Corpus overview


Overview

MeSH Disease

Human Phenotype

Fever (46)

Cough (42)

Pneumonia (35)

Fatigue (18)

Hypertension (14)


Transmission

Seroprevalence
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    A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP in China

    Authors: Dawei Yang; Tao Xu; Xun Wang; Deng Chen; Ziqiang Zhang; Lichuan Zhang; Jie Liu; Kui Xiao; Li Bai; Yong Zhang; Lin Zhao; Lin Tong; Chaomin Wu; Yaoli Wang; Chunling Dong; Maosong Ye; Yu Xu; Zhenju Song; Hong Chen; Jing Li; Jiwei Wang; Fei Tan; Hai Yu; Jian Zhou; Jinming Yu; Chunhua Du; Hongqing Zhao; Yu Shang; Linian Huang; Jianping Zhao; Yang Jin; Charles A. Powell; Yuanlin Song; Chunxue Bai

    doi:10.1101/2020.08.07.20163402 Date: 2020-08-11 Source: medRxiv

    Background The outbreak of coronavirus disease MESHD 2019 (COVID-19) has become a global pandemic acute infectious disease MESHD, especially with the features of possible asymptomatic TRANS carriers TRANS and high contagiousness. It causes acute respiratory distress HP syndrome MESHD and results in a high mortality rate if pneumonia MESHD pneumonia HP is involved. Currently, it is difficult to quickly identify asymptomatic TRANS cases or COVID-19 patients with pneumonia MESHD pneumonia HP due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease TRANS disease MESHD at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic TRANS COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic TRANS cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough MESHD cough HP', ' Fatigue MESHD Fatigue HP', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood SERO oxygen saturation<=93%', ' Lymphopenia MESHD Lymphopenia HP', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity SERO of the model, we used a cutoff value of 0.09. The sensitivity SERO and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity SERO and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic TRANS patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission TRANS of the disease from asymptomatic MESHD asymptomatic TRANS patients at the community level.

    COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform

    Authors: Zhou Yang; Jiwei Xu; Zhenhe Pan; Fang Jin

    id:2008.04285v1 Date: 2020-08-10 Source: arXiv

    The Coronavirus Disease MESHD 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths MESHD. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases TRANS and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases TRANS per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease MESHD disease spreading TRANS spreading dynamics in China and showing how the epidemic is taken under control in China.

    Association of mental disorders with SARS-CoV-2 infection MESHD infection and severe HP and severe health outcomes: a nationwide cohort study

    Authors: Ha-Lim Jeon; Jun Soo Kwon; So-Hee Park; Ju-Young Shin

    doi:10.1101/2020.08.05.20169201 Date: 2020-08-07 Source: medRxiv

    Background: No epidemiological data exists for the association between mental disorders and the risk of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) infection MESHD and coronavirus disease MESHD 2019 (COVID-19) severity. Aims: To evaluate the association between mental disorders and the risk of SARS-CoV-2 infection MESHD infection and severe HP and severe outcomes following COVID-19. Methods: We performed a cohort study using the Korean COVID-19 patient database based on the national health insurance data. Each patient with a mental or behavioral disorder (diagnosed during six months prior to the first SARS-CoV-2 test) was matched by age TRANS, sex, and Charlson comorbidity index with up to four patients without mental disorders. SARS-CoV-2 positivity risk and risk of death MESHD or severe events (intensive care unit admission, use of mechanical ventilation, and acute respiratory distress HP syndrome MESHD) post- infection MESHD were calculated using conditional logistic regression analysis. Results: Among 230,565 patients tested for SARS-CoV-2, 33,653 (14.6%) had mental disorders, 928/33,653 (2.76%) tested positive, and 56/928 (6.03%) died. In multivariate analysis with the matched cohort, there was no association between mental disorders and SARS-CoV-2 positivity risk (odds ratio [OR], 1.02; 95% confidence interval [CI], 0.92-1.12); however, a higher risk was associated with schizophrenia HP-related disorders (OR, 1.36; 95% CI, 1.02-1.81). Among confirmed cases TRANS, mortality risk significantly increased in patients with mental disorders (OR, 1.84, 95% CI, 1.07-3.15). Conclusion: Mental disorders are likely contributing factors of mortality following COVID-19. Although the infection MESHD infection risk TRANS infection risk TRANS risk did not increase in overall mental disorders, patients with schizophrenia HP-related disorders were more vulnerable to the infection MESHD.

