Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (324)

Fever (252)

Cough (203)

Hypertension (112)

Severe infection (97)


Transmission (942)

age categories (928)

gender (425)

fomite (421)

asymptotic cases (399)

    displaying 1 - 10 records in total 3638
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    Population-based prevalence SERO surveys during the COVID-19 pandemic: a systematic review

    Authors: Vinicius Bonetti Franceschi Jr.; Andressa Schneiders Santos Jr.; Andressa Barreto Glaeser Jr.; Janini Cristina Paiz Jr.; Gabriel Dickin Caldana Jr.; Carem Luana Machado Lessa Jr.; Amanda de Menezes Mayer Jr.; Julia Goncalves Kuchle Jr.; Paulo Ricardo Gazzola Zen Sr.; Alvaro Vigo Sr.; Ana Trindade Winck Sr.; Liane Nanci Rotta Sr.; Claudia Elizabeth Thompson Sr.; Andres F. Henao-Martinez; Leland Shapiro

    doi:10.1101/2020.10.20.20216259 Date: 2020-10-22 Source: medRxiv

    Population-based prevalence SERO surveys of COVID-19 contribute to establish the burden and epidemiology of infection MESHD, the role of asymptomatic TRANS and mild infections MESHD in transmission TRANS, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, their reliability, and biases. The available data described 37 surveys from 19 countries, mostly from Europe and America and using antibody testing SERO. They reached highly heterogeneous sample sizes and prevalence SERO estimates. Disproportional prevalence SERO was observed in minority communities. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way, and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, blood SERO samples and prevalence SERO >1%. Low risk of bias was related to Asian studies, RT-PCR tests and prevalence SERO <1%.

    Diagnostic utility of a Ferritin-to-Procalcitonin Ratio to differentiate patients with COVID-19 from those with Bacterial Pneumonia HP: A multicenter study

    Authors: Amal A. Gharamti; Fei Mei; Katherine C. Jankousky; Jin Huang; Peter Hyson; Daniel B. Chastain; Jiawei Fan; Sharmon Osae; Wayne W. Zhang; Jose G. Montoya; Kristine M. Erlandson; Sias J. Scherger; Carlos Franco-Paredes; Andres F. Henao-Martinez; Leland Shapiro

    doi:10.1101/2020.10.20.20216309 Date: 2020-10-22 Source: medRxiv

    Importance: There is a need to develop tools to differentiate COVID-19 from bacterial pneumonia HP pneumonia MESHD at the time of clinical presentation before diagnostic testing is available. Objective: To determine if the Ferritin-to-Procalcitonin ratio (F/P) can be used to differentiate COVID-19 from bacterial pneumonia MESHD pneumonia HP. Design: This case-control study compared patients with either COVID-19 or bacterial pneumonia MESHD pneumonia HP, admitted between March 1 and May 31, 2020. Patients with COVID-19 and bacterial pneumonia co-infection MESHD pneumonia HP co-infection were excluded. Setting: A multicenter study conducted at three hospitals that included UCHealth and Phoebe Putney Memorial Hospital in the United States, and Yichang Central People Hospital in China. Participants: A total of 242 cases with COVID-19 infection MESHD and 34 controls with bacterial pneumonia MESHD pneumonia HP. Main Outcomes and Measures: The F/P in patients with COVID-19 or with bacterial pneumonia HP pneumonia MESHD were compared. Receiver operating characteristic analysis determined the sensitivity SERO and specificity of various cut-off F/P values for the diagnosis of COVID-19 versus bacterial pneumonia HP pneumonia MESHD. Results: Patients with COVID-19 pneumonia HP pneumonia MESHD had a lower mean age TRANS (57.11 vs 64.4 years, p=0.02) and a higher BMI (30.74 vs 27.15 kg/m2, p=0.02) compared to patients with bacterial pneumonia MESHD pneumonia HP. Cases and controls had a similar proportion of women (47% vs 53%, p=0.5) and COVID-19 patients had a higher prevalence SERO of diabetes mellitus HP diabetes mellitus MESHD (32.6% vs 12%, p=0.01). The median F/P was significantly higher in patients with COVID-19 (4037.5) compared to the F/P in bacterial pneumonia HP pneumonia MESHD (802, p<0.001). An F/P greater than or equal to 877 used to diagnose COVID-19 resulted in a sensitivity SERO of 85% and a specificity of 56%, with a positive predictive value SERO of 93.2%, and a likelihood ratio of 1.92. In multivariable analyses, an F/P greater than or equal to 877 was associated with greater odds of identifying a COVID-19 case (OR: 11.27, CI: 4-31.2, p<0.001). Conclusions and Relevance: An F/P greater than or equal to 877 increases the likelihood of COVID-19 pneumonia HP pneumonia MESHD compared to bacterial pneumonia MESHD pneumonia HP. Further research is needed to determine if obtaining ferritin and procalcitonin simultaneously at the time of clinical presentation has improved diagnostic value. Additional questions include whether an increased F/P and/or serial F/P associates with COVID-19 disease severity or outcomes.

