Zoonotic diseases in animal agriculture and beyond: a one health perspective

This publication focuses on a group of vignettes to help understand zoonotic diseases, the anthropogenic factors accelerating their emergence, and the consequences of these events. While human activities and behavior are mostly responsible for creating this new era, the world struggles to prepare, change behavior, and rethink strategies to effectively address the inevitability of more frequent occurrences and severity of disease outbreaks and pandemics. Although we know and have experienced the cost of failure, past disease outbreaks seem to be quickly lost from our collective memories and new innovative interventions have not been imagined or adopted. This publication highlights examples that challenge our traditional actions and thinking and emphasize the need to adopt new approaches to prevent or ameliorate zoonotic diseases. The consensus of the experts contributing to this publication is that One Health should be embraced to achieve these results. The growing costs and societal disruptions of outbreaks and pandemics demand that zoonoses be part of our national security planning and deserve commensurate investments in preparedness, prevention, research, and resilience. This publication also highlights the necessity to fundamentally rethink and reestablish new relationships among institutions, organizations, and countries and especially between humanity and our natural systems worldwide.

Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life

The COVID-19 pandemic has resulted in the widespread implementation of extraordinary physical distancing interventions (e.g., stay-at-home orders) to slow the spread of the virus. Although vital, these interventions may be socially and economically disruptive, contributing to adverse psychological outcomes. This study examined relations of both stay-at-home orders and the perceived impact of COVID-19 on daily life to psychological outcomes (depression, health anxiety, financial worry, social support, and loneliness) in a nationwide U.S. community adult sample (N = 500;47% women, mean age = 40). Participants completed questionnaires assessing psychological outcomes, stay-at-home order status, and COVID-19's impact on their daily life. Being under a stay-at-home order was associated with greater health anxiety, financial worry, and loneliness. Moreover, the perceived impact of COVID-19 on daily life was positively associated with health anxiety, financial worry, and social support, but negatively associated with loneliness. Findings highlight the importance of social connection to mitigate negative psychological consequences of the COVID-19 pandemic.

Six-Month Outcomes Among Lung Transplant Patients with COVID-19

Purpose: There is limited data on the outcomes beyond the acute illness among lung transplant (LT) patients with Coronavirus disease 2019 (COVID-19). The current study sought to describe the predictors of 6-month survival among a single center cohort of LT. *Methods: We included all the LT patients diagnosed with COVID-19 during a one-year period (March 2020 to Feb 2021;n=54;median age: 60, 20-73 years;M:F 37:17). All patients completed at least 6-month follow up from COVID-19 diagnosis. We reviewed patient characteristics, presenting features, clinical course, and laboratory abnormalities at presentation and during the acute illness. We reviewed the hospital course and post-discharge outcomes including lung function loss among COVID-19 survivors. Median follow-up duration was 304 days. Six-month survival after COVID-19 was analyzed as the primary outcome variable. Result(s): Restrictive lung disease was the most common LT indication (n=41, 75.9%) and most had undergone bilateral LT (n=43, 79.6%). Patients were a median of 48 months (range <1-139 months) from their transplant. Majority of the patients required hospitalization (n=48) and significant proportion of patients developed respiratory failure (n=26). One month survival was 90.7% (n=49) while the survival dropped to 81.5% (n=44) by 6-month follow-up. On univariate analysis, females (35.3% vs 10.8%) and those with pre-existing chronic lung allograft dysfunction (CLAD, 33.3% vs 11.1%) experienced worse 6-month survival. Peak lactate dehydrogenase (LD) levels had the strongest association with 6-month survival on Mann Whitney U comparisons. On receiver operator characteristic curve analysis, the peak LD levels had an area under the curve of 82.9% (69.1-96.7%, p=0.002) with 400 U/L identified as the best cut-off. A peak LD level >400 U/L during the acute illness from COVID-19 was significantly associated with worse 6-month survival (OR, 95% CI: 4.38, 1.31-14.65, p=0.02).On Cox proportion hazard analysis, female gender (adjusted HR: 5.38, 1.13-25.64;p=0.035), pre-infection CLAD (5.63, 1.24-25.57;p=0.025) and peak LD levels >400 U/L (7.49, 1.72-35.53;p=0.007, see Figure for the Kaplan-Meier survival analysis) were independently associated with survival after COVID-19 among LT patients. Conclusion(s): COVID-19 is associated with significant mortality among LT patients with several patients succumbing beyond the period of acute illness. Female gender, established CLAD prior to COVID-19 and an LD>400 U/L at any time during the acute illness are adverse prognostic markers and may form the basis of customized management strategies. (Table Presented).