    Spatial Distribution and Trend Analysis of Current Status of COVID-19 in Nepal and Global Future Preventive Perspectives

    Authors: Ramesh Raj Pant; Buddha Bahadur Bahadur; Kiran Bishwakarma; Sudip Paudel; Nashib Pandey; Samir Kumar Adhikari; Kamal Ranabhat

    doi:10.21203/rs.3.rs-54139/v1 Date: 2020-08-05 Source: ResearchSquare

    Background: Coronavirus disease MESHD (COVID-19) is a recently discovered severe and contagious disease MESHD caused by severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) and has received worldwide attention. The risk of COVID-19 is serious for the infected persons of chronic diseases MESHD as well as vulnerable populations including elder group. Still, the present scenario of scarcity of effective treatment options and limited recovery rate of ongoing treatment are prevailed in Nepal. This study aims to analyze the spatial distribution and trends of COVID-19 with the help of geographic information system (GIS) software and outlook future preventive perspectives.Methods: In this research work, we used GIS tool ArcGIS 10.4.1 to map the distribution patterns of population of COVID-19 cases. Federal, provincial and district level daily cases data of COVID-19 confirmed, cured and death MESHD from 23rd January to 13th July 2020 were obtained from the Ministry of Health and Population (MoHP), Government of Nepal. In addition, we reviewed several peer-reviewed research papers to summarize the global scenarios and preventive perspectives on COVID-19.Results: In context to Nepal SARS-CoV-2 has spread throughout the country infecting 16,945 persons in all 77 districts, as of 13th July, 2020. Confirmed, cured and death MESHD cases experienced an upward trend up to 1st July, 2020 followed by downward trend as of 13th July, 2020. Over 70% of total confirmed cases TRANS of reported COVID-19 patients were from the lowland-plain area. Spatial clustering suggested that provinces 2 and 5 were at potentially increased risk of COVID-19, and Province 1 and Bagmati province could be grouped as future "hot spots". In addition, we proposed four strategies namely, identification of the key medical and social elements, discovery and development of treatment options for a future pandemic, investing in ethno-medicine research and epidemic preparedness of health care system to decrease the efficacy of calamities of future pandemics.Conclusion: Our study demonstrates one of the best ways to protect; control and sluggish transmission TRANS of SARS-CoV-2 could be achieved by monitoring active ties using GIS spatial analysis. And, the severity of future pandemic could be minimized by integrative action on the abovementioned four different preventive master plans.

    Epidemiological characteristics of SARS-COV-2 in Myanmar

    Authors: Aung Min Thway; Htun Tayza; Tun Tun Win; Ye Minn Tun; Moe Myint Aung; Yan Naung Win; Kyaw M Tun

    doi:10.1101/2020.08.02.20166504 Date: 2020-08-04 Source: medRxiv

    Coronavirus disease MESHD (COVID-19) is an infectious disease MESHD caused by a newly discovered severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2). In Myanmar, first COVID-19 reported cases were identified on 23rd March 2020. There were 336 reported confirmed cases TRANS, 261 recovered and 6 deaths MESHD through 13th July 2020. The study was a retrospective case series and all COVID-19 confirmed cases TRANS from 23rd March to 13th July 2020 were included. The data series of COVID-19 cases were extracted from the daily official reports of the Ministry of Health and Sports (MOHS), Myanmar and Centers for Disease MESHD Control and Prevention (CDC), Myanmar. Among 336 confirmed cases TRANS, there were 169 cases with reported transmission TRANS events. The median serial interval TRANS was 4 days (IQR 3, 2-5) with the range of 0 - 26 days. The mean of the reproduction number TRANS was 1.44 with (95% CI = 1.30-1.60) by exponential growth method and 1.32 with (95% CI = 0.98-1.73) confident interval by maximum likelihood method. This study outlined the epidemiological characteristics and epidemic parameters of COVID-19 in Myanmar. The estimation parameters in this study can be comparable with other studies and variability of these parameters can be considered when implementing disease MESHD control strategy in Myanmar.