    Review of Infective Dose, Routes of Transmission TRANS, and Outcome of COVID-19 Caused by the SARS-CoV-2 Virus MESHD: Comparison with Other Respiratory Viruses MESHD

    Authors: Sedighe Karimzadeh; Raj Bhopal; Huy Nguyen Tien

    id:202007.0613/v2 Date: 2020-10-22 Source:

    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pandemic. Prevention and control strategies require an improved understanding of SARS-CoV-2 dynamics. We did a rapid review of the literature on SARS-CoV-2 viral dynamics with a focus on infective dose. We sought comparisons of SARS-CoV-2 with other respiratory viruses including SARS-CoV-1 and MERS-CoV. We examined laboratory animal, and human studies. The literature on infective dose, transmission TRANS, and routes of exposure was limited specially in humans, and varying endpoints were used for measurement of infection MESHD. We propose the minimum infective dose of COVID-19 in humans, is higher than 100 particles, possibly slightly lower than the 700 particles estimated for H1N1 influenza. Despite variability in animal studies, there was some evidence that increased dose at exposure correlated with higher viral load clinically, and severer symptoms. Higher viral load measures did not reflect COVID-19 severity. Aerosol transmission TRANS seemed to raise the risk of more severe respiratory complications in animals. An accurate quantitative estimate of the infective dose of SARS-CoV-2 in humans is not currently feasible and needs further research. Further work is also required on the relationship between routes of transmission TRANS, infective dose, co-infection MESHD, and outcomes.

    Prevalence SERO of SARS-CoV-2 IgG antibodies SERO in a population from Veracruz (Southeastern Mexico).

    Authors: Jose Maria Remes-Troche; Antonio Ramos-de-la-Medina; Marisol Manriquez-Reyes; Laura Martinez-Perez Maldonado; Maria Antonieta Solis-Gonzalez; Karina Hernandez-Flores; Hector Vivanco-Cid; Graham Cooke; Timothy B Hallett; Katharina D Hauck; Peter J White; Mark R Thursz; Shevanthi Nayagam; Brendan Flannery; Ricardo Gilead Baibich; Iris Bigler; Matan Malul; Rotem Rishti; Asher Brenner; Yair E. Lewis; Eran Friedler; Yael Gilboa; Sara Sabach; Yuval Alfiya; Uta Cheruti; Nadav Davidovitch; Natalya Bilenko; Jacob Moran-Gilad; Yakir Berchenko; Itay Bar-Or; Ariel Kushmaro; Timothy Spector; Claire J Steves

