Genetic associations from the Long COVID-19 Host Genetics Initiative contribute to understanding of the genetics of prolonged symptoms in COVID-19

Background/Objectives: One of the most remarkable features of SARS-CoV-2 infection is that a large proportion of individuals are asymptomatic while others experience progressive, even lifethreatening acute respiratory distress syndrome, and some suffer from prolonged symptoms (long COVID). The contribution of host genetics to susceptibility and severity of infectious disease is well-documented, and include rare monogenic inborn errors of immunity as well as common genetic variation. Studies on genetic risk factors for long COVID have not yet been published. Method(s): We compared long COVID (1534) to COVID-19 patients (96,692) and population controls (800,353) using both questionnaire and EHR- based studies. First meta-analysis of 11 GWAS studies from 8 countries did not show genome-wide significant associations. Result(s): Testing 24 variants earlier associated to COVID-19 susceptibility or severity by COVID-19 Host Genetics Initiative showed genetic variation in rs505922, an intronic variant in ABO blood group gene, to be associated with long COVID compared to population controls (OR = 1.16, 95% CI: 1.07-1.27, p = 0.033). (Within-COVID analysis gave similar OR, but was not significant after conservative Bonferroni correction (OR = 1.17, 95% CI: 1.06-1.30, p = 092)). Conclusion(s): The first data freeze of the Long COVID Host Genetics Initiative suggests that the O blood group is associated with a 14% reduced risk for long COVID. The following data freezes with growing sample sizes will possibly elucidate long COVID pathophysiology and pave the way for possible treatments for long lasting COVID symptoms.

COVID-19 in immunocompromised children: comparison of SARS-CoV-2 viral load dynamics between the first and third waves.

SARS-CoV-2 dynamics across different COVID-19 waves has been unclear in immunocompromised children. We aimed to compare the dynamics of SARS-CoV-2 RNA viral load (VL) during the first and third waves of COVID-19 in immunocompromised children. A retrospective and longitudinal cohort study was conducted in a pediatric referral hospital of Argentina. The study included 28 admitted immunocompromised children with laboratory confirmed SARS-CoV-2 infection. Thirteen acquired the infection during COVID-19 first wave (May to August 2020, group 1 (G1)) and fifteen in the third wave (January to March 2022, group 2 (G2)). RNA viral load measure and its dynamic reconstruction were performed in nasopharyngeal swabs by validated quantitative, real time RT-PCR, and linear mixed-effects model, respectively. Of the 28 children included, 54% were girls, most of them had hemato-oncological pathology (57%), and the median age was 8 years (interquartile range (IQR): 3-13). The dynamic of VL was similar in both groups (P = 0.148), starting from a level of 5.34 log10 copies/mL (95% confidence interval (CI): 4.47-6.21) in G1 and 5.79 log10 copies/mL (95% CI: 4.93-6.65) in G2. Then, VL decayed with a rate of 0.059 (95% CI: 0.038-0.080) and 0.088 (95% CI: 0.058-0.118) log10 copies/mL per day since diagnosis and fell below the limit of quantification at days 51 and 39 after diagnosis in G1 and G2, respectively. Our results evidenced a longer viral RNA persistence in immunocompromised pediatric patients and no difference in VL dynamic between COVID-19 first wave-attributed to ancestral infections-and third wave-attributed to Omicron infections.

Impact of early rehabilitation on the functional quality of life of the critically ill at one year

Introduction: ICU-acquired weakness (ICUAW) is a long-recognised phenomenon, featuring a prevalence of 25-80%. Early mobilisation is anaccepted intervention that may attenuate ICUAW and improve outcomes [1, 2]. Method(s): Prospective observational study in polyvalent ICU analysing the effect of early rehabilitation (eRHB) on quality of life one year after discharge (D/C).Patients who required invasive mechanical ventilation > 24 h and survived SARS-CoV2 respiratory infection between 5/3/2020 and 12/01/2022 were included. Patients were classified into two groups: eRHB or not eRHB. Demographic and clinical data were collected, and a telephone survey was conducted one year after D/C. Clinical Frailty Scale at ICU admission (T1) and one year after D/C (T5);Medical Research Council (MRC) at the start of rehabilitation (T2) and hospital D/C (T4);Barthel Index at ICU D/C (T3), T4 and T5;and the SF-36 health questionnaire at T5 were also collected. Statistical analysis was performed between subgroups: Pearson's Chi-square test or Mann-Whitney U test to find significant differences. ART-ANOVA was used to analyse the survey results. Result(s): Of 99 patients, 64.6% belonged to the eRHB group. There were no statistically significant differences in the analysis of clinicdemographic variables. We observed a significant improvement of the MRC, a better Barthel Index in the eRHB group, and a statistically significant positive impact on several components of the SF-36 in the eRHB group (physical functioning, vitality, social functioning, bodily pain, general health, and self-reported health transition). Conclusion(s): Patients who received eRHB had better physical functioning and higher vitality recovery. In addition, they suffered less impact on their social life, had better pain control, and reported improved general health. All this emphasises the need for eRHB protocols in the ICU, promoting multidisciplinary care of our patients.