    Analysis of COVID-19 and comorbidity co- infection MESHD Model with Optimal Control

    Authors: Dr. Andrew Omame; Nometa Ikenna

    doi:10.1101/2020.08.04.20168013 Date: 2020-08-04 Source: medRxiv

    The new coronavirus disease MESHD 2019 (COVID-19) infection MESHD is a double challenge for people infected with comorbidities such as cardiovascular and cerebrovascular diseases MESHD and diabetes. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection MESHD in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re- infection MESHD. The model is simulated using data relevant to the dynamics of the diseases MESHD in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection MESHD by comorbid susceptibles as well as the rate of re- infection MESHD by those who have recovered from a previous COVID-19 infection MESHD. Sensitivity SERO analysis of the model when the population of individuals co-infected with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co- infection MESHD model are the effective contact rate for COVID-19 transmission TRANS, $\beta\sst{cv}$, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, $\chi\sst{cm}$, the comorbidity development rate, $\theta\sst{cm}$, the detection rate for singly infected and co-infected individuals, $\eta_1$ and $\eta_2$, as well as the recovery rate from COVID-19 for co-infected individuals, $\varphi\sst{i2}$. Simulations of the model reveal that the cumulative confirmed cases TRANS (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases TRANS (including those co-infected with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re- infection MESHD rates for COVID-19 is 0.1 per day. It may be worse than this if the re- infection MESHD rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection MESHD by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected individuals to 0.7 per day, then the reproduction number TRANS can be brought very low below one, and COVID-19 infection MESHD eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection MESHD by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

    Characteristics of COVID-19 fatality cases in East Kalimantan, Indonesia

    Authors: Swandari Paramita; Ronny Isnuwardana; Krispinus Duma; Rahmat Bakhtiar; Muhammad Khairul Nuryanto; Riries Choiru Pramulia Yudia; Evi Fitriany; Meiliati Aminyoto

    doi:10.1101/2020.08.01.20166470 Date: 2020-08-04 Source: medRxiv

    Introduction. Coronavirus Disease MESHD (COVID-19) is caused by SARS-CoV-2 infection MESHD. On March 2, 2020, Indonesia announced the first confirmed cases TRANS of COVID-19 infection MESHD. East Kalimantan will play an important role as the new capital of Indonesia. There is attention to the preparedness of East Kalimantan to respond to COVID-19. We report the characteristics of COVID-19 fatality cases in here. Methods. We retrospectively analyzed the fatality cases of COVID-19 patients from the East Kalimantan Health Office information system. All patients were confirmed COVID-19 by RT-PCR examination. Results. By July 31, 2020, 31 fatality cases of patients had been identified as having confirmed COVID-19 in East Kalimantan. The mean age TRANS of the patients was 55.1 + 9.2 years. Most of the patients were men (22 [71.0%]) with age TRANS more than 60 years old (14 [45.2%]). Balikpapan has the highest number of COVID-19 fatality cases from all regencies. Hypertension MESHD Hypertension HP was the most comorbidities in the fatality cases of COVID-19 patients in East Kalimantan. Discussion. Older age TRANS and comorbidities still contributed to the fatality cases of COVID-19 patients in East Kalimantan, Indonesia. Hypertension MESHD Hypertension HP, diabetes, cardiovascular disease MESHD, and cerebrovascular disease MESHD were underlying conditions for increasing the risk of COVID-19 getting into a serious condition. Conclusion. Active surveillance for people older than 60 years old and having underlying diseases MESHD is needed for reducing the case fatality rate of COVID-19 in East Kalimantan. Keywords. Comorbidity, fatality cases, COVID-19, Indonesia.