    doi:10.1101/2020.10.19.20215558 Date: 2020-10-21 Source: medRxiv

    Introduction/Aim: Recent studies have shown that seroprevalence SERO is quite variable depending on the country, the population and the time of the pandemic in which the serological tests SERO are performed. Here, we investigated the prevalence SERO of IgG antibodies SERO against SARS-CoV-2 in a population living in Veracruz City, Mexico. Methods: From of June 1 to July 31, 2020, the consecutive adult TRANS patients that attended 2 ambulatory diagnostic private practice centers for testing were included. Samples were run on the Abbott Architect instrument using the commercial Abbott SARS-CoV-2 IgG assay. The main outcome was seroprevalence SERO. Demographics, previous infection MESHD to SARS-CoV-2 (according to a previous positive polymerase-chain reaction nasopharyngeal swab), self-suspicious of virus of infection MESHD (according to have in the previous 4 weeks either fever HP fever MESHD, headache HP headache MESHD, respiratory symptoms but not a confirmatory PCR) or no having symptoms were also evaluated. Results: A total of 2174 subjects were tested, included 53.6% women (mean age TRANS 41.8, range 18-98 years). One thousand and forty-one (52.5%) subjects were asymptomatic TRANS, 722 (33.2%) had suspicious of infection MESHD and 311 (14.3%) had previous infection MESHD. Overall, 642 of 2174 (29.5% [95% CI 27.59%-31.47%]) of our population were seropositive. Seropositivity among groups was 21.3% in asymptomatic TRANS, 23.4% in self-suspicious patients and 73.9% in previous infection MESHD patients. Conclusions: We found one of the highest seroprevalences SERO reported for SARS-CoV-2 worldwide in asymptomatic TRANS subjects (21.3%) as well in subjects with self-suspicious of COVID-19 (23.4%). The number of infected subjects in our population is not encouraging and it should be interpreted with caution.

    Modelling the dispersion of SARS-CoV-2 on a dynamic network graph

    Authors: Patrick Bryant; Arne Elofsson; Theresa Hippchen; Sylvia Olberg; Monique van Straaten; Hedda Wardemann; Erec Stebbins; Hans-Georg Kraeusslich; Ralf Bartenschlager; Hermann Brenner; Vibor Laketa; Ben Schoettker; Barbara Mueller; Uta Merle; Tim Waterboer; James Watmough; Jude Dzevela Kong; Iain Moyles; Huaiping Zhu

    doi:10.1101/2020.10.19.20215046 Date: 2020-10-21 Source: medRxiv

    Background When modelling the dispersion of an epidemic using R0 TRANS, one only considers the average number of individuals each infected individual will infect MESHD. However, we know from extensive studies of social networks that there is significant variation in the number of connections and thus social contacts each individual has. Individuals with more social contacts are more likely to attract and spread infection MESHD. These individuals are likely the drivers of the epidemic, so-called superspreaders. When many superspreaders are immune, it becomes more difficult for the disease to spread TRANS, as the connectedness of the social network dramatically decreases. If one assumes all individuals being equally connected and thus as likely to spread disease TRANS as in a SIR model, this is not true. Methods To account for the impact of social network structure on epidemic development, we model the dispersion of SARS-CoV-2 on a dynamic preferential attachment graph which changes appearance proportional to observed mobility changes. We sample a serial interval TRANS distribution that determines the probability of dispersion for all infected MESHD nodes each day. We model the dispersion in different age groups TRANS using age TRANS-specific infection MESHD fatality rates. We vary the infection probabilities in different age groups TRANS and analyse the outcome. Results The impact of movement on network dynamics plays a crucial role in the spread of infections. We find that higher movement results in higher spread due to an increased probability of new connections being made within a social network. We show that saturation in the dispersion can be reached much earlier on a preferential attachment graph compared to spread on a random graph, which is more similar to estimations using R0 TRANS. Conclusions We provide a novel method for modelling epidemics by using a dynamic network structure related to observed mobility changes. The social network structure plays a crucial role in epidemic development, something that is often overlooked.

    An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State

    Authors: Amitava Datta; Peter Winkelstein; Surajit Sen; Aurora D'Atri; Lorenzo Viselli; Daniela Tempesta; Michele Ferrara; Marisa Ailin Hong; Maria do Carmo Timenetsky; Carmem aparecida de Freitas Oliveira; Luis Fernando de Macedo Brigido; Satish Lakkakula; Oren Miron; Ehud Rinott; Ricardo Gilead Baibich; Iris Bigler; Matan Malul; Rotem Rishti; Asher Brenner; Yair E. Lewis; Eran Friedler; Yael Gilboa; Sara Sabach; Yuval Alfiya; Uta Cheruti; Nadav Davidovitch; Natalya Bilenko; Jacob Moran-Gilad; Yakir Berchenko; Itay Bar-Or; Ariel Kushmaro; Timothy Spector; Claire J Steves

