Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 58
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    Effects of COVID-19 in Endocrine Patients: Results of a Sicilian Experience

    Authors: Elisabetta Morini; Rosanna Palmeri; Giuseppa Maresca; Lilla Bonanno; Maria Cristina De Cola; Adriana Andaloro; Santina Caliri; Placido Bramanti; Francesco Corallo

    id:10.20944/preprints202008.0041.v1 Date: 2020-08-02 Source: Preprints.org

    In March 2020 the World Health Organization declared the “pandemic state” due to COVID-19 imposing strict confinement of the world population. People were forced to spend more time at home, changing some daily routines, including social interactions HP social interactions TRANS, the possibility to perform sports, and diet habits. These changes could exert a greater impact on patients suffering from chronic diseases MESHD, such as endocrine patients. This study aimed to assess the effects of Covid-19 induced quarantine on daily habits in a group of patients with endocrine disorders, focusing on food consumption, eating, and sleep habits during the confinement. Eighty-five endocrine patients were enrolled. A structured interview was administered investigating: socio-demographic information, general medical conditions and habits adopted during the quarantine. All patients underwent the Spielberger State Anxiety HP Inventory (STAI-Y1) to assess state anxiety HP. Subjects had mainly a sedentary lifestyle. We found a significant increase in the number of cigarettes in smokers, an increase of meals consumed during the confinement and a high rate of sleep disorder occurrence, especially insomnia HP. The changes of daily habits were, probably, due to the alterations of routine, that determined more bore and inactivity during the day.

    SABCoM: A Spatial Agent-Based Covid-19 Model

    Authors: Allan Davids; Gideon Du Rand; Co-Pierre Georg; Tina Koziol; Joeri Anton Schasfoort

    doi:10.1101/2020.07.30.20164855 Date: 2020-08-01 Source: medRxiv

    How effective are 'lockdown' measures and other policy interventions to curb the spread of Covid-19 in emerging market cities that are characterized by large heterogeneity and high levels of informality? The most commonly used models to predict the spread of Covid-19 are SEIR models which lack the spatial resolution necessary to answer this question. We develop an agent-based model of social interactions HP social interactions TRANS in which the distribution of agents across wards, as well as their travel TRANS and interactions are calibrated to real data for Cape Town, South Africa. We characterize the elasticity of various policy interventions including increased likelihood to self-isolate, travel TRANS restrictions, assembly bans, and behavioural interventions like washing hands or wearing masks. Even in an informal setting, where agents' ability to self-isolate is compromised, a lockdown remains an effective intervention. In our model, the lockdown enacted in South Africa reduced expected fatalities in Cape Town by 26% and the expected demand for intensive care beds by 46%. However, our best calibration predicts a substantially higher case load, demand for ICU beds, and expected number of deaths MESHD than the current best estimate published for Cape Town.

    The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19

    Authors: Hokky Situngkir

    doi:10.1101/2020.07.27.20162511 Date: 2020-07-29 Source: medRxiv

    Various social policies and strategies have been deliberated and used within many countries to handle the COVID-19 pandemic. Some of those basic ideas are strongly related to the understanding of human social interactions HP social interactions TRANS and the nature of disease MESHD transmission TRANS and spread. In this paper, we present an agent- based approach to model epidemiological phenomena as well as the interventions upon it. We elaborate on micro-social structures such as social-psychological factors and distributed ruling behaviors to grow an artificial society where the interactions among agents may exhibit the spreading of the virus. Capturing policies and strategies during the pandemic, four types of intervention are also applied in society. Emerged macro-properties of epidemics are delivered from sets of simulations, lead to comparisons between each effectivity of the policy/strategy.

    A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

    Authors: Pietro Coletti; Pieter Libin; Oana Petrof; Lander Willem; Abrams Steven; Sereina A. Herzog; Christel Faes; James Wambua; Elise J. Kuylen; Philippe Beutels; Niel Hens

    doi:10.1101/2020.07.20.20157933 Date: 2020-07-25 Source: medRxiv

    In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age TRANS-structured population of Belgium. The model is calibrated to daily hospitalization data and serological data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data TRANS collected during and after the lockdown. Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections MESHD, and find that it allows re-establishing relatively more social interactions HP social interactions TRANS while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection MESHD has changed with respect to the pre-lockdown period. Community contacts are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment is crucial to adjust to evolving behavioral changes that can affect epidemic diffusion. In addition to social distancing, sufficient capacity for extensive testing and contact tracing TRANS is essential for successful mitigation.

