetite loss, whilst 27 (35.50%) patients suffered from severe death anxiety. Marital status, COVID-19 disease condition ranks, educational level and death anxiety were explained 55.20% of the total variance in the appetite regression model (F = 31.83, p < .001), whilst the gender, avoidance coping strategy and appetite level were explained 49.80% of the total variance in death anxiety model (F = 17.80, p < .001). A moderate negative correlation between appetite and death anxiety was also confirmed (r=-.55, p < .001).

Bridging "Office-Based Care" With the "Virtual Practice Care Model": Evolving Care for Chronic Kidney Disease Patients in the COVID-19 Pandemic-And Beyond.

Since the outbreak of the coronavirus epidemic, the "virtual" telemedicine has become a critical substitute for patient-provider interactions. However, virtual encounters often face challenges in the care of patients in high-risk categories such as chronic kidney disease (CKD) patients. In this study, we explore the patient's satisfaction and the practical effects of a newly established telemedicine program on CKD patients' care during the COVID-19 pandemic. Based on a prior version of an online patient care platform established in 2017, we developed a customized and improved online telemedicine program designed to specifically address the challenges emerging from the pandemic. This included an online, smart phone-based strategy for triage and medical care delivery and psychological support. We invited a total of 278 CKD patients to join the new platform during the pandemic. The subjects in group A were patients utilizing our old online CKD system and were historical users registered at least 3 months before the pandemic. A pilot survey interrogating medical and psychological conditions was conducted. Feedback on the program as well as a psychological assessment were collected after 1 month. In total, 181 patients showed active responses to the program, with 289 person-time medical consultations occurring during the study. The virtual care program provided a rapid triage for 17% (30 out of 181) patients, with timely referral to in-patient medical encounters for their worsening medical conditions or severe psychological problems. Nearly all patients (97.4%) believed the program was helpful. The number of symptoms (OR 1.309, 95%CI 1.113-1.541; P = 0.001) and being enrolled during the pandemic (OR 3.939, 95% CI 1.174-13.221; P = 0.026) were associated with high stress. During the follow-up, the high-stress CKD group at baseline showed a significant decrease in avoidance score (6.9 ± 4.7 vs. 9.8 ± 1.9, P = 0.015). In conclusion, during the pandemic, we established an online telemedicine care program for CKD patients that provides a rapid triage function, effective CKD disease management, and potentially essential psychological support.

How doctors take initiatives in online healthcare communities

PurposeIn China, healthcare services have historically been expensive and difficult to access, with resources being unfairly distributed, often being centralized in large hospitals in major cities. In rural regions, hospitals often suffer from limited supplies, including human capital and equipment. E-health technologies have received significant attention from governments and citizens, with online healthcare communities (OHCs) providing easier communication between patients and doctors. Although doctors play a pivotal role in the success of OHCs, they are often unsure how to attract patients, with limited research focusing on this. The purpose of this paper is to explore how doctors can take initiatives in OHCs, from the joint perspectives of individual effort (i.e. intrapersonal factor) and identity in medical teams (MTs) (i.e. interpersonal factor), based on attribution theory.Design/methodology/approachHierarchical linear regression was conducted on data from 3,170 doctors participating in 865 online MTs, to examine the effects of individual effort and identity in MTs on individual performance. Individual effort included central effort (log-in frequency to OHC) and peripheral effort (articles published on doctors' homepages). Identity in MTs was represented as the identity of team leader and multiple team membership (MTM).FindingsThis study found that the main variables – central and peripheral effort, and leader and MTM identity – all had significant and positive impacts on the service quantity (SQ) of both written and telephone consultations. Although positive effects could be experienced in most conditions that were congruent with the logic of identity theory, the interaction terms demonstrated complex influences. Specifically, leader identity did not moderate the effect of article effort in written consultation, while MTM identity could not moderate the relationship between frequency effort and SQ in telephone-consultation services. Further, the leader identity negatively moderated the relationship between article effort and SQ in telephone consultations. Thus, for doctors with the leader identity, the impact of article effort on SQ was weaker. In summary, both aspects were proved to play important roles in individual SQ.Practical implicationsThis study provides empirical findings through focusing on the SQ of both written and telephone consultations in OHCs, thereby enabling healthcare providers to take initiatives and ultimately improve the efficiency and provision of delivered healthcare services. It is worth mentioning that doctors possessing the identity of team leader should be cautious that the more articles published by them may not lead to envisaged telephone-consultation performance, according to the negative moderating effect of leader identity on the relationship between article effort and SQ during telephone consultations.Originality/valueThis study contributes to the OHC literature by investigating how doctors' efforts and identity in OHCs affect individual performance, based on attribution theory and identity theory. Further, we provide healthcare practitioners with an improved understanding of these dimensions to improve autonomy regarding service provision in OHCs.

Intention to response, emergency preparedness and intention to leave among nurses during COVID-19.

AIM: To comprehensively assess the current level and identify associated factors of intention to response and emergency preparedness of clinical nurses during COVID-19 outbreak. DESIGN: A cross-sectional study was designed. METHODS: Demographic and characteristic questionnaire, intention to response scale, emergency preparedness scale and a self-designed questionnaire related to effects of events and intention to leave were used in this study. RESULTS: The mean scores of intention to response and emergency preparedness were 82.00 (SD = 18.17) and 64.99 (SD = 12.94), respectively. Moral consideration, engaged in COVID-19 protection training, had working experience in SARS, and the other eight factors were explained 34.6% of the total model variance in intention to response model (F = 80.05, p < .001). While, the level of IR, whether the pace of work was affected and above three same factors were explained 21.5% of the total model variance (F = 91.05, p < .001). Pathway analysis revealed that moral consideration, intention to leave and impacts on work and life mediate the relationship between EP and IR.

First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community.

Infection with SARS-CoV-2, the etiologic agent of the ongoing COVID-19 pandemic, is accompanied by the shedding of the virus in stool. Therefore, the quantification of SARS-CoV-2 in wastewater affords the ability to monitor the prevalence of infections among the population via wastewater-based epidemiology (WBE). In the current work, SARS-CoV-2 RNA was concentrated from wastewater in a catchment in Australia and viral RNA copies were enumerated using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) resulting in two positive detections within a six day period from the same wastewater treatment plant (WWTP). The estimated viral RNA copy numbers observed in the wastewater were then used to estimate the number of infected individuals in the catchment via Monte Carlo simulation. Given the uncertainty and variation in the input parameters, the model estimated a median range of 171 to 1,090 infected persons in the catchment, which is in reasonable agreement with clinical observations. This work highlights the viability of WBE for monitoring infectious diseases, such as COVID-19, in communities. The work also draws attention to the need for further methodological and molecular assay validation for enveloped viruses in wastewater.