Overview

MeSH Disease

Infections (45)

Disease (34)

Death (24)

Fever (10)

Human Phenotype

Fever (10)

Pneumonia (8)

Cough (4)

Falls (2)

Transmission

Seroprevalence
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SARS-CoV-2 antigens expressed in plants detect antibody SERO responses in COVID-19 patients

Authors: Mohau S Makatsa; Marius B Tincho; Jerome M Wendoh; Sherazaan D Ismail; Rofhiwa Nesamari; Francisco Pera; Scott de Beer; Anura David; Sarika Jugwanth; Maemu P Gededzha; Nakampe Mampeule; Ian Sanne; Wendy Stevens; Lesley Scott; Jonathan Blackburn; Elizabeth S Mayne; Roanne S Keeton; Wendy A Burgers

doi:10.1101/2020.08.04.20167940 Date: 2020-08-04 Source: medRxiv

Background: The SARS-CoV-2 pandemic has swept the world and poses a significant global threat to lives and livelihoods, with over 16 million confirmed cases TRANS and at least 650 000 deaths MESHD from COVID-19 in the first 7 months of the pandemic. Developing tools to measure seroprevalence SERO and understand protective immunity to SARS-CoV-2 is a priority. We aimed to develop a serological assay SERO using plant-derived recombinant viral proteins, which represent important tools in less-resourced settings. Methods: We established an indirect enzyme-linked immunosorbent assay SERO ( ELISA SERO) using the S1 and receptor-binding domain (RBD) portions of the spike protein from SARS-CoV-2, expressed in Nicotiana benthamiana. We measured antibody SERO responses in sera from South African patients (n=77) who had tested positive by PCR for SARS-CoV-2. Samples were taken a median of six weeks after the diagnosis, and the majority of participants had mild and moderate COVID-19 disease MESHD. In addition, we tested the reactivity of pre-pandemic plasma SERO (n=58) and compared the performance SERO of our in-house ELISA SERO with a commercial assay. We also determined whether our assay could detect SARS-CoV-2-specific IgG and IgA in saliva. Results: We demonstrate that SARS-CoV-2-specific immunoglobulins are readily detectable using recombinant plant-derived viral proteins, in patients who tested positive for SARS-CoV-2 by PCR. Reactivity to S1 and RBD was detected in 51 (66%) and 48 (62%) of participants, respectively. Notably, we detected 100% of samples identified as having S1-specific antibodies SERO by a validated, high sensitivity SERO commercial ELISA SERO, and OD values were strongly and significantly correlated between the two assays. For the pre-pandemic plasma SERO, 1/58 (1.7%) of samples were positive, indicating a high specificity for SARS-CoV-2 in our ELISA SERO. SARS-CoV-2-specific IgG correlated significantly with IgA and IgM responses. Endpoint titers of S1- and RBD-specific immunoglobulins ranged from 1:50 to 1:3200. S1-specific IgG and IgA were found in saliva samples from convalescent volunteers. Conclusions: We demonstrate that recombinant SARS-CoV-2 proteins produced in plants enable robust detection of SARS-CoV-2 humoral responses. This assay can be used for seroepidemiological studies and to measure the strength and durability of antibody SERO responses to SARS-CoV-2 in infected patients in our setting.

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.

Deep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)

Authors: Devante Ayris; Kye Horbury; Blake Williams; Mitchell Blackney; Celine Shi Hui See; Syed Afaq Ali Shah

id:2008.01170v1 Date: 2020-07-29 Source: arXiv

SARS-CoV2, which causes coronavirus disease MESHD (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases TRANS of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance SERO of the proposed models.

Predicting Critical State after COVID-19 Diagnosis Using Real-World Data from 20152 Confirmed US Cases TRANS

Authors: Mike Domenik Rinderknecht; Yannick Klopfenstein

doi:10.1101/2020.07.24.20155192 Date: 2020-07-27 Source: medRxiv

The global COVID-19 pandemic caused by the virus SARS-CoV-2 has led to over 10 million confirmed cases TRANS, half a million deaths MESHD, and is challenging healthcare systems worldwide. With limited medical resources, early identification of patients with a high risk of progression to severe disease MESHD or a critical state is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (EHR) within IBM Explorys, including demographics, comorbidities, symptoms, laboratory test results, insurance types, and hospitalization. Our entire cohort included 20152 COVID-19 cases, of which 3160 patients went into critical state or died. Random, stratified train-test splits were repeated 100 times to obtain a distribution of performance SERO. The median and interquartile range of the areas under the receiver operating characteristic curve (ROC AUC) and the precision recall SERO curve (PR AUC) were 0.863 [0.857, 0.866] and 0.539 [0.526, 0.550], respectively. Optimizing the decision threshold lead to a sensitivity SERO of 0.796 [0.775, 0.821] and a specificity of 0.784 [0.769, 0.805]. Good model calibration was achieved, showing only minor tendency to over-forecast probabilities above 0.6. The validity of the model was demonstrated by the interpretability analysis confirming existing evidence on major risk factors (e.g., higher age TRANS and weight, male TRANS gender TRANS, diabetes, cardiovascular disease MESHD disease, and chronic kidney HP kidney disease MESHD). The analysis also revealed higher risk for African Americans and "self-pay patients". To the best of our knowledge, this is the largest dataset based on EHR used to create a prognosis model for COVID-19. In contrast to large-scale statistics computing odds ratios for individual risk factors, the present model combining a rich set of covariates can provide accurate personalized predictions enabling early treatment to prevent patients from progressing to a severe or critical state.