Marginalization, Vulnerability and Economic Dynamics in COVID-19

Abstract COVID-19, caused by the coronavirus family SARS-CoV-2 and declared a pandemic in March 2020, continues to spread. Its enormous and unprecedented impact on our society has evidenced the huge social inequity of our modern society, in which the most vulnerable individuals have been pushed into even worse socioeconomic situations, struggling to survive. As the pandemic continues, we witness the huge suffering of the most marginalized populations around the globe, even in developed, high-income latitudes, such as North America and Europe. That is even worse in low-income regions, such as Brazil, where the public healthcare infrastructure had already been struggling before the pandemic. Cities with even more evident social inequity have been impacted the most, leaving the most socioeconomically disadvantaged ones, such as slum residents and black people, continuously inflating the statistics of COVID-19 sufferers. Poverty, marginalization, and inequity have been well-known risk factors for morbidity and mortality from other diseases. However, COVID-19 has deepened our society's wound. It is up to us to heal it up. If we really care for the others and want to survive as a species, we must fight social inequity.

Individual risk-aversion responses tune epidemics to critical transmissibility (R = 1).

Changes in human behaviour are a major determinant of epidemic dynamics. Collective activity can be modified through imposed control measures, but spontaneous changes can also arise as a result of uncoordinated individual responses to the perceived risk of contagion. Here, we introduce a stochastic epidemic model implementing population responses driven by individual time-varying risk aversion. The model reveals an emergent mechanism for the generation of multiple infection waves of decreasing amplitude that progressively tune the effective reproduction number to its critical value R = 1. In successive waves, individuals with gradually lower risk propensity are infected. The overall mechanism shapes well-defined risk-aversion profiles over the whole population as the epidemic progresses. We conclude that uncoordinated changes in human behaviour can by themselves explain major qualitative and quantitative features of the epidemic process, like the emergence of multiple waves and the tendency to remain around R = 1 observed worldwide after the first few waves of COVID-19.

A Cost, Time, and Demographic Analysis of Participant Recruitment and Urine Sample Collection Through Social Media Optimization.

INTRODUCTION: Clinical research can be expensive and time consuming due to high associated costs and/or duration of the study. We hypothesized that urine sample collection using online recruitment and engagement of research participants via social medial has the potential to reach a large population in a small timeframe, at a reasonable cost. METHODS: We performed a retrospective cost analysis of a cohort study comparing cost per sample and time per sample for both online and clinically recruited participants for urine sample collection. During this time, cost data were collected based on study associated costs from invoices and budget spreadsheets. The data were subsequently analyzed using descriptive statistics. RESULTS: Each sample collection kit contained 3 urine cups, 1 for the disease sample and 2 for control samples. Out of the 3,576 (1,192 disease + 2,384 control) total sample cups mailed, 1,254 (695 control) samples were returned. Comparatively, the 2 clinical sites collected 305 samples. Although the initial startup cost of online recruitment was higher, cost per sample for online recruited was found to be $81.45 compared to $398.14 for clinic sample. CONCLUSIONS: We conducted a nationwide, contactless, urine sample collection through online recruitment in the midst of the COVID-19 pandemic. Results were compared with the samples collected in the clinical setting. Online recruitment can be utilized to collect urine samples rapidly, efficiently, and at a cost per sample that was 20% of an in-person clinic, and without risk of COVID-19 exposure.

Location Scheme of Routine Nucleic Acid Testing Sites Based on Location-Allocation Models: A Case Study of Shenzhen City

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.