Effects of COVID-19 Pandemic on Pediatric Emergency Medicine Fellowship Training

Background: The COVID-19 pandemic brought shelter-in-place orders by public health organizations and eliminated large gatherings. Training programs abruptly stopped in-person learning to ensure the safety of medical trainees and redesigned curricula to use virtual platforms for didactic learning with variable success. Trainees had decreased patient contact time as pediatric emergency department (ED) patient volumes dropped, operative cases were canceled, clinic visits rescheduled, and use of telehealth visits became more prominent. Resident well-being became even more vital with social isolation and fears of infecting loved ones increased. Our study evaluated the impacts of COVID-19 on pediatric emergency medicine (PEM) fellowship programs, including effects on fellows' clinical, didactic, and research experiences, and effects on fellows' health and wellbeing. Methods: Two surveys were developed using an iterative process by the PEM Collaborative Scholarship Committee, 1 for program leadership (29 questions) and 1 for trainees (27 questions). Surveys were approved and distributed by the PEMPD Survey Committee to program directors (PD) a total of 3 times in 2-week intervals. PDs were asked to forward the trainee survey to their fellows. Survey responses were anonymous. Survey questions met our study objectives and consisted of multiple choice, 5-point Likert scale, and free text responses. Surveys were completed online using Qualtrics software between March 17, 2021 and April 19, 2021. Results: PDs had a 56.8% (50/88) response rate, fellows 34.6% (144/416). The majority of PD responses represented the Northeast US (n = 18) followed by the Midwest (n = 10). For trainees, responses mostly represented the Midwest (n = 39) followed by the Northeast (n = 38). Fifty-seven respondents self-identified as 1st year, 41 as 2nd years, 45 as 3rd years and 1 as 4th year fellows. All PDs reported a decrease in patient volumes during the height of the pandemic, estimating volumes decreased by 25-50% (n = 17, 36%), 51-75% (n = 20, 43%), or >75% (n = 10, 21%). Most common responses included change in rotations and block schedules, didactics moving to a virtual platform, increased frequency of speakers from outside the institution, fellows being able to participate in COVID related research, and additional mental health services for providers (see Table 1). Reasons for changes to the rotation schedules included rotation cancelations, extra time in the pediatric ED, modifications to rotations, and pregnancy (see Figure 1). PDs and trainees report being required to provide service to COVID patients outside of a pediatric ED. Commonly cited locations included the adult ED and medical intensive care units (MICU). Conclusion: COVID-19 had a clear impact on PEM fellowship training including decreases in pediatric patient volumes, canceled electives, increased care of adults, and altered didactics/conferences in accordance to CDC guidelines. The impact of these changes remains unclear. Future research might assess pandemic-related differences on intraining exam scores or how prepared fellows feel for unsupervised practice.

