Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (19)

Falls (1)

Growth delay (1)

Fever (1)

Cough (1)


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    Suppressing COVID-19 transmission TRANS in Hong Kong: an observational study of the first four months

    Authors: Peng Wu; Tim K. Tsang; Jessica Y. Wong; Tiffany W. Y. Ng; Faith Ho; Huizhi Gao; Dillon C. Adam; Doug H. Cheung; Eric H. Y. Lau; Wey Wen Lim; Sheikh Taslim Ali; Dennis K. M. Ip; Joseph T. Wu; Benjamin J. Cowling; Gabriel M. Leung

    doi:10.21203/ Date: 2020-06-08 Source: ResearchSquare

    Background: Hong Kong was one of the first locations outside of mainland China to identify COVID-19 cases in January 2020. We assessed the impact of various public health measures on transmission TRANS.Methods: We analysed data on all COVID-19 cases and public health measures in Hong Kong up to 7 May 2020. We described case-based, travel TRANS-based and community-based measures and examined their potential effects on case identification and transmission TRANS. Changes in transmissibility TRANS measured by the effective reproductive number TRANS Rt were estimated by comparing the Rt between periods when public health measures were and were not in effect. Delays in case confirmation in imported cases and locally infected cases were analysed to indicate the possible impact of expansion of laboratory testing capacity.Findings: Introduction of a 14-day quarantine on persons arriving from affected areas was associated with a 95% reduction in transmissibility TRANS from imported cases. Testing all arriving travelers reduced mean delays between arrival and detection of imported cases. Increases in laboratory testing capacity for pneumonia MESHD pneumonia HP inpatients and symptomatic outpatients reduced the delay from onset to confirmation. Working from home and physical distancing measures implemented in high-risk facilities were associated with 67% and 58% reductions in transmission TRANS of COVID-19, respectively.Interpretation: Suppression of COVID-19 transmission TRANS in the first pandemic wave in Hong Kong was achieved through integration of travel TRANS-based, case-based and community-based public health measures focusing on early case identification and isolation and physical distancing.

    The effects of the clinical symptoms pneumonia MESHD pneumonia HP-confirmation strategy of the COVID-19 epidemic in Wuhan, China

    Authors: Yanjin Wang; Pei Wang; Shudao Zhang; Hao Pan

    doi:10.21203/ Date: 2020-05-12 Source: ResearchSquare

    Motivated by the quick control in Wuhan, China, and the rapid spread in other countries of COVID-19, we investigate the questions that what is the turning point in Wuhan by quantifying the variety of basic reproductive number TRANS after the lockdown city. The answer may help the world to control the COVID-19 epidemic. A modified SEIR model is used to study the COVID-19 epidemic in Wuhan city. Our model is calibrated by the hospitalized cases. The modeling result gives out that the means of basic reproductive numbers TRANS are 1.5517 (95% CI 1.1716-4.4283) for the period from Jan 25 to Feb 11, 2020, and 0.4738(95% CI 0.0997-0.8370) for the period from Feb 12 to Mar 10. The transmission TRANS rate fell HP after Feb 12, 2020 as a result of China’s COVID-19 strategy of keeping society distance and the medical support from all China, but principally because of the clinical symptoms to be used for the novel coronavirus pneumonia MESHD pneumonia HP (NCP) confirmation in Wuhan since Feb 12, 2020. Clinical diagnosis can quicken up NCP-confirmation such that the COVID-19 patients can be isolated without delay. So the clinical symptoms pneumonia MESHD pneumonia HP-confirmation is the turning point of the COVID-19 battle of Wuhan. The measure of clinical symptoms pneumonia MESHD pneumonia HP-confirmation in Wuhan has delayed the growth HP and reduced size of the COVID-19 epidemic, decreased the peak number of the hospitalized cases by 96% in Wuhan. Our modeling also indicates that the earliest start date of COVID-19 in Wuhan may be Nov 2, 2019.

