Corpus overview


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MeSH Disease

Transmission

Seroprevalence
    displaying 1 - 10 records in total 1044
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    A COVID-19 MESHD Model for Local Authorities of the United Kingdom

    Authors: Swapnil Mishra; Jamie Scott; Harrison Zhu; Neil M Ferguson; Samir Bhatt; Seth Flaxman; Axel Gandy; Fredrik Nyberg; Waheed-Ul-Rahman Ahmed; Osaid Alser; Heba Alghoul; Thamir Alshammari; Lin Zhang; Paula Casajust; Carlos Areia; Karishma Shah; Christian Reich; Clair Blacketer; Alan Andryc; Stephen Fortin; Karthik Natarajan; Mengchun Gong; Asieh Golozar; Daniel Morales; Peter Rijnbeek; Vignesh Subbian; Elena Roel; Martina Recalde; Jennifer C.E. Lane; David Vizcaya; Jose D. Posada; Nigam H. Shah; Jitendra Jonnagaddala; Lana Yin Hui Lai; Francesc Xavier Aviles-Jurado; George Hripcsak; Marc A. Suchard; Otavio T. Ranzani; Patrick Ryan; Daniel Prieto-Alhambra; Kristin Kostka; Talita Duarte-Salles

    doi:10.1101/2020.11.24.20236661 Date: 2020-11-27 Source: medRxiv

    We propose and describe a model for the COVID-19 MESHD epidemic of the United Kingdom at the level of local authorities. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic, with some important innovations: for example, we estimate the proportion of infections resulting in deaths MESHD and reported cases and we model the infections explicitly as random variables. The model is designed to be updated daily based on publicly available data. We envisage the model to be useful for short term projections of the epidemic over the next few weeks and to estimate past local values such as the reproduction number TRANS of the epidemic in the past. The model fits are available on a public website,https://imperialcollegelondon.github.io/ covid19 MESHDlocal. The model is currently being used by the Scottish government in their decisions on interventions within Scotland [1,issue 24 to now]

    The effectiveness of the three tier system of local restrictions for control of COVID-19 MESHD

    Authors: Paul R Hunter; Julii Suzanne Brainard; Alastair Grant; Phil Fang Cheng; Elisa Bellini; Pål Johansen; Agathe Duda; Stefan Nobbe; Reto Lienhard; Philipp Peter Bosshard; Mitchell Paul Levesque; Daniel Muema; Dirhona Ramjit; Gila Lustig; Thumbi Ndung'u; Willem Hanekom; Bernadett I Gosnell; COMMIT-KZN Team; Emily Wong; Tulio de Oliveira; Mahomed-Yunus S Moosa; Alasdair Leslie; Henrik Kloverpris; Alex Sigal

    doi:10.1101/2020.11.22.20236422 Date: 2020-11-24 Source: medRxiv

    Despite it being over 10 months since COVID-19 MESHD was first reported to the world and it having caused over 1.3 million deaths it is still uncertain how the virus can be controlled whilst minimising the negative impacts on society and the economy. On the 14th October England introduced a three-tier system of regional restrictions in an attempt to control the epidemic. This lasted until the 5th November when a new national lockdown was imposed. Tier 1 was the least and tier 3 the most restrictive tiers. We used publicly available data of daily cases by local authority (local government areas) and estimated the reproductive rate ( R value TRANS) of the epidemic over the previous 14 days at various time points after the imposition of the tier system or where local authorities were moved into higher tiers at time points after reallocation. At day 0 there vas very little difference in the R value TRANS between authorities in the different groups but by day 14 the R value TRANS in tier 3 authorities had fallen HP to about 0.9, in tier 2 to about 1.0 and in tier 1 the R value TRANS was about 1.5. The restrictions in tier 1 had little impact on transmission TRANS and allowed exponential growth in the large majority of authorities. By contrast the epidemic was declining in most tier 3 authorities. In tier 2, exponential growth was being seen in about half of authorities but declining in half. We concluded that the existing three tier system would have been sufficient to control the epidemic if all authorities had been moved out of tier 1 into tier 2 and there had been more rapid identification and transfer of those authorities where the epidemic was increasing out of tier 2 into tier 3. A more restrictive tier than tier 3 may be needed but only by a small number of authorities.