Persistent asthma-like symptoms in COVID-19 patients 3 and 12 months after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)infection

Background: Post-acute COVID-19 syndrome is recognised as a complex systematic disease. There is limited information on asthma-like symptoms following acute COVID-19. Objective(s): We estimated prevalence and persistence of asthma-like symptoms at 3 and 12 months after acute COVID-19 as compared to a control population. Method(s): Community-based COVID-19 patients from the first pandemic wave in Bergen, Norway, were included in a longitudinal clinical study. At 3- and 12-months, 158 and 89 patients, respectively, also participated in an additional sub-study, which was harmonised with clinical follow-up of the community-based RHINESSA population (control) with 235 participants. The European Community Respiratory Heath Survey structured questionnaire on general characteristics, asthma and respiratory symptoms, was administered to both groups. The prevalence of respiratory symptoms in acute COVID-19 patients at two time points post infection was estimated, using logistic regression adjusted for sex, age and smoking. Result(s): In the COVID-19 patients, symptoms in the last 3 days were more common after 12 than after 3 months: wheeze 11.2% vs 3.2%, waking up with shortness of breath (SOB) 5.6% vs 0.6%, and night cough 10.1% vs 3.2%. A year after the infection, COVID-19 cases reported more asthma-like symptoms than the control population: waking up with SOB (odds ratio [OR], 95% confidence interval [CI]: 2.50, 1.10-5.68);wheeze in the last 3 days (OR, 95%CI: 5.00, 1.10-22.8);and waking up with cough in the last 3 days (OR, 95%CI: 1.80, 0.60-5.37). Conclusion(s): Our findings indicated that asthma-like symptoms persisted one year after acute COVID-19.

Intracellular "in silico microscopes"-fully 3D spatial Hepatitis C virus replication model simulations

Virus pandemics and endemics cause enormous pain and economic, political, and social costs and turmoil. While the Covid19 pandemics induced obvious damages, the "silent" Hepatitis C virus (HCV) infection induced liver damages are the main reason for liver transplantations. HCV-generated virus genome replication factories are housed within virus-induced intracellular structures termed membranous webs (MW) which are derived from the Endoplasmatic Reticulum (ER). Up to now, very advanced experimental data such as highly spatially resolved fuorescence and electron-tomography data often do not enter computational HCV viral RNA (vRNA) cycle models. Based upon difusion-reaction partial differential equation (PDE) models, we are developing fully 3D resolved "in silico microscopes" to mirror in vitro / in vivo experiments of the intracellular vRNA cycle dynamics. Our first models described the major components (vRNA, non-structural viral proteins-NSPs-and a host factor). The next steps incorporated additional parameters: Different aggregate states of vRNA and NSPs, and population dynamics inspired difusion and reaction co-Effcients instead of multilinear ones. Our work in progress framework presently is merging effects restricted to 2D manifold surface grids (e.g. ER surface, NSP difusion) with others occurring in 3D volume meshes (e.g. cytosol, host factor supply). We estimate and incorporate realistic parameters such as NSP difusion constants. The simulations are performed upon experimental data based reconstructed cell geometries and help understanding the relation of form and function of virus replication. In the long run, our framework might help to facilitate the systematic development of Effcient direct antiviral agents and vaccines.

Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.

Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics

Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.

Trends in rice research: 2030 and beyond

Rice production and research have met unprecedented challenges in recent years. Yield and total production have plateaued for many years in some major producing rice-producing countries while the demand from populations in poverty is ever increasing. For example, more than 100 million additional people became extremely poor, mostly from Asia and sub-Saharan Africa in 2020 alone. Rice is not only the calorie source for half of the global population but also the key staple food for the world's poorest and undernourished people living in Asia and Africa. In this review, we have analysed the trends in rice research in the past three decades, particularly on the mega-projects that attempted to revolutionize rice yield, sustainability and quality of both Asian (Oryza sativa) and African (O. glaberrima) rice, with their impact on rice cultivation. We have also analysed the trends in population growth, rice cultivation, production, price and consumption along with their projections for 2030 and beyond. Furthermore, we have analysed recent trends in variety release using Bangladesh as an example. Finally, we have identified the future challenges and priorities of rice research.

Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis

Telemedicine has become fundamental for the challenges posed to healthcare. This set of instruments turns pivotal for facing one of the most relevant emergencies in human history: the COVID-19 pandemic. The multisectoral crisis led to a vigorously sustained adoption of innovations, including telemedicine technology. Telehealth was proven, in this context, to be a relevant tool to reduce healthcare costs, reduce not-needed hospitalizations, and improve the results in health care. Some barriers such as the costs of technologies, patient privacy and technical literacy have slowed down telemedicine adoption. Amidst the COVID-19 era, telemedicine calls for a managerial duty to change healthcare's organizational models. The present work aims to explore the growing literature to illuminate the relationships between telemedicine, innovations and healthcare in the COVID-19 framework. A bibliometric analysis of the existing literature based on 285 published works in 2019–2020 is put forward with the aim to detect the relevant literature, themes and approaches on telemedicine and COVID-19. Making use of community detection on the co-occurrence keywords network, we identify the "semantic cores” in the literature representing the relevant results on critical themes. The sorting implications are important for researchers and policymakers by mapping the existing literature and results in evidence-based analysis. We provide the key communities as the "semantic core” of the publications and results for the considered period. This allows for future research to be oriented towards perduring health policies that could lead to the adoption of telemedicine technologies in a post-pandemic scenario. © 2021