    Backtesting the predictability of COVID-19

    Authors: Dmitry Gordeev; Philipp Singer; Marios Michailidis; Mathias Müller; SriSatish Ambati

    id:2007.11411v1 Date: 2020-07-22 Source: arXiv

    The advent of the COVID-19 pandemic has instigated unprecedented changes in many countries around the globe, putting a significant burden on the health sectors, affecting the macro economic conditions, and altering social interactions HP social interactions TRANS amongst the population. In response, the academic community has produced multiple forecasting models, approaches and algorithms to best predict the different indicators of COVID-19, such as the number of confirmed infected cases. Yet, researchers had little to no historical information about the pandemic at their disposal in order to inform their forecasting methods. Our work studies the predictive performance SERO of models at various stages of the pandemic to better understand their fundamental uncertainty and the impact of data availability on such forecasts. We use historical data of COVID-19 infections MESHD from 253 regions from the period of 22nd January 2020 until 22nd June 2020 to predict, through a rolling window backtesting framework, the cumulative number of infected cases for the next 7 and 28 days. We implement three simple models to track the root mean squared logarithmic error in this 6-month span, a baseline model that always predicts the last known value of the cumulative confirmed cases TRANS, a power growth model and an epidemiological model called SEIRD. Prediction errors are substantially higher in early stages of the pandemic, resulting from limited data. Throughout the course of the pandemic, errors regress slowly, but steadily. The more confirmed cases TRANS a country exhibits at any point in time, the lower the error in forecasting future confirmed cases TRANS. We emphasize the significance of having a rigorous backtesting framework to accurately assess the predictive power of such models at any point in time during the outbreak which in turn can be used to assign the right level of certainty to these forecasts and facilitate better planning.

    A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing TRANS

    Authors: Norman Fenton; Scott McLachlan; Peter Lucas; Kudakwashe Dube; Graham Hitman; Magda Osman; Evangelia Kyrimi; Martin Neil

    doi:10.1101/2020.07.15.20154286 Date: 2020-07-19 Source: medRxiv

    Concerns about the practicality and effectiveness of using Contact Tracing TRANS Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. One of the key non-technical obstacles to widespread adoption of CTA has been concerns about privacy. We present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that does not compromise privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions HP social interactions TRANS. The model then provides them feedback about the likelihood of the presence of asymptotic TRANS, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

    COVID-19 Pandemic among Latinx Farmworker and Non-farmworker Families in North Carolina: Knowledge, Risk Perceptions, and Preventive Behaviors

    Authors: Sara A. Quandt; Natalie J. LaMonto; Dana C. Mora; Jennifer W. Talton; Paul J. Laurienti; Thomas A. Arcury

    doi:10.1101/2020.07.14.20153429 Date: 2020-07-17 Source: medRxiv

    (1) Background: The COVID-19 pandemic poses substantial threats to Latinx farmworkers and other immigrants in food production and processing. Classified as essential, such workers cannot shelter at home. Therefore, knowledge and preventive behaviors are important to reduce COVID-19 spread in the community. (2) Methods: Respondents for 67 families with at least one farmworker (FWF) and 38 comparable families with no farmworkers (non-FWF) in North Carolina completed a telephone survey in May, 2020. The survey queried knowledge of COVID-19, perceptions of its severity, self-efficacy, and preventive behaviors. Detailed data were collected to document household members' social interaction HP social interaction TRANS and use of face coverings. (3) Results: Knowledge of COVID-19 and prevention methods was high in both groups, as was its perceived severity. Non-FWF had higher self-efficacy for preventing infection MESHD. Both groups claimed to practice preventive behaviors, though FWF emphasized social avoidance and non-FWF emphasized personal hygiene. Detailed social interactions HP social interactions TRANS showed high rates of inter-personal contact at home, at work, and in the community with more mask use in non-FWF than FWF. (4) Conclusions: Despite high levels of knowledge and perceived severity for COVID-19, these immigrant families were engaged in frequent interpersonal contact that could expose community members and themselves to COVID-19.