    COVID-19 MESHD: Short term prediction model using daily incidence data

    Authors: Hongwei Zhao; Naveed N Merchant; Alyssa McNulty; Tiffany Radcliff; Murray J Cote; Rebecca Fischer; Huiyan Sang; Marcia G Ory; Peter Bentzer; Areti Angeliki Veroniki; Lehana Thabane; Fanlong Bu; Sarah Klingenberg; Christian Gluud; Janus Christian Jakobsen; Willem Hanekom; Bernadett I Gosnell; COMMIT-KZN Team; Emily Wong; Tulio de Oliveira; Mahomed-Yunus S Moosa; Alasdair Leslie; Henrik Kloverpris; Alex Sigal

    doi:10.1101/2020.11.23.20237024 Date: 2020-11-24 Source: medRxiv

    Background: Prediction of the dynamics of new SARS-CoV-2 infections MESHD during the current COVID-19 pandemic MESHD is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods: Our approach to forecasting future COVID-19 MESHD cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number TRANS assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission TRANS rate will stay the same, or change by a certain degree. Results: We apply our method to predicting the number of new COVID-19 MESHD cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number TRANS is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission TRANS over time. Conclusion: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

    Effect of hot zone infection outbreaks on the dynamics of SARS-CoV-2 spread in the community at large

    Authors: Dominik Wodarz; Natalia L. Komarova; Luis M. Schang; Daan J.L. van Twist; Peter W. de Leeuw; Jacqueline Buijs; Johan Holgersson; Niklas Nielsen; Peter Bentzer; Areti Angeliki Veroniki; Lehana Thabane; Fanlong Bu; Sarah Klingenberg; Christian Gluud; Janus Christian Jakobsen; Willem Hanekom; Bernadett I Gosnell; COMMIT-KZN Team; Emily Wong; Tulio de Oliveira; Mahomed-Yunus S Moosa; Alasdair Leslie; Henrik Kloverpris; Alex Sigal

    doi:10.1101/2020.11.23.20237172 Date: 2020-11-24 Source: medRxiv

    Transmission TRANS of SARS-CoV-2 appears especially effective in "hot zone" locations where individuals interact in close proximity. We present mathematical models describing two types of hot zones. First, we consider a metapopulation model of infection spread where transmission TRANS hot zones are explicitly described by independent demes in which the same people repeatedly interact (referred to as "static" hot zones, e.g. nursing homes, food processing plants, prisons, etc.). These are assumed to exists in addition to a "community at large" compartment in which virus transmission TRANS is less effective. This model yields a number of predictions that are relevant to interpreting epidemiological patterns in COVID19 MESHD data. Even if the rate of community virus spread is assumed to be relatively slow, outbreaks in hot zones can temporarily accelerate initial community virus growth, which can lead to an overestimation of the viral reproduction number TRANS in the general population. Further, the model suggests that hot zones are a reservoir enabling the prolonged persistence of the virus at " infection plateaus MESHD" following implementation of non-pharmaceutical interventions, which has been frequently observed in data. The second model considers "dynamic" hot zones, which can repeatedly form by drawing random individuals from the community, and subsequently dissolve (e.g. restaurants, bars, movie theaters). While dynamic hot zones can accelerate the average rate of community virus spread and can provide opportunities for targeted interventions, they do not predict the occurrence of infection plateaus MESHD or other atypical epidemiological dynamics. The models therefore identify two types of transmission TRANS hot zones with very different effects on the infection dynamics, which warrants further epidemiological investigations.