    90 Days of COVID-19 Social Distancing and Its Impacts on Air Quality and Health in Sao Paulo, Brazil

    Authors: Daniela Debone; Mariana da Costa; Simone Miraglia

    id:10.20944/preprints202008.0022.v1 Date: 2020-08-02 Source: Preprints.org

    The coronavirus disease MESHD (COVID-19) pandemic caused by spreading rapidly a severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) has imposed a unique situation for the humanity. Sao Paulo has reported 124,105 confirmed cases TRANS of COVID-19 and 5,623 deaths MESHD up to June 14th, being considered the epicenter of the pandemic in Brazil and in South America. Due to the measures for social distancing, there was a drop in the air pollution concentration in Sao Paulo. Starting on March 16th, 2020, we broke 90 days of social distancing into 13 weeks and compared to an equivalent period in 2019. We investigated the air quality improvement during the quarantine period and compared the associated avoided deaths MESHD to COVID-19 burden deaths MESHD. Nitrogen dioxide (NO2) was the best indicator of air quality in the analyzed weeks, since its reduction reached 58 %. Our study showed that the 5,623 deaths MESHD occurred during the analyzed weeks of quarantine represents an economic health loss of US$ 10.5 billion. In opposite, we observed a significant air quality improvement due to pollutants concentrations’ reductions during the analyzed weeks. Considering PM10, PM2.5 and NO2, the decrease of concentration levels respectively avoided 78, 337 and 387 premature deaths MESHD and prevented up to US$ 1.5 billion on health costs. These results highlight the importance of continuing to enforce existing air pollution regulations and measures to protect human health both during and after COVID-19 pandemic.

    Correlation of COVID-19 Fatality Risk with Health Care Access and Quality in 87 Countries

    Authors: Shih-Yung Su; Chi-Tai Fang; Wen-Chung Lee

    doi:10.21203/rs.3.rs-51551/v1 Date: 2020-07-31 Source: ResearchSquare

    Background. The coronavirus disease MESHD 2019 (COVID-19) pandemic has been a severe threat to global health.Method. We calculated Spearman rank correlation coefficients between a country’s case fatality risk at various stages of the pandemic and the Healthcare Access and Quality (HAQ) index in 87 countries.Results. COVID-19 case fatality risks and HAQ indexes were negatively correlated at <1000 confirmed cases TRANS. This negative correlation decreased and even turned positive (but nonsignificantly) as countries approached >5000 confirmed cases TRANS. As for per-capita cases, the aforementioned correlation was significantly negative at <100 cases per million people. This negative correlation decreased (r = −0.0429) and became nonsignificant as countries approached >500 cases per million people.Conclusion. Negative correlations between COVID-19 case fatality risks and HAQ indexes weakened and then disappeared with the progressive burden of COVID-19 infection MESHD over time. In the end, even the best public health systems were overwhelmed by the COVID-19 pandemic.

    Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan

    Authors: Motoaki Utamura; Makoto Koizumi; Seiichi Kirikami

    doi:10.1101/2020.07.31.20165829 Date: 2020-07-31 Source: medRxiv

    Background: Coronavirus Disease MESHD 2019 (COVID19) currently poses a global public health threat. Although no exception, Tokyo, Japan was affected at first by only a small epidemic. Medical collapse nevertheless nearly happened because no predictive method existed for counting patients. A standard SIR epidemiological model and its derivatives predict susceptible, infectious, and removed (recovered/ deaths MESHD) cases but ignore isolation of confirmed cases TRANS. Predicting COVID19 trends with hospitalized and infectious people in field separately is important to prepare beds and develop quarantine strategies. Methods: Time-series COVID19 data from February 28 to May 23, 2020 in Tokyo were adopted for this study. A novel epidemiological model based on delay differential equation was proposed. The model can evaluate patients in hospitals and infectious cases in the field. Various data such as daily new cases, cumulative infections MESHD, patients in hospital, and PCR test positivity ratios were used to examine the model. This approach derived an alternative formulation equivalent to the standard SIR model. Its results were compared quantitatively with those of the present isolation model. Results: The basic reproductive number TRANS, inferred as 2.30, is a dimensionless parameter composed of modeling parameters. Effects of intervention to mitigate the epidemic spread were assessed a posteriori. An exit policy of how and when to release a statement of emergency MESHD was also assessed using the model. Furthermore, results suggest that the rapid isolation of infectious cases has a large potential to effectively mitigate the spread of infection MESHD and restores social and economic activities safely. Conclusions: A novel mathematical model was proposed and examined using COVID19 data for Tokyo. Results show that shortening the period from infection MESHD to hospitalization is effective against outbreak without rigorous public health intervention and control. Faster and precise case cluster detection and wider and quicker introduction of testing measures are strongly recommended.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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