    doi:10.1101/2020.10.19.20215517 Date: 2020-10-21 Source: medRxiv

    We introduce a simple agent based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent and simulate the spread of a pandemic and subsequently validate it by using publicly available COVID-19 data. Our simulation tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection MESHD, recovering asymptomatically TRANS, or getting hospitalized. The virtual community is divided into family groups with 2-6 individuals in each group. Agents interact with other agents in virtual public places like at grocery stores, on public transportation and in offices. We initially seed the virtual community with a very small number of infected MESHD individuals and then monitor the disease spread TRANS and hospitalization over a period of fifty days, which is a typical time-frame for the initial spread of a pandemic. An uninfected or asymptomatic TRANS agent is randomly selected from a random family group in each simulation step for visiting a random public space. An uninfected agent contracts infection if the public place is occupied by other infected agents. We have calibrated our simulation rounds according to the size of the population of the virtual community for simulating realistic exposure of agents to a contagion. Our simulation results are consistent with the publicly available hospitalization and ICU patient data from different communities of varying sizes in New York state. Our model can predict the trend in epidemic spread and hospitalization from a set of simple parameters and could be potentially useful in exploring strategies to keep a community safe.

    Deep learning segmentation MESHD model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity

    Authors: Haiming Tang; Nanfei Sun; Yi Li

    doi:10.1101/2020.10.19.20215483 Date: 2020-10-21 Source: medRxiv

    The pandemic of Covid-19 has caused tremendous losses to lives and economy in the entire world. Up until October 2020, it has caused more than 38 million infections MESHD and 1.1 million deaths. This has created a severe burden for the health care system worldwide. The machine learning models have been applied to the radiological images of the Covid-19 positive patients for disease prediction and severity assessment. However, a segmentation model for detecting the opacity regions like haziness, ground-glass opacity and lung consolidation from the Covid-19 positive chest X-rays is still lacking. The recently published dataset of a collection of radiological images for a rural population in United States had made development of such a model a possibility due to the high quality of the radiological images and the consistency in clinical measurements. We manually annotated 221 chest X-ray images with lung fields and opacity regions and trained a segmentation model for the opacity region. The model has a good performance SERO in regarding the overlap between predicted and manually labelled opacity regions for both the testing data set and the validation dataset from very different sources. In addition, the percentage of the opacity region over the area of the total lung fields shows a good predictive power for the patient severity. In view of the above, our model is a successful first try in developing a segmentation model for the opacity regions for the Covid-19 positive chest X-rays. However, careful manual examinations of the model predictions by experienced radiologists show mistakenly predicted opacity regions caused probably by the anatomical complexities. Thus, additional work is needed before a robust and accurate model can be developed for the ultimate goal of implementation in the clinical setting. The model, manual segmentation and other supporting materials can be found in

    Basrah experience among 6404 patients with COVID-19

    Authors: Saad S. Hamadi Al-Taher; Abbas K AlKanan; Mohammad N. Fares; Nihad Q. Mohammed; Ali Raheem Al-Jabery; Awatif A. Habeeb; Abbas Ali Mansour; Kerstin Klaser; Michela Antonelli; Liane S Canas; Erika Molteni; Marc Modat; M. Jorge Cardoso; Anna May; Sajaysurya Ganesh; Richard Davies; Long H Nguyen; David Alden Drew; Christina M Astley; Amit D. Joshi; Jordi Merino; Neli Tsereteli; Tove Fall; Maria F Gomez; Emma Duncan; Christina Menni; Frances MK Williams; Paul W Franks; Andrew T Chan; Jonathan Wolf; Sebastien Ourselin; Timothy Spector; Claire J Steves

    doi:10.1101/2020.10.19.20215384 Date: 2020-10-21 Source: medRxiv

    Background: The first case of COVID-19 report in Basrah was in early March 2020. This study aimed to assess some of the characteristics of patients with COVID-19 in Basrah for the period from March ,4th to September ,8th 2020. Methods: Retrospective database analysis of the University of Basrah database. All RT-PCR positive patients during the study period were enrolled. Results: Of 6404 patients included , male TRANS constituted 54.8%. Healthcare workers constituted 11.4% of the infected people. Of health care workers 16.1% were physicians . The mean age TRANS for the whole cohort was 39{+/-}16.7 years; adolescents and children TRANS younger than 20 years constituted 12.4%. The peak age TRANS was 31-40 years, those aged TRANS 61 years or more constituted 9.8% only. The case fatality rate was 3% ( males TRANS 55.2% and females TRANS 44.8%) . No death MESHD was reported in adolescents or children TRANS. The highest death rate was among those age TRANS 61 years or more. Conclusion: The situation of COVID-19 infection MESHD in Basrah is evolving like other countries. Furthers studies are needed to assess associated comorbidities, treatment lines, outcomes and variables associated with mortality.