    Demography, social contact patterns and the COVID-19 MESHD burden in different settings of Ethiopia: a modeling study

    Authors: Filippo Trentini; Giorgio Guzzetta; Margherita Galli; Agnese Zardini; Fabio Manenti; Giovanni Putoto; Valentina Marziano; Worku Gamshie Nigussa; Ademe Tsegaye; Alessandro Greblo; Alessia Melegaro; Marco Ajelli; Stefano Merler; Piero Poletti; Amit Joshi; Andrew Chan; Jakob Cramer; Tim Spector; Jonathan Wolf; Sebastien Ourselin; Claire Steves; Albert Loeliger; Henrik Kloverpris; Alex Sigal

    doi:10.1101/2020.11.24.20237560 Date: 2020-11-24 Source: medRxiv

    Background COVID-19 MESHD spread may have a dramatic impact in countries with vulnerable economies and limited availability of, and access to, healthcare resources and infrastructures. However, in sub-Saharan Africa a low prevalence SERO and mortality have been observed so far. Methods We collected data on individual social contacts in Ethiopia across geographical contexts characterized by heterogeneous population density, work and travel TRANS opportunities, and access to primary care. We assessed how socio-demographic factors and observed mixing patterns can influence the COVID-19 MESHD disease burden, by simulating SARS-CoV-2 transmission TRANS in remote settlements, rural villages, and urban neighborhoods MESHD, under the current school closure mandate. Results From national surveillance data, we estimated a net reproduction number TRANS of 1.62 (95%CI 1.55-1.70). We found that, at the end of an epidemic mitigated by school closure alone, 10-15% of the overall population would have been symptomatic and 0.3-0.4% of the population would require mechanical ventilation and/or possibly result in a fatal outcome. Higher infection attack rates TRANS are expected in more urbanized areas, but the highest incidence of critical disease MESHD is expected in remote subsistence farming settlements. Conclusions The relatively low burden of COVID-19 MESHD in Ethiopia can be explained by the estimated mixing patterns, underlying demography and the enacted school closures. Socio-demographic factors can also determine marked heterogeneities across different geographical contexts within the same country. Our findings can contribute to understand why sub-Saharan Africa is experiencing a relatively lower attack rate TRANS of severe cases compared to high income countries.

    Role of asymptomatic TRANS COVID-19 MESHD cases in viral transmission TRANS: Findings from a hierarchical community contact network model

    Authors: Tianyi Luo; Zhidong Cao; Yuejiao Wang; Daniel Dajun Zeng; Qingpeng Zhang; Anthony Thomas Maurelli; Eric J. Nelson; Maryam Azimzadeh Irani; Martin Kuper; Orlando Quintero; Kent Feng; Catherine Ley; Dean Winslow; Jennifer Newberry; Karlie Edwards; Colin Hislop; Ingrid Choong; Yvonne Maldonado; Jeffrey Glenn; Ami Bhatt; Catherine Blish; Taia Wang; Chaitan Khosla; Benjamin Pinsky; Manisha Desai; Julie Parsonnet; Upinder Singh

    doi:10.1101/2020.11.21.20236034 Date: 2020-11-23 Source: medRxiv

    Background: As part of on-going efforts to contain the COVID-19 pandemic MESHD, understanding the role of asymptomatic TRANS patients in the transmission TRANS system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic TRANS cases remains unclear. Methods: This study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 MESHD in China, accounting for the heterogeneity of social network. We assessed epidemic control measures for asymptomatic TRANS cases on three dimensions. Impact of asymptomatic TRANS cases on epidemic propagation was examined based on the effective reproduction number TRANS, abnormally high transmission TRANS events, and type and structure of transmission TRANS. Results: Management of asymptomatic TRANS cases can help flatten the infection curve. Tracking 75% of asymptomatic TRANS cases corresponds to an overall reduction in new cases by 34.3% (compared to tracking no asymptomatic TRANS cases). Regardless of population-wide measures, family transmission TRANS is higher than other types of transmission TRANS, accounting for an estimated 50% of all cases. Conclusions: Asymptomatic TRANS case tracking has significant effect on epidemic progression. When timely and strong measures are taken for symptomatic cases, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic TRANS cases.

