Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 1017
    records per page




    Implications of the COVID-19 pandemic on eliminating trachoma MESHD as a public health problem

    Authors: Seth Blumberg; Anna Borlase; Joaquin M Prada; Anthony W Solomon; Paul Emerson; Pamela J Hooper; Michael S. Deiner; Benjamin Amoah; Deirdre S. Hollingsworth; Travis C Porco; Thomas M Lietman; Sarah Caddy; Anna Yakovleva; Grant Hall; Fahad A Khokhar; Theresa Feltwell; Malte Pinckert; Iliana Georgana; Yasmin Chaudhry; Martin Curran; Surendra Parmar; Dominic Sparkes; Lucy Rivett; Nick K Jones; Sushmita Sridhar; Sally Forest; Tom Dymond; Kayleigh Grainger; Chris Workman; Effrossyni Gkrania-Klotsas; Nicholas M Brown; Michael Weekes; Stephen Baker; Sharon J Peacock; Theodore Gouliouris; Ian G. Goodfellow; Daniela de Angelis; M. Estee Torok

    doi:10.1101/2020.10.26.20219691 Date: 2020-10-27 Source: medRxiv

    Background: Progress towards elimination of trachoma MESHD as a public health problem has been substantial, but the COVID-19 pandemic has disrupted community-based control efforts. Methods: We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence SERO of active trachoma MESHD. Results: We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number TRANS and the treatment-associated reproduction number TRANS are above or below a value of one. We find that when the basic reproduction number TRANS is below one, no significant delays in disease control will be caused. However, when the basic reproduction number TRANS is above one, significant delays can occur. In most districts a year of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection MESHD control because the current strategies will not eliminate disease. Conclusion: If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma MESHD as a public health problem could be accelerated.

    Adaptive COVID-19 Forecasting via Bayesian Optimization

    Authors: Nayana Bannur; Harsh Maheshwari; Sansiddh Jain; Shreyas Shetty; Srujana Merugu; Alpan Raval; Luis Odorico Monteiro de Andrade; Leuridan Torres; Flavia Kelly Alvarenga Pinto; Francisco Marto Leal Pinheiro-Junior; Rebeca Valentim Leite; Amanda Carolina Abreu Felix Cavalcanti de Abreu; Rebecca Lucena Theophilo; Fernando Rodrigues Magalhaes; Susane Lindinalva da Silva; Carl Kendall

    doi:10.1101/2020.10.19.20215293 Date: 2020-10-27 Source: medRxiv

    Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number TRANS. In the current work, we propose a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria. Empirical results demonstrate the efficacy of the proposed approach with SEIR-like compartmental models on COVID-19 case forecasting tasks. A city-level forecasting system based on this approach is being used for COVID-19 response in a few highly impacted Indian cities.

    Detecting COVID-19 infection MESHD hotspots in England using large-scale self-reported data from a mobile application

    Authors: Thomas Varsavsky; Mark S Graham; Liane S Canas; Sajaysurya Ganesh; Joan Capdevila Puyol; Carole H Sudre; Benjamin Murray; Marc Modat; M. Jorge Cardoso; Christina M Astley; David A Drew; Long H Nguyen; Tove Fall; Maria F Gomez; Paul W Franks; Andrew T Chan; Richard Davies; Jonathan Wolf; Claire J Steves; Tim D Spector; Sebastien Ourselin

    doi:10.1101/2020.10.26.20219659 Date: 2020-10-27 Source: medRxiv

    Background As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence SERO and effective reproduction number TRANS. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots. Findings More than 2.6 million app users in England provided 115 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence SERO showed similar sensitivity SERO to changes as two national community surveys: the ONS and REACT studies. On a geographically granular level, our estimates were able to highlight regions before they were subject to local government lockdowns. Between 12 May and 29 September we were able to flag between 35-80% of regions appearing in the Government's hotspot list. Interpretation Self-reported data from mobile applications can provide a cost-effective and agile resource to inform a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance.

    Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data

    Authors: Sarah Nadeau; Christiane Beckmann; Ivan Topolsky; Timothy Vaughan; Emma Hodcroft; Tobias Schaer; Ina Nissen; Natascha Santacroce; Elodie Burcklen; Pedro Ferreira; Kim Philipp Jablonski; Susana Posada-Cespedes; Vincenzo Capece; Sophie Seidel; Noemi Santamaria de Souza; Julia M. Martinez-Gomez; Phil Cheng; Philipp Bosshard; Mitchell P. Levesque; Verena Kufner; Stefan Schmutz; Maryam Zaheri; Michael Huber; Alexandra Trkola; Samuel Cordey; Florian Laubscher; Ana Rita Goncalves; Karoline Leuzinger; Madlen Stange; Alfredo Mari; Tim Roloff; Helena Seth-Smith; Hans Hirsch; Adrian Egli; Maurice Redondo; Olivier Kobel; Christoph Noppen; Niko Beerenwinkel; Richard A. Neher; Christian Beisel; Tanja Stadler

    doi:10.1101/2020.10.14.20212621 Date: 2020-10-27 Source: medRxiv

    Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases TRANS per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission TRANS before the first confirmed case TRANS. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number TRANS, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.