    Age TRANS-specific social mixing of school- aged TRANS children TRANS in a US setting using proximity detecting sensors and contact surveys

    Authors: Kyra H. Grantz; Derek A.T. Cummings; Shanta Zimmer; Charles Vukotich Jr.; David Galloway; Mary Lou Schweizer; Hasan Guclu; Jennifer Cousins; Carrie Lingle; Gabby M.H. Yearwood; Kan Li; Patti A Calderone; Eva Noble; Hongjiang Gao; Jeanette Rainey; Amra Uzicanin; Jonathan M. Read

    doi:10.1101/2020.07.12.20151696 Date: 2020-07-14 Source: medRxiv

    Comparisons of the utility and accuracy of methods for measuring social interactions HP social interactions TRANS relevant to disease MESHD transmission TRANS are rare. To increase the evidence base supporting specific methods to measure social interaction HP social interaction TRANS, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age TRANS-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age TRANS-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection TRANS risk of infection TRANS infection MESHD by age TRANS. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age TRANS-specific mixing patterns relevant to the dynamics of respiratory virus transmission TRANS.

    Dynamic Graph Streaming Algorithm for Digital Contact Tracing TRANS

    Authors: Gautam Mahapatra; Priodyuti Pradhan; Ranjan Chattaraj; Soumya Banerjee

    id:2007.05637v2 Date: 2020-07-10 Source: arXiv

    Digital contact tracing TRANS of an infected person, testing the possible infection MESHD for the contacted persons, and isolation play a crucial role in alleviating the outbreak. Here, we design a dynamic graph streaming algorithm that can trace the contacts TRANS under the control of the Public Health Authorities (PHA). The algorithm can work as the augmented part of the PHA for the crisis period. Our algorithm receives proximity data from the mobile devices as contact data TRANS streams and uses a sliding window model to construct a dynamic contact graph sketch. Prominently, we introduce the edge label of the contact graph as a contact vector, which acts like a sliding window and holds the latest D days of social interactions HP social interactions TRANS. Importantly, the algorithm prepares the direct and indirect (multilevel) contact list from the contact graph sketch for a given set of infected persons. The algorithm also uses a disjoint set data structure to construct the infectious trees for the trace TRANS list. The present study offers the design of algorithms with underlying data structures for digital contact trace TRANS relevant to the proximity data produced by Bluetooth enabled mobile devices. Our analysis reveals that for COVID-19 close contact TRANS parameters, the storage space requires maintaining the contact graph of ten million users having 14 days close contact TRANS data in PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list.

    Adolescents' health literacy, health protective measures, and health-related quality of life during the Covid-19 pandemic

    Authors: Kirsti Riiser; Solvi Helseth; Kristin Haraldstad; Astrid Torbjornsen; Kare Ronn Richardsen

    doi:10.1101/2020.07.08.20148916 Date: 2020-07-10 Source: medRxiv

    Purpose: First, to describe adolescents' health information sources and knowledge, health literacy (HL), health protective measures, and health-related quality of life (HRQoL) during the initial phase of the Covid-19 pandemic in Norway. Second, to investigate the association between HL and the knowledge and behavior relevant for preventing spread of the virus. Third, to explore variables associated with HRQoL in a pandemic environment. Methods: This cross-sectional study includes survey data from 2,205 Norwegian adolescents 16-19 years of age TRANS. The participants reported on their health information sources, HL, handwashing knowledge and behavior, number of social interactions HP social interactions TRANS, and HRQoL. Associations between study variables and specified outcomes were explored using multiple linear and logistic regression analyses. Results: Television (TV) and family were indicated to be the main sources for pandemic-related health information. Handwashing, physical distancing, and limiting the number of social contacts were the most frequently reported measures. HL and handwashing knowledge and HL and handwashing behavior were significantly associated. For each unit increase on the HL scale, the participants were 5% more likely to socialize less with friends TRANS in comparison to normal. The mean HRQoL was very poor compared to European norms. Being quarantined or isolated and having confirmed or suspected Covid-19 were significantly negatively associated with HRQoL, but seeing less friends TRANS than normal was not associated. HL was significantly positively associated with HRQoL, albeit of minor clinical importance. Conclusion: Adolescents follow the health authorities' guidelines and appear highly literate. However, high fidelity requires great sacrifice because the required measures seem to collide with certain aspects that are important for the adolescents' HRQoL.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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