    Statistical techniques to estimate the SARS-CoV-2 infection fatality MESHD rate

    Authors: Mikael Mieskolainen; Robert Bainbridge; Oliver Buchmueller; Louis Lyons; Nicholas Wardle; Valery Vechorko; Alexander Tonevitsky; Fanghua Hao; Huaiyu Tian; Sanam Shah; Tessa Whiteley; Gonzalo Solis-Garcia; Foteini Tsotra; Ivan Zhelyazkov; Hira Imeri; Nicola Low; Michel Jacques Counotte; Claudia Langenberg; Maik Pietzner; Dennis Valentine; Elias Allara; Praveen Surendran; Stephen Burgess; Jing Hua Zhao; James E Peters; Bram P Prins; John Danesh; Poornima Devineni; Yunling Shi; Kristine E Lynch; Scott L DuVall; Helene Garcon; Lauren Thomann; Jin J Zhou; Bryan R Gorman; Jennifer E Huffman; Christopher J O'Donnell; Philip S Tsao; Jean C Beckham; Saiju Pyarajan; Sumitra Muralidhar; Grant D Huang; Rachel Ramoni; Adriana M Hung; Kyong-Mi Chang; Yan V Sun; Jacob Joseph; Andrew R Leach; Todd L Edwards; Kelly Cho; J Michael Gaziano; Adam S Butterworth; Juan P Casas

    doi:10.1101/2020.11.19.20235036 Date: 2020-11-22 Source: medRxiv

    AO_SCPLOWBSTRACTC_SCPLOWThe determination of the infection fatality MESHD rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number TRANS R0 TRANS, are the main epidemic parameters describing severity and transmissibility TRANS of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and "best practice" recommendations. The developed code is available online.

    Forecasting the spread of COVID19 MESHD in Hungary

    Authors: Owais Mujtaba Khanday; Samad Dadvandipour; Mohd Aaqib Lone; Ashley N. Gray; Nicole H. Tobin; Kathie G. Ferbas; Grace M. Aldrovandi; Anne W Rimoin; Fiona Warren; Liam J Peck; Thomas G Ritter; Zoe de Toledo; Laura Warren; David Axten; Richard J Cornall; E Yvonne Jones; David I Stuart; Gavin Screaton; Daniel Ebner; Sarah Hoosdally; Meera Chand; - Oxford University Hospitals Staff Testing Group; Derrick W Crook; Christopher P Conlon; Koen B Pouwels; A Sarah Walker; Tim EA Peto; Susan Hopkins; Tim M Walker; Katie Jeffery; David W Eyre; Talat Mokhtari-Azad; Reza Najafipour; Reza Malekzadeh; Kimia Kahrizi; Seyed Mohammad Jazayeri; Hossein Najmabadi

    doi:10.1101/2020.11.19.20234815 Date: 2020-11-19 Source: medRxiv

    Time series analysis of the COVID19 MESHD/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number TRANS.

    Meta-analysis of the SARS-CoV-2 serial interval TRANS and the impact of parameter uncertainty on the COVID-19 MESHD reproduction number TRANS

