Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (156)

Fever (106)

Cough (106)

Falls (48)

Fatigue (26)


Transmission

Seroprevalence
    displaying 21 - 30 records in total 2502
    records per page




    Analytical Model of COVID-19 for lifting non-pharmaceutical interventions

    Authors: Garry Jacyna; James R. Thompson; Matt Koehler; David M. Slater

    doi:10.1101/2020.07.31.20166025 Date: 2020-08-04 Source: medRxiv

    In the present work, we outline a set of coarse-grain analytical models that can be used by decision-makers to bound the potential impact of the COVID-19 pandemic on specific communities with known or estimated social contact structure and to assess the effects of various non-pharmaceutical interventions on slowing the progression of disease MESHD disease spread TRANS. This work provides a multi-dimensional view of the problem by examining steady-state and dynamic disease MESHD disease spread TRANS spread using a network-based approach. In addition, Bayesian-based estimation procedures are used to provide a realistic assessment of the severity of outbreaks based on estimates of the average and instantaneous basic reproduction number TRANS R0 TRANS.

    Face masks prevent transmission TRANS of respiratory diseases MESHD: a meta-analysis of randomized controlled trials

    Authors: Hanna M Ollila; Markku Partinen; Jukka Koskela; Riikka Savolainen; Anna Rotkirch; Liisa T Laine

    doi:10.1101/2020.07.31.20166116 Date: 2020-08-04 Source: medRxiv

    Background: Coronavirus Disease MESHD 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome MESHD Coronavirus 2 (SARS-CoV-2) and spreads through droplet-mediated transmission TRANS on contaminated surfaces and in air. Mounting scientific evidence from observational studies suggests that face masks for the general public may reduce the spread of infections MESHD. However, results from randomized control trials (RCT) have been presented as inconclusive, and concerns related to the safety and efficacy of non-surgical face masks in non-clinical settings remain. This controversy calls for a meta-analysis which considers non-compliance in RCTs, the time-lag in benefits of universal masking, and possible adverse effects. Methods: We performed a meta-analysis of RCTs of non-surgical face masks in preventing viral respiratory infections MESHD in non-hospital and non-household settings at cumulative and maximum follow-up as primary endpoints. The search for RCTs yielded five studies published before May 29th, 2020. We pooled estimates from the studies and performed random-effects meta-analysis and mixed-effects meta-regression across studies, accounting for covariates in compliance vs. non-compliance in treatment. Results: Face masks decreased infections across MESHD all studies at maximum follow-up (p=0.0318$, RR=0.608 [0.387 - 0.956]), and particularly in studies without non-compliance bias. We found significant between-study heterogeneity in studies with bias (I^2=71.2%, p=0.0077). We also used adjusted meta-regression to account for heterogeneity. The results support a significant protective effect of masking (p=0.0006, beta=0.0214, SE= 0.0062). No severe adverse effects were detected. Interpretation: The meta-analysis of existing randomized control studies found support for the efficacy of face masks among the general public. Our results show that face masks protect populations from infections MESHD and do not pose a significant risk to users. Recommendations and clear communication concerning the benefits of face masks should be provided to limit the number of COVID-19 and other respiratory infections MESHD.

    Land Use Change and Coronavirus Emergence Risk

    Authors: Maria Cristina Rulli; Paolo D'Odorico; Nikolas Galli; David Hayman

    doi:10.1101/2020.07.31.20166090 Date: 2020-08-04 Source: medRxiv

    Coronavirus disease MESHD 2019 (COVID-19) and severe acute respiratory syndrome MESHD (SARS) causing coronaviruses are mostly discovered in Asian horseshoe bats. It is still unclear how ongoing land use changes may facilitate SARS-related coronavirus transmission TRANS to humans. Here we use a multivariate hotspot analysis of high-resolution land-use data to show that regions of China populated by horseshoe bats are hotspots of forest fragmentation, livestock and human density. We also identify areas susceptible to new hotspot emergence in response to moderate expansion of urbanization, livestock production, or forest disturbance, thereby highlighting regions vulnerable to SARS-CoV spillover under future land-use change. In China population growth and increasing meat consumption associated with urbanization and economic development have expanded the footprint of agriculture, leading to human encroachment in wildlife habitat and increased livestock density in areas adjacent to fragmented forests. The reduced distance between horseshoe-bats and humans elevates the risk for SARS-related coronavirus transmission TRANS to humans.

    Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients

    Authors: John A Lednicky; Michael Lauzardo; Z. Hugh Fan; Antarpreet S Jutla; Trevor B Tilly; Mayank Gangwar; Moiz Usmani; Sripriya N Shankar; Karim Mohamed; Arantza Eiguren-Fernandez; Caroline J Stephenson; Md. Mahbubul Alam; Maha A Elbadry; Julia C Loeb; Kuttichantran Subramaniam; Thomas B Waltzek; Kartikeya Cherabuddi; John Glenn Morris Jr.; Chang-Yu Wu

    doi:10.1101/2020.08.03.20167395 Date: 2020-08-04 Source: medRxiv

    Background - There currently is substantial controversy about the role played by SARS-CoV-2 in aerosols in disease MESHD transmission TRANS, due in part to detections of viral RNA but failures to isolate viable virus from clinically generated aerosols. Methods - Air samples were collected in the room of two COVID-19 patients, one of whom had an active respiratory infection MESHD with a nasopharyngeal (NP) swab positive for SARS-CoV-2 by RT-qPCR. By using VIVAS air samplers that operate on a gentle water-vapor condensation principle, material was collected from room air and subjected to RT-qPCR and virus culture. The genomes of the SARS-CoV-2 collected from the air and of virus isolated in cell culture from air sampling and from a NP swab from a newly admitted patient in the room were sequenced. Findings - Viable virus was isolated from air samples collected 2 to 4.8m away from the patients. The genome sequence of the SARS-CoV-2 strain isolated from the material collected by the air samplers was identical to that isolated from the NP swab from the patient with an active infection MESHD. Estimates of viable viral concentrations ranged from 6 to 74 TCID50 units/L of air. Interpretation - Patients with respiratory manifestations of COVID-19 produce aerosols in the absence of aerosol-generating procedures that contain viable SARS-CoV-2, and these aerosols may serve as a source of transmission TRANS of the virus.

    Implication of backward contact tracing TRANS in the presence of overdispersed transmission TRANS in COVID-19 outbreak

    Authors: Akira Endo; - Centre for the Mathematical Modelling of Infectious Diseases (CMMID) COVID-19 Working Group; Quentin J Leclerc; Gwenan M Knight; Graham F Medley; Katherine E Atkins; Sebastian Funk; Adam J Kucharski

    doi:10.1101/2020.08.01.20166595 Date: 2020-08-04 Source: medRxiv

    Unlike forward contact tracing TRANS, backward contact tracing TRANS identifies the source of newly detected cases. This approach is particularly valuable when there is high individual-level variation in the number of secondary transmissions TRANS. By using a simple branching process model, we explored the potential of combining backward contact tracing TRANS with more conventional forward contact tracing TRANS for control of COVID-19.

    A Study on Survival Scenario of COVID-19 patients in India: An Application of Survival Analysis on patient demographics

    Authors: Sampurna Kundu; Kirti; Debarghya Mandal

    doi:10.1101/2020.08.01.20162115 Date: 2020-08-04 Source: medRxiv

    The study of transmission TRANS dynamics of COVID-19, have depicted the rate, patterns and predictions of the pandemic cases. In order to combat the disease MESHD transmission TRANS in India, the Government had declared lockdown on the 25th of March. Even after a strict lockdown nationwide, the cases are increasing and have crossed 4.5 lakh positive cases. A positive point to be noted amongst all that the recovered cases are slowly exceeding the active cases. The survival of the patients, taking death MESHD as the event that varies over age groups TRANS and gender TRANS wise is noteworthy. This study aims in carrying out a survival analysis to establish the variability in survivorship among age groups TRANS and sex, at different levels, that is, national, state and district level. The open database of COVID-19 tracker (covid19india.org) of India has been utilized to fulfill the objectives of the study. The study period has been taken from the beginning of the first case which was on 30th Jan 2020 till 30th June. Due to the amount of under-reporting of data and dropping missing columns a total of 26,815 sample patients were considered. The entry point of each patient is different and event of interest is death MESHD in the study. Kaplan Meier survival estimation, Cox proportional hazard model and multilevel survival model has been used to perform survival analysis. Kaplan Meier survival function, shows that the probability of survival has been declining during the study period of five months. A significant variability has been observed in the age groups TRANS, as evident from all the survival estimates, with increasing age TRANS the risk of dying from COVID-19 increases. When Western and Central India show ever decreasing survival rate in the framed time period then Eastern , North Eastern and Southern India shows a slightly better picture in terms of survival. Maharashtra, Gujarat, Delhi, Rajasthan and West bengal showed alrmingly poor survival as well. This study has depicted a grave scenario of gradation of ever decreasing survival rates in various regions and shows the variability by age TRANS and gender TRANS.