    Transmission TRANS of COVID-19 in the state of Georgia, United States: Spatiotemporal variation and impact of social distancing

    Authors: Yuke Wang; Casey Siesel; Yangping Chen; Ben Lopman; Laura Edison; Michael Thomas; Carly Adams; Max SY Lau; Peter F.M. Teunis; Jean-Louis Mege; Jean-Marc Busnel; Joana Vitte

    doi:10.1101/2020.10.22.20217661 Date: 2020-10-26 Source: medRxiv

    Background Beginning in early February 2020, COVID-19 spread across the state of Georgia leading to 258,354 cumulative cases as of August 25, 2020. The time scale of spreading (i.e., serial interval TRANS) and magnitude of spreading (i.e., Rt or reproduction number TRANS) for COVID-19, were observed to be heterogenous by demographic characteristics, region and time period. In this study, we examined the COVID-19 transmission TRANS in the state of Georgia, United States. Methods During February 1-July 13, 2020, we identified 4080 transmission TRANS pairs using contact information from reports of COVID-19 cases from the Georgia Department of Public Health. We examined how various transmission TRANS characteristics were affected by disease symptoms, demographics ( age TRANS, gender TRANS, and race), and time period (during shelter-in-place and after reopening). In addition, we estimated the time course of reproduction numbers TRANS during early February-mid-June for all 159 counties in the state of Georgia, using a total of 118,491 reported COVID-19 cases. Findings Over this period, the serial interval TRANS appeared to decrease from 5.97 days in February-April to 4.40 days in June-July. With regard to age TRANS, transmission TRANS was assortative and patterns of transmission TRANS changed over time. COVID-19 mainly spread from adults TRANS to all age groups TRANS; transmission TRANS among and between children TRANS and the elderly TRANS was found less frequently. Younger adults TRANS (20-50 years old) were involved in the majority of transmissions TRANS occurring during or after reopening subsequent to the shelter-in-place period. By mid-July, two waves of COVID-19 transmission TRANS were apparent, separated by the shelter-in-place period in the state of Georgia. Counties around major cities and along interstate highways had more intense transmission TRANS. Interpretation The transmission TRANS of COVID-19 in the state of Georgia had been heterogeneous by area and changed over time. The shelter-in-place was not long enough to sufficiently suppress COVID-19 transmission TRANS in densely populated urban areas connected by major transportation links. Studying local transmission TRANS patterns may help in predicting and guiding states in prevention and control of COVID-19 according to population and region. Funding Emory COVID-19 Response Collaborative.

    Coronavirus Disease MESHD (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

    Authors: Sina Ardabili; Amir MOSAVI; Shahab S. Band; Annamaria R. Varkonyi-Koczy; Laura Edison; Michael Thomas; Carly Adams; Max SY Lau; Peter F.M. Teunis; Jean-Louis Mege; Jean-Marc Busnel; Joana Vitte

    doi:10.1101/2020.10.22.20217604 Date: 2020-10-26 Source: medRxiv

    An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases MESHD. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient ( r) values TRANS. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

    Coronavirus Disease MESHD (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

    Authors: Sina Ardabili; Amir Mosavi; Shahab S. Band; Annamaria R. Varkonyi-Koczy

    id:10.20944/preprints202010.0519.v1 Date: 2020-10-26 Source: Preprints.org

    An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases MESHD. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient ( r) values TRANS. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

    Mathematical Analysis of COVID-19 Transmission TRANS Dynamics. A Case Study with Nigeria

    Authors: Agbata Celestine Benedict; Ogala Emmanuel; Bashir Tenuche; William William-Denteh; David Harte; Michelle Sutherland; Matthew Parry; Erasmus Smit; Gary McAuliffe; James Ussher; Nicole Moreland; Susan Jack; Arlo Upton; Danielle Skinner; Ken Hirata; Sungjun Beck; Aaron F Carlin; Alex E. Clark; Laura Berreta; Daniel Maneval; Felix Frueh; Brett L Hurst; Hong Wang; Klaudia I Kocurek; Frank M Raushel; Jair L. Siqueira-Neto; Thomas D Meek; James H McKerrow

    doi:10.1101/2020.10.20.20216473 Date: 2020-10-23 Source: medRxiv

    Abstract: In this article, we formulated a mathematical model for the spread of the COVID-19 disease and we introduced quarantined and isolated compartments. The next generation matrix method was adopted to compute the basic reproduction number TRANS in order to assess the transmission TRANS dynamics of the COVID-19 deadly disease. Stability analysis of the disease free equilibrium is investigated based on the basic reproduction number TRANS and the result shows that it is locally and asymptotically stable for less than 1. Numerical calculation of the basic reproduction number TRANS revealed that which means that the disease can be eradicated from Nigeria. The study shows that isolation, quarantine and other government policies like social distancing and lockdown are the best approaches to control the pernicious nature of COVID-19 pandemic. Key words: mathematical model, basic reproduction number TRANS, isolation, quarantine, COVID-19, computer simulation