Point-of-care ultrasound for COVID-19 pneumonia MESHD pneumonia HP patients in the ICU

Authors: zouheir bitar; Mohammed Shamsah; Omar Bamasood; Ossama Maadrani; Huda Al foudri

doi:10.21203/rs.3.rs-49196/v1 Date: 2020-07-26 Source: ResearchSquare

BackgroundPoint-of-care ultrasound (POCUS) has a major role in the management of patients with acute hypoxic respiratory and circulatory failure and guides hemodynamic management. There is scarce literature on POCUS assessment characteristics in COVID-19 pneumonia MESHD pneumonia HP with hypoxic respiratory failure HP.MethodsThe study is an observational, prospective, single‐center study conducted in the intensive care unit of Adan General Hospital from May 1st, 2020, to June 25, 2020. The study included adults TRANS suspected to have COVID-19 transferred to the intensive care unit (ICU) with fever MESHD fever HP or suspected respiratory infection MESHD. Patients were transferred to the ICU directly from the ED or general medical wards after reverse transcriptase-polymerase chain reaction (RT-PCR) testing. A certified intensivist in critical care ultrasound who was blinded to the RT-PCR results, if available at the time of examination, performed the lung ultrasound and echocardiology within 12 hours of the patient’s admission to the ICU. We calculated the E/e’, E/A ratio, left ventricular ejection fraction EF, IVC diameter, RV size and systolic function. We performed ultrasound in 12 chest areas.ResultsOf 92 patients with suspected COVID-19 pneumonia MESHD pneumonia HP, 77 (84%) cases were confirmed TRANS. The median age TRANS of the patients was 53 (82-36) years, and 71 (77%) were men.In the group of patients with confirmed COVID-19 pneumonia MESHD pneumonia HP, echocardiographic findings showed normal E/e’, deceleration time (DT), and transmittal E/A ratio in comparison to the non-COVID19 patients (P .001 for both). The IVC diameter was <2 cm with > 50% collapsibility in 62 (81%) patients with COVID-19 pneumonia MESHD pneumonia HP; a diameter of > 2 cm and < 50% collapsibility in all patients, with a P value of 0.001, was detected among those with non-COVID-19 pneumonia MESHD pneumonia HP. There were 3 cases of myocarditis MESHD myocarditis HP with poor EF (5.5%), severe RV dysfunction was seen in 9 cases (11.6%), and 3 cases showed RV thrombus.Chest US revealed four signs suggestive of COVID-19 pneumonia MESHD pneumonia HP in 77 patients (98.6%) ( sensitivity SERO 96.9%, CI 85%‐99.5%) when compared with RT-PCR results.ConclusionPOCUS plays an important role in bedside diagnosis, hemodynamic assessment and management of patients with acute hypoxic respiratory and circulatory failure in patients with COVID-19 pneumonia MESHD pneumonia HP.

A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases MESHD such as COVID-19

Authors: Rahul Singh; Fang Liu; Ness B. Shroff

id:2007.13023v1 Date: 2020-07-25 Source: arXiv

The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows infected individuals to be quarantined, thus reducing the spread of COVID-19, saving countless lives, and helping to restart the economy safely and securely. Developing a good testing strategy can be greatly aided by contact tracing TRANS that provides health care providers information about the whereabouts of infected patients in order to determine whom to test. Countries that have been more successful in corralling the virus typically use a test, treat, trace TRANS, test'' strategy that begins with testing individuals with symptoms, traces contacts TRANS of positively tested individuals via a combinations of patient memory, apps, WiFi, GPS, etc., followed by testing their contacts, and repeating this procedure. The problem is that such strategies are myopic and do not efficiently use the testing resources. This is especially the case with COVID-19, where symptoms may show up several days after the infection MESHD (or not at all, there is evidence to suggest that many COVID-19 carriers TRANS are asymptotic TRANS, but may spread the virus). Such greedy strategies, miss out population areas where the virus may be dormant and flare up in the future. In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing TRANS tools. We then develop efficient learning strategies that minimize the number of infected individuals. These strategies are based on policy iteration and look-ahead rules. We investigate fundamental performance SERO bounds, and ensure that our solution is robust to errors in the input graph as well as in the tests themselves.