Analysis of interactions between human immune system and a pathogenic virus

In this paper we present a mathematical model for studying the interactions between human immune systemand a pathogenic virus, such as Covid-19. Amathematical analysis based on dynamical systems theory is performed. More exactly, we model the interactions between the immune system and the virus by a modified predator-prey method. Several conclusions emerge from this study, and the main two of them are the followings: 1) a deficiency in the concentration of a single type of white blood cells in the early stages of virus proliferation may lead to the virus victory, and 2) if the number of at least one type of white blood cells can be increased beyond the normal threshold by medical interventions in the early stages of virus infection, then the immune system has a better chance to win against the virus. [ FROM AUTHOR]

Research on COVID-19 network public opinion events based on topic model and community detection algorithm

COVID-19 has caused a large number of online public opinion incidents. How to timely and effectively guide the resulting network public opinion has become an urgent problem to be solved. This paper collects more than 130,000 original Weibo posts during the Wuhan "city closure” incident, and analyses the topic characteristics of the incident on the basis of user classification through topic models and community detection algorithms. It was found that during this period, the government responded quickly to the epidemic and gained public support. For different Weibo users, officially certified users mainly publish information about the epidemic and epidemic prevention measures. Personally certified users mainly forwarded and transmitted official information actively, and they also expressed their opinions and made suggestions. Non-certified users actively expressed their emotions and opinions, so they were important users that reflect public opinion. © 2022 SPIE.

A Note on an Epidemic Model with Cautionary Response in the Presence of Asymptomatic Individuals

We analyse a simple disease transmission model accounting for demographic features and an illness appearing in two forms, asymptomatic and symptomatic. Its main feature is the epidemic-induced fear of the population, for which contacts are reduced, responding to increasing symptomatic numbers. We find that in the presence of asymptomatic individuals, if the progression rate to symptomatic is high, protection measures may prevent the whole population becoming infected. The results also elucidate the importance of assessing transmission rates as quickly as possible.

Effective Analysis and Investigation in COVID-19 Prevention Policies Based on Population Dynamics Modeling and Simulation

The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.

COVID-19 Visualization Platform Based on Population Density Propagation Model

Based on the classical SIR model and CEMM intercity model, a new model was established by adding "population density"parameter to analyze and predict the spread of virus. In addition, the current trend of the epidemic and forecast data can be referenced to the public in an intuitive web view to improve the perception of risk information in the society. The real-time epidemic data interface was adopted to analyze the real-time pneumonia epidemic data captured by the deployment of timing crawler combined with the regional population density to build a model. Then, the diversified charts, Python and Web front-end technologies were used to realize the visualization of epidemic information. COVID-19 grows exponentially without obstruction, and when a place has a high population density, the spread of the virus accelerates and the number of people infected increases. The research shows that the integration of population density parameters can further improve the epidemic prediction function, provide epidemic data reference in a more effective and accurate way, and further improve the public's ability to perceive social risk information. © 2022 IEEE.

Impact of Migration on COVID-19 Epidemic Process in Romania: A Simulation Study

The Russian war in Ukraine, which escalated on February 24, 2022, caused massive destruction and the death of thousands of people. In addition, the Russian invasion has affected the public health system and the spread of infectious diseases. Millions of Ukrainians fled from the war, which caused a pan-European migration crisis. This study is devoted to testing the hypothesis of the impact of population migration caused by the Russian war in Ukraine on the dynamics of the spread of COVID-19 in Romania. For this, a machine learning model was developed based on the polynomial regression method. The model showed high accuracy. However, the formulated hypothesis was not confirmed fully. The results of the experimental study showed that population migration have not impacted the fatality caused by COVID-19, but has the impact on COVID-19 new cases. The further investigation is needed to find out the exact factors which influenced the epidemic process. © 2022 IEEE.

[COVID-19 exposure setting, social and gender determinants in a mediterranean region.]

OBJECTIVE: Knowledge of social and gender determinants, which influence the places where people are exposed to COVID-19, may be relevant in the development of preventive and control strategies. The aim of this paper was to determine the context in which COVID-19 cases were infected (household, work/labor, health, social-health, and social-leisure settings) according to country of origin, occupational social class and gender, which is essential in order to designing public health strategies.