    Containing the Spread of Infectious Disease MESHD on College Campuses

    Authors: Mirai Shah; Gabrielle Ferra; Susan Fitzgerald; Paul Barreira; Pardis Sabeti; Andres Colubri

    doi:10.1101/2020.07.31.20166348 Date: 2020-08-04 Source: medRxiv

    College campuses in the United States are highly vulnerable to infectious diseases MESHD outbreaks, and there is a mounting need to develop strategies that best mitigate their size and duration, particularly as colleges consider reopening their campuses in the midst of the COVID-19 pandemic. Towards addressing this need, we applied a stochastic transmission TRANS model to quantify the impact of university-level responses to past outbreaks on their campuses and used it to determine which control interventions are most effective. The model aims to simultaneously overcome three crucial issues: stochastic variation in small populations, missing or unobserved case data, and changes in disease MESHD transmission TRANS rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps MESHD outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that universities should design more aggressive diagnostic procedures and stricter isolation policies to decrease infectious disease MESHD incidence on campus. Our model can be applied to data from other outbreaks in college campuses and similar small-population settings.

    Fitting models to the COVID-19 outbreak and estimating R

    Authors: Matt J Keeling; Louise Dyson; Glen Guyver-Fletcher; Alex Holmes; Malcolm G Semple; - ISARIC4C Investigators; Michael J Tildesley; Edward M Hill

    doi:10.1101/2020.08.04.20163782 Date: 2020-08-04 Source: medRxiv

    The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease MESHD. Fitting mathematical models of infectious disease MESHD transmission TRANS to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the basic reproductive ratio, $R$, has taken on special significance in terms of the general understanding of whether the epidemic is under control ($R<1$). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease MESHD outbreaks. Here, we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing SERO. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

    Risk stratification as a tool to rationalize quarantine among health care workers exposed to COVID-19 cases - Evidence from a tertiary healthcare centre in India

    Authors: Ravneet Kaur; Shashi Kant; Mohan Bairwa; Arvind Kumar; Shivram Dhakad; Vignesh Dwarakanathan; Aftab Ahmad; Pooja Pandey; Arti Kapil; Rakesh Lodha; Naveet Wig

    doi:10.1101/2020.07.31.20166264 Date: 2020-08-04 Source: medRxiv

    Background: Quarantine of healthcare workers (HCWs) exposed to COVID 19 confirmed cases TRANS is a well known strategy for limiting the transmission TRANS of infection MESHD. However, there is a need for evidence-based guidelines for the quarantine of HCWs in COVID 19. Methods: We describe our experience of contact tracing TRANS and risk stratification of 3853 HCWs who were exposed to confirmed COVID-19 cases in a tertiary health care institution in India. We developed an algorithm, on the basis of risk stratification, to rationalize quarantine among HCWs. Risk stratification was based on the duration of exposure, distance from the patient, and appropriateness of personal protection equipment (PPE) usage. Only high-risk contacts were quarantined for 14 days. They underwent testing for COVID 19 after five days of exposure, while low risk contacts continued their work with adherence to physical distancing, hand hygiene, and appropriate use of PPE. The low-risk contacts were encouraged to monitor for symptoms and report for COVID 19 screening if fever MESHD fever HP, cough MESHD cough HP, or shortness of breath occurred. We followed up all contacts for 14 days from the last exposure and observed for symptoms of COVID 19 and test positivity. Results and interpretation: Out of total 3853 contacts, 560 (14.5%) were categorized as high-risk contacts, and 40 of them were detected positive for COVID 19, with a test positivity rate of 7.1% (95% CI = 5.2, 9.6). Overall, 118 (3.1%) of all contacts tested positive. Our strategy prevented 3215 HCWs from being quarantined and saved 45,010 person-days of health workforce until June 8, 2020, in the institution. We conclude that exposure-based risk stratification and quarantine of HCWs is a viable strategy to prevent unnecessary quarantine, in a healthcare institution.

    Reconciling epidemiological models with misclassified case-counts for SARS-CoV-2 with seroprevalence SERO surveys: A case study in Delhi, India

    Authors: Rupam Bhattacharyya; Ritwik Bhaduri; Ritoban Kundu; Maxwell Salvatore; Bhramar Mukherjee

    doi:10.1101/2020.07.31.20166249 Date: 2020-08-04 Source: medRxiv

    Underreporting of COVID-19 cases and deaths MESHD is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections MESHD asymptomatic TRANS. In addition, diagnostic tests (RT-PCR tests for detecting current infection MESHD) and serological antibody tests SERO for IgG (to assess past infections MESHD) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections MESHD like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths MESHD under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence SERO studies can provide a rough estimate of the total number of infections MESHD and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths MESHD at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence SERO, we compute adjusted estimates of the true number of infections MESHD reported by the survey (after accounting for misclassification of the antibody test SERO results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections MESHD varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths MESHD and gauge the true extent of transmission TRANS of this insidious virus.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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