Human Happiness at Hard Times - COVID-19 Pandemic

COVID-19 has brought in a lot of physical health problems and mental health issues to people of all walks of life. The main aim of this study is to present that at the hardest of times (like COVID-19) it is possible to find happiness. The study follows the method of scientific review. The researcher selected articles based on the keywords from Scopus, Google Scholar, WHO documents, PubMed, Springer & Elsevier journals, and Government websites. Based on the protocol of inclusion and exclusion criteria the researcher selected, excluded, analyzed, critically evaluated and synthesized the data from the research articles and documents. Finally, 70 research articles were included in the study. The findings identified factors like fear, anxiety, worry, depression, panic, social withdrawal, etc., that affected human happiness during COVID-19. The study predominantly focuses on presenting various practical ways to help people to be happy even during the hard times of COVID-19. © The Electrochemical Society

Expert Decision Support System for Donor Identification Using Multi-Agent System

Convalescent Plasma (CP) therapy is an efficient method in the treatment of COVID-19 patients who either have a weak immune system or who are early in their illness. The notable setback for the implementation of the CP therapy lies in understanding the availability and spatial distribution of plasma donors. A multi-agent-based expert system is proposed in this paper to identify a suitable plasma donor in a short span and also in an efficient manner. Moreover, the issues with blood banks are twofold in connection with uneven intra-state and interstate distribution and lacuna of necessary facilities like the Component Blood Separation Units (CBSU) and Apheresis. The proposed expert system would remove the barriers of non-uniform distribution of blood banks and facilities across the country, and will provide a suitable solution to overcome the pandemic using multi-agent systems if implemented systematically. © 2021 IEEE.

Bayesian particle filter algorithm for learning epidemic dynamics

In this article, we consider a dynamic model for the spread of epidemics, in particular of COVID-19, and the inverse problem of estimating sequentially the time evolution of the unknown state and the model parameters based on noisy observations of the new daily infections. A characteristic of COVID-19 is the significant proportion of secondary infections though contacts with asymptomatic or oligosymptomatic infectious individuals. Since most of these individuals are not accounted for in the number of new daily infections, the size of this cohort can be inferred only indirectly through the underlying model. The evolution model used to propagate the current state from one data instance to the next is a suitably modified SEIR compartment model, providing the expected value for the new daily infection count that is modeled as a Poisson distributed random variable. The estimation of the state and the model parameters is based on a Bayesian particle filtering algorithm. The sequential Bayesian framework naturally provides a quantification of the uncertainty in the estimates of the model parameters, basic reproduction number, and size of the cohorts. Of particular interest is the fact that the algorithm makes it possible to estimate the size of the asymptomatic cohort, a key component for understanding the COVID-19 dynamics, and for planning mitigation measures. Alternative versions of the classical basic reproduction number for estimating the speed of the propagation of the disease are also proposed. The viability of the algorithm is demonstrated through a set of computed examples with both simulated realistic data and actual real data from selected US counties. The numerical tests show that the algorithm reproduces a ratio of asymptomatic vs symptomatic cohort sizes remarkably close to what is currently suggested by the Center for Disease Control.

Modeling epidemic spread among a commuting population using transport schemes

Understanding the dynamics of the spread of COVID-19 between connected communities is fundamental in planning appropriate mitigation measures. To that end, we propose and analyze a novel metapopulation network model, particularly suitable for modeling commuter traffic patterns, that takes into account the connectivity between a heterogeneous set of communities, each with its own infection dynamics. In the novel metapopulation model that we propose here, transport schemes developed in optimal transport theory provide an efficient and easily implementable way of describing the temporary population redistribution due to traffic, such as the daily commuter traffic between work and residence. Locally, infection dynamics in individual communities are described in terms of a susceptible-exposed-infected-recovered (SEIR) compartment model, modified to account for the specific features of COVID-19, most notably its spread by asymptomatic and presymptomatic infected individuals. The mathematical foundation of our metapopulation network model is akin to a transport scheme between two population distributions, namely the residential distribution and the workplace distribution, whose interface can be inferred from commuter mobility data made available by the US Census Bureau. We use the proposed metapopulation model to test the dynamics of the spread of COVID-19 on two networks, a smaller one comprising 7 counties in the Greater Cleveland area in Ohio, and a larger one consisting of 74 counties in the Pittsburgh–Cleveland–Detroit corridor following the Lake Erie’s American coastline. The model simulations indicate that densely populated regions effectively act as amplifiers of the infection for the surrounding, less densely populated areas, in agreement with the pattern of infections observed in the course of the COVID-19 pandemic. Computed examples show that the model can be used also to test different mitigation strategies, including one based on state-level travel restrictions, another on county level triggered social distancing, as well as a combination of the two. © 2021 by the authorsLicensee MDPI, Basel, Switzerland.