    COVID-19 mitigation strategies and overview on results from relevant studies in Europe

    Authors: Philipp Heider

    id:2005.05249v1 Date: 2020-05-11 Source: arXiv

    In December 2019, the first patients in Wuhan, China were diagnosed with a primary atypical pneumonia MESHD pneumonia HP, which showed to be unknown and contagious. Since then, known as COVID-19 disease MESHD, the responsible viral pathogen, SARS-CoV-2, has spread around the world in a pandemic. Decisions on how to deal with the crisis are often based on simulations of the pandemic spread of the virus. The results of some of these, as well as their methodology and possibilities for improvement, will be described in more detail in this paper in order to inform beyond the current public health dogma called "flatten-the-curve". There are several ways to model an epidemic in order to simulate the spread of diseases TRANS diseases MESHD. Depending on the timeliness, scope and quality of the associated real data, these multivariable models differ in the value of used parameters, but also in the selection of considered influencing factors. It was exemplarily shown that epidemics in their course are simulated more realistically by models that assume subexponential growth. Furthermore, various simulations of the COVID-19 pandemic were presented in an European perspective, compared against each other and discussed in more detail. It is difficult to estimate how credible the simulations of the pandemic models currently are, so it remains to be seen whether the spread of the pandemic can be effectively reduced by the measures taken. Whether a model works well in reality is largely determined by the quality and scope of its underlying data. Past studies have shown that countermeasures are able to reduce reproduction numbers TRANS or transmission TRANS rates in epidemics. In addition to that, the presented modelling study provides a good framework for the creation of subexponential-growth-models for assessing the spread of COVID-19.

    Epidemic Peak for COVID-19 in India, 2020

    Authors: Chaitanya S. Wagh; Parikshit N. Mahalle; Sanjeev J. Wagh

    id:10.20944/preprints202005.0176.v1 Date: 2020-05-10 Source:

    In India the first case of coronavirus disease MESHD 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member TRANS of coronaviridae where previously Middle East Respiratory Syndrome MESHD MERS and Severe Acute Respiratory Syndrome MESHD SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever MESHD fever HP, cough MESHD cough HP and breath shortness, in severe cases may cause pneumonia MESHD pneumonia HP, SARS or some time death MESHD. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 TRANS to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease MESHD geospatially and analyze the death MESHD. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

    Analysis and prediction of the 2019 novel coronavirus pneumonia MESHD pneumonia HP epidemic in China based on an individual-based model

    Authors: Zuiyuan Guo; Dan Xiao

    doi:10.21203/ Date: 2020-04-28 Source: ResearchSquare

    We developed a stochastic model to simulate the process of the epidemic of novel coronavirus pneumonia MESHD pneumonia HP in China. The study provides valuable reference to understand the transmission TRANS mechanism of the novel coronavirus and evaluate the influence of intervention meaures. We established a stochastic individual-based model, and simulated the whole process of occurrence, development, and control of the epidemic, and the infectors and patients leaving Hubei Province before the traffic was closed. Additionally, the R0 TRANS and the number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R0 TRANS at the initial stage of the epidemic predicted by the model was 4.97 (95% confidence interval [CI], 4.82- 5.17). Before the traffic lockdown in Hubei , an estimated 2000 (95% CI, 1982–2030) infectors and patients left Hubei and traveled TRANS throughout the country. The model estimated that as of March 15, the cumulative number of laboratory-confirmed patients in Hubei and other provinces would reach 42,739 (95% CI, 32734-55472) and 12,870 (95% CI, 11520-14572), respectively. If the government had taken prevention and control measures 1 day later, the cumulative number of laboratory- confirmed patients in the whole country would increase by 32.1%. If the lockdown of Hubei was taken 1 day in advance, it is estimated that the cumulative number of laboratory-confirmed patients in other provinces would decrease by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. Intervention measurements nationwide have effectively curbed human-to-human transmission TRANS of the virus.

    Application of COVID-19 pneumonia MESHD pneumonia HP diffusion data to predict epidemic situation

    Authors: Zhenguo Wu

    doi:10.1101/2020.04.11.20061432 Date: 2020-04-14 Source: medRxiv

    Objective: To evaluate novel coronavirus pneumonia MESHD pneumonia HP cases by establishing the mathematical model of the number of confirmed cases TRANS daily, and to assess the current situation and development of the epidemic situation, so as to provide a digital basis for decision-making. Methods: The number of newly confirmed covid-19 cases per day was taken as the research object, and the seven-day average value (M)) and the sequential value (R TRANS) of M were calculated to study the occurrence and development of covid-19 epidemic through the analysis of charts and data. Results: M reflected the current situation of epidemic development; R reflected the current level of infection MESHD and the trend of epidemic development. Conclusion: The current data can be used to evaluate the number of people who have been infected, and when R < 1, the peak of epidemic can be predicted.

