Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (13)

Fever (2)

Cough (2)

Falls (1)

Growth delay (1)



There are no seroprevalence terms in the subcorpus

    displaying 1 - 10 records in total 13
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    Optimal Drug Regimen and Combined Drug Therapy and its Efficacy in the Treatment of COVID-19 : An Within-Host Modeling Study

    Authors: Bishal Chhetri; Vijay M. Bhagat; D. K. K. Vamsi; Ananth V S; Bhanu Prakadh; Swapna Muthuswamy; Pradeep Deshmukh; Carani B Sanjeevi

    id:2009.10049v1 Date: 2020-09-21 Source: arXiv

    The COVID-19 pandemic has resulted in more than 30.35 million infections and 9, 50, 625 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection MESHD induced mortality. The current within-host mathematical modeling studies deals with the optimal drug regimen and the efficacy of combined therapy in treatment of COVID-19. The drugs/interventions considered include Arbidol, Remdesivir, Inteferon (INF) and Lopinavir/Ritonavir. It is concluded that these drug interventions when administered individually or in combination reduce the infected cells and viral load. Four scenarios involving administration of single drug intervention, two drug interventions, three drug interventions and all the four have been discussed. In all these scenarios the optimal drug regimen is proposed based on two methods. In the first method these medical interventions are modeled as control interventions and a corresponding objective function and optimal control problem is formulated. In this setting the optimal drug regimen is proposed. Later using the the comparative effectiveness method the optimal drug regimen is proposed based on basic reproduction number TRANS and viral load. The average infected cell count and viral load decreased the most when all the four interventions were applied together. On the other hand the average susceptible cell count decreased the best when Arbidol alone was administered. The basic reproduction number TRANS and viral count decreased the best when all the four interventions were applied together reinstating the fact obtained earlier in the optimal control setting. These findings may help physicians with decision making in treatment of life-threatening COVID-19 pneumonia HP pneumonia MESHD.

    Transmission TRANS Dynamics of SARS-CoV-2 in a Mid-size City of China

    Authors: Hongjun Zhao; Xiaoxiao Lu; Wenhui Lun; Tiegang Li; Boqi Rao; Dedong Wang; Di Wu; Fuman Qiu; Zhicong Yang; Lu Jiachun

    doi:10.21203/ Date: 2020-08-14 Source: ResearchSquare

    Background: An outbreak of pneumonia HP pneumonia MESHD associated with the severe acute respiratory syndrome coronavirus 2 MESHD (SARS-CoV-2) emerged in Wuhan city and then spread to other cities. It is very urgent to delineate the epidemiological and clinical characteristics of these affected patients. Methods: To investigate the epidemiological characteristics of the COVID-19, we describe a case series of 473 patients with confirmed COVID-19 in Wenzhou of China from January 27 to March 2, 2020.Results: The median age TRANS of all patients was 47.6 years, 48.4% of which were female TRANS. 33.8% of the patients had a history of residence in Wuhan. Fever HP Fever MESHD (71.7%) and cough HP (43.1%) were the most common symptoms. In addition, three kinds of unconventional cases were observed, namely 4.9% asymptomatic TRANS patients, 7.6% confirmed patients who had no link to Wuhan city but contact with individuals from Wuhan without any symptoms at the time of contact, and 12.9% confirmed patients who had an unknown source of transmission TRANS. We estimated that the basic reproductive number TRANS ( R0 TRANS) was 2.75 (95%CI: 2.37-3.23). The effective reproduction number TRANS (Rt) fluctuated within the range of 2.50 to 3.74 from January 11 to January 16 while gradually reached the peak of 3.74 on January 16. Rt gradually decreased after January 16 and decreased to 1.00 on January 30. Rt continually decreased and reached the lowest point (0.03) on February 21, 2020.Conclusion: Our findings presented the possibility of asymptomatic TRANS carriers TRANS affected with SARS-CoV-2, and this phenomenon suggested that chances of uncontrollable transmission TRANS in the larger population might be higher than formerly estimated, and transmission TRANS by these three kinds of unconventional patients in Wenzhou may be an important characteristic of infection MESHD in other mid-sized cities in the world. This study evaluated the epidemic characteristics of Wenzhou after having cases imported from Hubei Province and the effects after adopting a series of strict prevention and control strategy. 

    The effects of the clinical symptoms 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 HP-confirmation is the turning point of the COVID-19 battle of Wuhan. The measure of clinical symptoms pneumonia HP pneumonia MESHD-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 HP pneumonia MESHD, which showed to be unknown and contagious. Since then, known as COVID-19 disease, 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. 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 MERS and Severe Acute Respiratory Syndrome SARS belongs MESHD to same family. The COVID-19 attacks on respiratory system signing fever HP fever MESHD, cough HP cough MESHD and breath shortness MESHD, in severe cases may cause pneumonia HP pneumonia MESHD, 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 geospatially and analyze the death. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

    Application of COVID-19 pneumonia HP pneumonia MESHD 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 coronavirus 2 MESHD (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 HP pneumonia MESHD and derived complications from concomitant disease 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.

    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 HP pneumonia MESHD 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 early during this epidemic. Findings: The spatial patterns of disease spread TRANS 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 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 HP pneumonia MESHD. 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.

    A model simulation study on effects of intervention measures in Wuhan COVID-19 epidemic

    Authors: Guopeng ZHOU; Chunhua CHI

    doi:10.1101/2020.02.14.20023168 Date: 2020-02-18 Source: medRxiv

    Background: In the beginning of January 2020, new unknown virus pneumonia HP pneumonia MESHD cases started to emerge in local hospitals in Wuhan, China. This virus epidemic quickly became a public health emergency of international concern by the WHO. Enormous amount of medical supplies as well as healthcare personals from other provinces were mobilized to support Wuhan. This current work tent to help people understanding how infectious disease MESHD disease spread TRANS and the purpose and consequences of various efforts based on simulation model. Method: a simulation model was created using known parameters. R0 TRANS set to 3 and mean incubation time to be 7.5days. the epidemic was divided to 3 periods. Simulation would run 50 times to mimic different patient0 status. Personal activity index was used to mimic different level of control measures. 141427709 simulated patients were created. Cumulation number of patients at the end of period 1 (day50) is 2868.7 {+/-} 1739.0. Total infected patients could be 913396.5 {+/-} 559099.9 by the end of period 2 (day70) in free transmission TRANS state. And at day90, total patients number is 913396.5 {+/-} 559099.9. Conclusion: COVID-19 is a novel severe respiratory disease MESHD. This will put great burden on the shoulder of healthcare workers as well as on medical hardware and supplements. Current strict control measures help to contain disease from spreading TRANS. An early detecting, reporting and fast reacting system needs to be setup to prevent future unknown infectious disease MESHD.

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MeSH Disease
Human Phenotype

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