    Prediction of the infection of COVID-19 in Bangladesh by classical SIR model

    Authors: Sofi Mahmud Parvez; Faria Tabassum; H. M. Shahadat Ali; Md. Murad Hossain; Kristin Y. Shiue; Chaney Kalinich; Sarah Jednak; Isabel Ott; Chantal Vogels; Jay Wohlgemuth; James Weisberger; John DiFiori; Deverick J. Anderson; Jimmie Mancell; David Ho; Nathan D. Grubaugh; Yonatan H. Grad; Riina Janno; Irja Lutsar; Raissa Prado Rocha; Alex Fiorini de Carvalho; Pedro Augusto Alves; Jose Luiz Proenca Modena; Artur Torres Cordeiro; Daniela Barreto Barbose Trivella; Rafael Elias Marques; Ronir R Luiz; Paolo Pelosi; Jose Roberto Lapa e Silva

    doi:10.1101/2020.10.21.20216846 Date: 2020-10-23 Source: medRxiv

    The ongoing outbreak of the novel coronavirus (COVID-19) started from Wuhan, China, at the end of December 2019. It is one of the leading public health challenges in the world because of high transmissibility TRANS. The first patient of COVID-19 was officially reported on March 8, 2020, in Bangladesh. Using the epidemiological data up to October 17, 2020, we try to estimate the infectious size. In this paper, we used Classical SIR (Susceptible- Infected-Recovered), model. The epidemic has now spread to more than 216 countries around the world. The necessary reproduction number TRANS R_o of Bangladesh is 1.92. The primary data was collected from the Coronavirus (COVID-19) Dashboard (BANGLADESH: CASE TREND). In our analysis, the statistical parameters specify the best import to provide the predicted result. We projected that the epidemic curve pulling down in Bangladesh will start from the first week of November (November 4, 2020) and may end in the last week of July (July 24, 2021). It is also estimated that the start of acceleration on May 24, 2020, in 53 days, and the start of steady growth on September 10, 2020, in 109 days. The start of the ending phase of the epidemic may appear in the first week of November 2020, and the epidemic is expected to be finished by the last week of July 2021. However, these approximations may become invalid if a large variety of data occurs in upcoming days.

    Simulation and prediction of further spread of COVID-19 in The Republic of Serbia by SEIRDS model of disease transmission TRANS

    Authors: Slavoljub Grozdan Stanojevic; Mirza Ponjavic; Slobodan Stanojevic; Aleksandar Stevanovic; Sonja Radojicic; Beatriz Perazzi; Sergio Villordo; Diego Alvarez; - BioBanco Working Group; Marcela Echavarria; Kasopefoluwa Y. Oguntuyo; Christian Stevens; Benhur Lee; Jorge Carradori; Julio Caramelo; Marcelo Yanovsky; Andrea Gamarnik; Bart N Lambrecht; Lynda Coughlan; Adolfo Garcia-Sastre; Bruno G De Geest; Michael Schotsaert; Marion Yger; Bertrand Degos; Louise-Laure Mariani; Christophe Bouche; Nathalie Dzierzynski; Bruno Oquendo; Flora Ketz; An-Hung Nguyen; Aurelie Kas; Jean-Yves Delattre; Jean-Christophe Corvol

    doi:10.1101/2020.10.21.20216986 Date: 2020-10-23 Source: medRxiv

    As a response to the pandemic caused by SARSCov-2 virus, on 15 March, 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID 19. After a slowdown in the epidemic, on 6 May, 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 15 October, 2020, a total of 35,454 cases of SARSCov-2 infection MESHD have been reported in Serbia, including 770 deaths MESHD caused by COVID19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed a mathematical model SEIRDS (S-susceptible, E-exposed, I-infected MESHD, R-recovered, D-dead due to COVID19 infection MESHD, S-susceptible). When developing the model, we took into account the differences between different population strata, which can impact the disease dynamics and outcome. The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID19, the model is enabled to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID19 and reduce number of deaths MESHD. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number TRANS of Ro=2.46 and vaccine efficacy of 68%, an 87%- coverage would be sufficient to stop the virus circulation.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.