    Confronting COVID-19: Surging critical care capacity in Italy

    Authors: Jose Manuel Rodriguez Llanes; Rafael Castro Delgado; Morten Gram Pedersen; Pedro Arcos Gonzalez; Matteo Meneghini

    doi:10.1101/2020.04.01.20050237 Date: 2020-04-06 Source: medRxiv

    The current spread of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) in Europe threats Italian capacity and that of other national health systems to effectively respond to the needs of patients who require intensive care, mostly due to pneumonia MESHD pneumonia HP and derived complications from concomitant disease MESHD and age TRANS. Predicting the surge in capacity has proved difficult due to the requirement of a subtle combination of diverse expertise and difficult choices to be made on selecting robust measures of critical care utilization, and parsimonious epidemic modelling which account for changing government measures. We modelled the required surge capacity of ICU beds in Italy exclusively for COVID-19 patients at epidemic peak. Because new measures were imposed by the Italian government, suspending nearly all non-essential sectors of the economy, we included the potential impacts of these new measures. The modelling considered those hospitalized and home isolated as quarantined, mimicking conditions on the ground. The percentage of patients in intensive care (out of the daily active confirmed cases TRANS) required for our calculations were chosen based on clinical relevance and robustness, and this number was consistently on average 9.9% from February 24 to March 6, 2020. Five different scenarios were produced (two positive and three negative). Under most positive scenarios, in which R0 TRANS is reduced below 1 (i.e., 0.71), the number of daily active confirmed cases TRANS will peak at nearly 89 000 by the early days of April and the total number of intensive care beds exclusively dedicated to COVID-19 patients required in Italy estimated at 8791. Worst scenarios produce unmanageable numbers. Our results suggest that the decisive moment for Italy has come. Jointly reinforcement by the government of the measures approved so far, including home confinement, but even more important the full commitment of the civil society in respecting home confinement, social distancing and hygiene will be key in the next days. Yet, even under the best circumstances, intensive care capacity will need to get closer to 9000 units in the country to avoid preventable mortality. So far, only strong measures were effective in Italy, as shown by our modelling, and this may offer an opportunity to European countries to accelerate their interventions.

    Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries

    Authors: Edward De Brouwer; Daniele Raimondi; Yves Moreau

    doi:10.1101/2020.04.02.20046375 Date: 2020-04-04 Source: medRxiv

    On March 11, 2020, the World Health Organization declared the COVID-19 outbreak, originally started in China, a global pandemic. Since then, the outbreak has indeed spread across all continents, threatening the public health of numerous countries. Although the Case Fatality Rate (CFR) of COVID-19 is relatively low when optimal level of healthcare is granted to the patients, the high percentage of severe cases developing severe pneumonia MESHD pneumonia HP and thus requiring respiratory support is worryingly high, and could lead to a rapid saturation of Intensive Care Units (ICUs). To overcome this risk, most countries enacted COVID-19 containment measures. In this study, we use a Bayesian SEIR epidemiological model to perform a parametric regression over the COVID-19 outbreaks data in China, Italy, Belgium, and Spain, and estimate the effect of the containment measures on the basic reproduction ratio R_0 TRANS. We find that the effect of these measures is detectable, but tends to be gradual, and that a progressive strengthening of these measures usually reduces the R_0 TRANS below 1, granting a decay of the outbreak. We also discuss the biases and inconsistencies present in the publicly available data on COVID-19 cases, providing an estimate for the actual number of cases in Italy on March 12, 2020. Lastly, despite the data and model's limitations, we argue that the idea of "flattening the curve" to reach herd immunity is likely to be unfeasible.