    Authors: Robert Challen; Ellen Brooks-Pollock; Krasimira Tsaneva-Atanasova; Leon Danon; John Buresh; Mackenzie Edmondson; Peter A. Merkel; Ebbing Lautenbach; Rui Duan; Yong Chen; Liang Zhong; Angela SM Koh; Seow Yen Tan; Paul A Tambyah; Laurent Renia; Lisa F. P. Ng; David Chien Boon Lye; Christine Cheung; Sam T Douthwaite; Gaia Nebbia; Jonathan D Edgeworth; Ali R Awan; - The COVID-19 Genomics UK (COG-UK) consortium

    doi:10.1101/2020.11.17.20231548 Date: 2020-11-18 Source: medRxiv

    The serial interval TRANS of an infectious disease MESHD, commonly interpreted as the time between onset of symptoms TRANS in sequentially infected individuals within a chain of transmission TRANS, is a key epidemiological quantity involved in estimating the reproduction number TRANS. The serial interval TRANS is closely related to other key quantities, including the incubation period TRANS, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases TRANS, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing TRANS and are subject to considerable uncertainty, which is especially true for early COVID-19 MESHD data. In this paper we estimate these key quantities in the context of COVID-19 MESHD for the UK, including a meta-analysis of early estimates of the serial interval TRANS. We estimate distributions for the serial interval TRANS with a mean 5.6 (95% CrI 5.1-6.2) and SD 4.2 (95% CrI 3.9-4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3-5.41) and SD 1.7 (95% CrI 1.0-2.6) days (fitted gamma distribution), and the incubation period with a mean TRANS 5.5 (95% CrI 5.1-5.8) and SD MESHD 4.9 (95% CrI 4.5-5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval TRANS, generation interval, incubation period TRANS and time delays, on the subsequent estimation of the reproduction number TRANS, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 MESHD transmission TRANS.

    Regular universal screening for SARS-CoV-2 infection MESHD may not allow reopening of society after controlling a pandemic wave

    Authors: Martin CJ Bootsma; Mirjam E Kretzschmar; Ganna Rozhnova; Hans Heesterbeek; JAN J. A. J. W. kluytmans; Marc JM Bonten; Mayon Haresh Patel; Jade Stockham; Aisling O'Neill; Tristan Luke Clark; Tom Wilkinson; Paul Little; Nick A Francis; Gareth Griffiths; Michael Moore

    doi:10.1101/2020.11.18.20233122 Date: 2020-11-18 Source: medRxiv

    BackgroundTo limit societal and economic costs of lockdown measures, public health strategies are needed that control the spread of SARS-CoV-2 and simultaneously allow lifting of disruptive measures. Regular universal random screening of large proportions of the population regardless of symptoms has been proposed as a possible control strategy. MethodsWe developed a mathematical model that includes test sensitivity SERO depending on infectiousness for PCR-based and antigen-based tests, and different levels of onward transmission TRANS for testing and non-testing parts of the population. Only testing individuals participate in high- risk transmission TRANS events, allowing more transmission TRANS in case of unnoticed infection. We calculated the required testing interval and coverage to bring the effective reproduction number TRANS due to universal random testing (Rrt) below 1, for different scenarios of risk behavior of testing and non-testing individuals. FindingsWith R0 TRANS = 2.5, lifting all control measures for tested subjects with negative test results would require 100% of the population being tested every three days with a rapid test SERO method with similar sensitivity SERO as PCR-based tests. With remaining measures in place reflecting Re = 1.3, 80% of the population would need to be tested once a week to bring Rrt below 1. With lower proportions tested and with lower test sensitivity SERO, testing frequency should increase further to bring Rrt below 1. With similar Re values for tested and non-tested subjects, and with tested subjects not allowed to engage in higher risk events, at least 80% of the populations needs to test every five days to bring Rrt below. The impact of the test- sensitivity SERO on the reproduction number TRANS is far less than the frequency of testing. InterpretationRegular universal random screening followed by isolation of infectious individuals is not a viable strategy to reopen society after controlling a pandemic wave of SARS-CoV-2. More targeted screening approaches are needed to better use rapid testing SERO such that it can effectively complement other control measures. FundingRECOVER (H2020-101003589) (MJMB), ZonMw project 10430022010001 (MK, HH), FCT project 131_596787873 (GR). ZonMw project 91216062 (MK)

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MeSH Disease
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Seroprevalence


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