    City-level SARS-CoV-2 sewage surveillance

    Authors: Karin Yaniv; Marilou Shagan; Esti Kramarsky-Winter; Victoria Indenbaum; Merav Weil; Michal Elul; Oran Erster; Alin Sela Brown; Ella Mendelson; Batya Mannasse; Rachel Shirazi; Satish Lakkakula; Oren Miron; Ehud Rinott; Ricardo Gilead Baibich; Iris Bigler; Matan Malul; Rotem Rishti; Asher Brenner; Yair E. Lewis; Eran Friedler; Yael Gilboa; Sara Sabach; Yuval Alfiya; Uta Cheruti; Nadav Davidovitch; Natalya Bilenko; Jacob Moran-Gilad; Yakir Berchenko; Itay Bar-Or; Ariel Kushmaro; Timothy Spector; Claire J Steves

    doi:10.1101/2020.10.19.20215244 Date: 2020-10-21 Source: medRxiv

    The COVID-19 pandemic created a global crisis impacting not only healthcare systems, but also world economies and society. Recent data have indicated that fecal shedding of SARS-CoV-2 is common, and that viral RNA can be detected in wastewater. This suggests that wastewater monitoring is a potentially efficient tool for both epidemiological surveillance, and early warning for SARS-CoV-2 circulation at the population level. In this study we sampled an urban wastewater infrastructure in the city of Ashkelon, Israel, during the end of the first COVID-19 wave in May 2020 when the number of infections MESHD seemed to be waning. We were able to show varying presence of SARS-CoV-2 RNA in wastewater from several locations in the city during two sampling periods. This was expressed as a new index, Normalized Viral Load (NVL), which can be used in different area scales to define levels of virus activity such as red (high) or green (no), and to follow morbidity in the population at tested area. Our index showed the rise in viral load between the two sampling periods (one week apart) and indicated an increase in morbidity that was evident a month later in the population. Thus, this methodology may provide an early indication for SARS-CoV-2 infection MESHD outbreak in a population before an outbreak is clinically apparent.

    On nonlinear incidence rate of Covid-19

    Authors: Swarna Kamal Paul; Saikat Jana; Parama Bhaumik; Victoria Indenbaum; Merav Weil; Michal Elul; Oran Erster; Alin Sela Brown; Ella Mendelson; Batya Mannasse; Rachel Shirazi; Satish Lakkakula; Oren Miron; Ehud Rinott; Ricardo Gilead Baibich; Iris Bigler; Matan Malul; Rotem Rishti; Asher Brenner; Yair E. Lewis; Eran Friedler; Yael Gilboa; Sara Sabach; Yuval Alfiya; Uta Cheruti; Nadav Davidovitch; Natalya Bilenko; Jacob Moran-Gilad; Yakir Berchenko; Itay Bar-Or; Ariel Kushmaro; Timothy Spector; Claire J Steves

    doi:10.1101/2020.10.19.20215665 Date: 2020-10-21 Source: medRxiv

    Classical Susceptible-Infected-Removed model with constant transmission TRANS rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread. On top of that transmission TRANS rate may vary widely in a large region due to non-stationarity of spatial features which poses difficulty in creating a global model. We modified discrete global Susceptible-Infected-Removed model by using time varying transmission TRANS rate, recovery rate and multiple spatially local models. No specific functional form of transmission TRANS rate has been assumed. We have derived the criteria for disease-free equilibrium within a specific time period. A single Convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission TRANS rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection MESHD cases in each locality in a 10-day prediction period. Local interpretations of the model using perturbation method reveals local influence of different features on transmission TRANS rate which in turn is used to generate a set of generalized global interpretations. A what-if scenario with modified recovery rate illustrates rapid dampening of the spread when forecasted with the trained model. A comparative study with current normal scenario reveals key necessary steps to reach baseline.

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MeSH Disease
Human Phenotype

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