    Changing transmission TRANS dynamics of COVID-19 in China: a nationwide population-based piecewise mathematical modelling study

    Authors: Jiawen Hou; Jie Hong; Boyun Ji; Bowen Dong; Yue Chen; Michael P Ward; Wei Tu; Zhen Jin; Jian Hu; Qing Su; Wenge Wang; Zheng Zhao; Shuang Xiao; Jiaqi Huang; Wei Lin; Zhijie Zhang

    doi:10.1101/2020.03.27.20045757 Date: 2020-03-30 Source: medRxiv

    Background: The first case of COVID-19 atypical pneumonia MESHD pneumonia HP was reported in Wuhan, China on December 1, 2019. Since then, at least 33 other countries have been affected and there is a possibility of a global outbreak. A tremendous amount of effort has been made to understand its transmission TRANS dynamics; however, the temporal and spatial transmission TRANS heterogeneity and changing epidemiology have been mostly ignored. The epidemic mechanism of COVID-19 remains largely unclear. Methods: Epidemiological data on COVID-19 in China and daily population movement data from Wuhan to other cities were obtained and analyzed. To describe the transmission TRANS dynamics of COVID-19 at different spatio-temporal scales, we used a three-stage continuous-time Susceptible-Exposed-Infectious-Recovered (SEIR) meta-population model based on the characteristics and transmission TRANS dynamics of each stage: 1) local epidemic from December 1, 2019 to January 9, 2020; 2) long-distance spread due to the Spring Festival travel TRANS rush from January 10 to 22, 2020; and 3) intra-provincial transmission TRANS from January 23, 2020 when travel TRANS restrictions were imposed. Together with the basic reproduction number TRANS ( R_0 TRANS) for mathematical modelling, we also considered the variation in infectivity and introduced the controlled reproduction number TRANS (R_c) by assuming that exposed individuals to be infectious; we then simulated the future spread of COVID across Wuhan and all the provinces in mainland China. In addition, we built a novel source tracing TRANS algorithm to infer the initial exposed number of individuals in Wuhan on January 10, 2020, to estimate the number of infections MESHD early during this epidemic. Findings: The spatial patterns of disease MESHD disease spread TRANS spread were heterogeneous. The estimated controlled reproduction number TRANS (R_c) in the neighboring provinces of Hubei province were relatively large, and the nationwide reproduction number TRANS (except for Hubei) ranged from 0.98 to 2.74 with an average of 1.79 (95% CI 1.77-1.80). Infectivity was significantly greater for exposed than infectious individuals, and exposed individuals were predicted to have become the major source of infection MESHD after January 23. For the epidemic process, most provinces reached their epidemic peak before February 10, 2020. It is expected that the maximum number of infections MESHD will be approached by the end of March. The final infectious size is estimated to be about 58,000 for Wuhan, 20,800 for the rest of Hubei province, and 17,000 for the other provinces in mainland China. Moreover, the estimated number of the exposed individuals is much greater than the officially reported number of infectious individuals in Wuhan on January 10, 2020. Interpretation: The transmission TRANS dynamics of COVID-19 have been changing over time and were heterogeneous across regions. There was a substantial underestimation of the number of exposed individuals in Wuhan early in the epidemic, and the Spring Festival travel TRANS rush played an important role in enhancing and accelerating the spread of COVID-19. However, China's unprecedented large-scale travel TRANS restrictions quickly reduced R_c. The next challenge for the control of COVID-19 will be the second great population movement brought by removing these travel TRANS restrictions.

    A deterministic epidemic model for the emergence of COVID-19 in China

    Authors: Meng Wang; Jingtao Qi

    doi:10.1101/2020.03.08.20032854 Date: 2020-03-10 Source: medRxiv

    Coronavirus disease MESHD (COVID-19) broke out in Wuhan, Hubei province,China, in December 2019 and soon after Chinese health authorities tookunprecedented prevention and control measures to curb the spreading ofthe novel coronavirus-related pneumonia MESHD pneumonia HP. We develop a mathematicalmodel based on daily updates of reported cases to study the evolutionof the epidemic. With the model, on 95% confidence level, we estimatethe basic reproduction number TRANS, R0 TRANS = 2.82 {+/-} 0.11, time between March19 and March 21 when the effective reproduction number TRANS becoming lessthan one, the epidemic ending after April 2 and the total number ofconfirmed cases approaching 14408 {+/-} 429 on the Chinese mainlandexcluding Hubei province.

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MeSH Disease
Human Phenotype

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