Serum SERO interleukin-6 is an indicator for severity in 901 patients with SARS-CoV-2 infection MESHD: A cohort study

Authors: Jing Zhang; Yiqun Hao; Wuling Ou; Fei Ming; Gai Liang; Yu Qian; Qian Cai; Shuang Dong; Sheng Hu; Weida Wang; Shaozhong Wei

doi:10.21203/rs.3.rs-47937/v1 Date: 2020-07-23 Source: ResearchSquare

Background Interleukin-6 (IL-6) was proposed to be associated with the severity of coronavirus disease MESHD 2019 (COVID-19). The present study aimed to explore the kinetics of IL-6 levels, validate this association in COVID-19 patients, and report preliminary data on the efficacy of IL-6 receptor blockade.Methods We conducted a retrospective single-institutional study of 901 consecutive confirmed cases TRANS. Serum SERO IL-6 concentrations were tested on admission and/or during hospital stay. Tocilizumab was given to 16 patients with elevated IL-6 concentration.Results 366 patients were defined as common cases, 411 patients as severe, and 124 patients as critical according to the Chinese guideline on diagnosis and treatment of COVID-19. The median concentration of IL-6 was < 1.5 pg/ml (IQR < 1.50–2.15), 1.85 pg/ml (IQR < 1.50–5.21), and 21.55 pg/ml (IQR 6.47–94.66) for the common, severe, and critical groups respectively (P༜0.001). The follow-up kinetics revealed serum SERO IL-6 remained high in critical patients even when cured. An IL-6 concentration higher than 37.65 pg/ml was predictive of in-hospital death MESHD (AUC 0.97 [95%CI 0.95–0.99], P < 0.001) with a sensitivity SERO of 91.7% and a specificity of 95.7%. In the 16 patients who received tocilizumab, IL-6 concentrations were significantly increased after administration, and survival outcome was not significantly different from that of propensity-score matched counterparts (n = 53, P = 0.12).Conclusion Serum SERO IL-6 should be included in diagnostic work-up to stratify disease MESHD severity, but the benefit of tocilizumab needs further confirmation.Trial registration: retrospectively registered.

Backtesting the predictability of COVID-19

Authors: Dmitry Gordeev; Philipp Singer; Marios Michailidis; Mathias Müller; SriSatish Ambati

id:2007.11411v1 Date: 2020-07-22 Source: arXiv

The advent of the COVID-19 pandemic has instigated unprecedented changes in many countries around the globe, putting a significant burden on the health sectors, affecting the macro economic conditions, and altering social interactions HP social interactions TRANS amongst the population. In response, the academic community has produced multiple forecasting models, approaches and algorithms to best predict the different indicators of COVID-19, such as the number of confirmed infected cases. Yet, researchers had little to no historical information about the pandemic at their disposal in order to inform their forecasting methods. Our work studies the predictive performance SERO of models at various stages of the pandemic to better understand their fundamental uncertainty and the impact of data availability on such forecasts. We use historical data of COVID-19 infections MESHD from 253 regions from the period of 22nd January 2020 until 22nd June 2020 to predict, through a rolling window backtesting framework, the cumulative number of infected cases for the next 7 and 28 days. We implement three simple models to track the root mean squared logarithmic error in this 6-month span, a baseline model that always predicts the last known value of the cumulative confirmed cases TRANS, a power growth model and an epidemiological model called SEIRD. Prediction errors are substantially higher in early stages of the pandemic, resulting from limited data. Throughout the course of the pandemic, errors regress slowly, but steadily. The more confirmed cases TRANS a country exhibits at any point in time, the lower the error in forecasting future confirmed cases TRANS. We emphasize the significance of having a rigorous backtesting framework to accurately assess the predictive power of such models at any point in time during the outbreak which in turn can be used to assign the right level of certainty to these forecasts and facilitate better planning.

Short-term forecasting COVID-19 cumulative confirmed cases TRANS: Perspectives for Brazil

Authors: Matheus Henrique Dal Molin Ribeiro; Ramon Gomes da Silva; Viviana Cocco Mariani; Leandro dos Santos Coelho

id:2007.12261v1 Date: 2020-07-21 Source: arXiv

The new Coronavirus (COVID-19) is an emerging disease MESHD responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow knowing the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths MESHD. In this paper, autoregressive integrated moving average (ARIMA), cubist (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases TRANS in ten Brazilian states with a high daily incidence. In the stacking learning approach, the cubist, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking ensemble learning reach a better performance SERO regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87% - 3.51%, 1.02% - 5.63%, and 0.95% - 6.90% in one, three, and six-days-ahead, respectively. The ranking of models in all scenarios is SVR, stacking ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.

Winter is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

Authors: Albertus Smit; Jennifer Fitchett; Francois Engelbrecht; Robert Scholes; Godfrey Dzhivhuho; Neville Sweijd

id:10.20944/preprints202007.0456.v1 Date: 2020-07-20 Source: preprints.org

SARS-CoV-2 virus infections MESHD in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020 COVID-19 is present in 213 countries and territories, with over 12 million confirmed cases TRANS and over half a million attributed deaths MESHD. Knowledge of other viral respiratory diseases MESHD suggests that the transmission TRANS of SARS-CoV-2 could be modulated by seasonally-varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity SERO of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission TRANS rate in places entering the boreal summer and that southern hemisphere countries would experience enhanced disease MESHD. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease MESHD has been prevalent for only half a year in the northern, and a quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e. using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the ‘Global South’ have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally-modulated transmission TRANS, if it exists, will be more evident in 2021 and subsequent years.

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 All None MeSH Disease Human Phenotype Transmission Seroprevalence

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