Does health literacy impact technological comfort in cancer patients?

INTRODUCTION: As healthcare systems are adapting due to COVID-19, there has been an increased need for telehealth in the outpatient setting. Not all patients have been comfortable with this transition. We sought to determine the relationship between health literacy and technological comfort in our cancer patients. METHODS: We conducted a survey of patients that presented to the oncology clinics at a single-center over a 2-month period. Patients were given a voluntary, anonymous, survey during their visit containing questions regarding demographics, health literacy and technological comfort. RESULTS: 344 surveys were returned (response-rate 64.3%). The median patient age was 61 years, 70% of responders were female and the most common race was White (67.3%). Increasing patient age, male gender, Black and Native-American race, decreased health literacy and lack of home broadband were associated with lower technological comfort score. CONCLUSION: In our cohort, patients with lower health literacy scores, older and male patients, or who have poor internet access showed a lower level of technological comfort. At risk patients can be identified and provided additional support in their use of telehealth services.

Tele health is here-are patients ready for it? an analysis of technological knowledge in cancer patients

Background: The COVID-19 pandemic has led to the rapid expansion of telehealth use. Telehealth has the potential to improve access for underserved populations who live far distances from well-equipped medical centers. This healthcare modality will likely be particularly important for patients with cancer. We hypothesized that there are disparities in the resources available to utilize telehealth and sought to study patient access and knowledge of associated technologies. Methods: We conducted a single-center cross-sectional survey study of patients at an NCCN-designated comprehensive cancer center over a two-month period. Demographics, education, internet, and cell phone access were assessed. Participant technological knowledge was determined with a validated 10-question quiz of terms regarding computers and the internet. Income was extrapolated using IRS data for individual zip codes. Analysis was performed using either an ordered logistic regression or mixed model ordered logistic regression (Stata SE 16.1). Results: There were 344 survey responses (rate 64.3%). Mean age of the respondents was 57.5 years, 70.0% were women, 67.3% were Caucasian, and 25.4% were Black. Many patients (35.0%) did not attend college, and 5.0% had not finished high school. The median estimated income was ≥45,820. Ninety-six patients (30.0%) did not have adequate internet access for telehealth use with 6.5% not having any internet access at all. Participants scored an average of 57.4% correct on the technology knowledge quiz. In unadjusted analysis, technological knowledge was predicted by age (Fig A), race (Fig B), income (Fig C), education level (Fig D), and cell phone type (Fig E). The type of home internet access and was not predictive of quiz score. Age, race, education level, and cell phone type remained significantly predictive of technological knowledge in multivariable analysis (Fig F). Home internet access (p =0.416) and median income (p =0.109) were not significantly associated with quiz score. Conclusions: In our sample, nearly one-third of cancer patients did not have adequate internet access for telehealth. There were significant disparities in technological knowledge among cancer patients which were associated with demographic characteristics. This study provides information for identifying patients that may have barriers to successful utilization of telehealth.

Role of wastewater treatment in COVID-19 control

The International Water Association (IWA) initiated a Task Force in April 2020 to serve as a leadership team within IWA whose role is to keep abreast and communicate the emerging science, technology, and applications for understanding the impact and the ability to respond to the COVID-19 pandemic and specifically designed for water professionals and industries. Expertise was nominated across the world with the purpose of collectively providing the water sector with knowledge products for the guidance on the control of COVID-19 and other viruses. This review paper developed by a working group of the IWA Task Force focuses on the control of COVID-19. The purpose of this review paper is to provide an understanding of existing knowledge with regards to COVID-19 and provide the necessary guidance of risk mitigation based on currently available knowledge of viruses in wastewater. This review paper considered various scenarios for both the developed world and the developing world and provided recommendations for managing risk. The review paper serves to pool the knowledge with regards to the pandemic and in relation to other viruses. The IWA Task Team envisage that this review paper provides the necessary guidance to the global response to the ongoing pandemic.

Demographic and clinical profile of pediatric patients with multiple sclerosis infected with sars-cov2

Background: COVID-19, the disease caused by SARS CoV2, causes severe respiratory disease, and rarely multisystem inflammatory syndrome, in some pediatric patients. Little is known about the disease course among patients with pediatric-onset multiple sclerosis. Objectives: To describe the demographic and clinical characteristics of a subgroup of pediatric-onset multiple sclerosis (POMS) patients infected with SARS CoV2. Methods: The Network of Pediatric Multiple Sclerosis Centers (NPMSC), a consortium of 10 US pediatric multiple sclerosis (MS) centers contributes clinical information about POMS patients and demyelinating disorders to a centralized database, the Pediatric Demyelinating Disease Database (PeMSDD), to facilitate research for this rare disorder. In addition to collecting clinical data on clinical course, comorbidities, disease modifying therapy use, and functional status, the NPMSC developed a screening questionnaire to administer to patients during standard of care visits to further evaluate their COVID- 19 status. Additionally POMS patients with confirmed or highly suspected COVID-19, will be assessed for risk factors including smoking use, recent glucocorticoid use, comorbidities;clinical presentation, including symptoms, radiological and laboratory data;COVID-19 treatments and outcomes. POMS patients will also complete the COViMS (COVID-19 Infections in MS & Related Diseases) database, a joint effort of the US National MS Society and the Consortium of MS Centers to capture information on outcomes of people with MS and other central nervous system (CNS) demyelinating diseases (Neuromyelitis Optica Spectrum Disease, or MOG antibody disease) who have developed COVID- 19. Together with data collected from the PeMSDD, we will present comprehensive data on the POMS patient experience with COVID- 19 and compare it to POMS patients without known or suspected COVID-19. Results: Data collection continues. Results available by the meeting due date will describe the demographics, risk factors, treatments and outcomes of POMS with COVID-19. Conclusions: will be drawn pending results of data analysis. We anticipate reporting on demographic data, risk factors, outcomes and any associations with disease modifying therapy.

A Unique Protein Self-Assembling Nanoparticle with Significant Advantages in Vaccine Development and Production

Nanoparticles are playing an increasingly powerful role in vaccine development. Here, we report the repurposing of nonstructural proteins 10 and 11 (hereafter NSP10) from the replicase polyprotein 1a (pp1a) of the human SARS coronavirus (severe acute respiratory syndrome) as a novel self-assembling platform for bioengineered nanoparticles for a variety of applications including vaccines. NSP10 represents a 152 amino acid, 17 kD zinc finger transcription/regulatory protein which self-assembles to form a spherical 84 Å diameter nanoparticle with dodecahedral trigonal 32 point symmetry. As a self-assembling nanoparticle, NSP10 possesses numerous advantages in vaccine development and antigen display, including the unusual particle surface disposition of both the N- and C-termini. Each set of N- or C-termini is spatially disposed in a tetrahedral arrangement and positioned at optimal distances from the 3-fold axes (8-10 Å) to nucleate and stabilize the correct folding of complex helical or fibrous trimeric receptors, such as those responsible for viral tropism and cell infection. An application example in the exploratory development of a therapeutic vaccine for idiopathic pulmonary fibrosis (IPF), including preliminary analysis and immunogenic properties, is presented. The use of this system could accelerate the discovery and development of vaccines for a number of human, livestock, and veterinary applications. © 2020 Daniel C. Carter et al.