bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-throughput extraction of SARS-CoV-2 RNA from nasopharyngeal swabs using solid-phase reverse immobilization beads
Authors: Kalikiri Mahesh K. R. , Hasan Mohammad Rubayet , Mirza Faheem , Xaba Thabisile , Tang Patrick , Lorenz Stephan 
Published: April 2020

ABSTRACTThe ongoing pandemic of the novel coronavirus, SARS-CoV-2, has led to a global surge in laboratory testing for the virus. The gold standard approach to detecting an active viral infection is the use of RT-qPCR. This approach requires the isolation of viral RNA from respiratory specimens, such as nasopharyngeal swabs.We developed a method using a widely available lysis buffer coupled with solid-phase reverse immobilization (SPRI) beads to extract viral RNA from swabs collected in viral transport medium (VTM) which can be performed manually or on a Hamilton STAR liquid-handling robot. Using a WHO recommended, laboratory-developed RT-qPCR for SARS-CoV-2, we validated this method in a CAP-accredited laboratory, against the IVD-labelled bioMérieux NucliSENS easyMAG automated extraction platform.Our method demonstrates a comparable sensitivity and specificity, making it suitable for large-scale testing and monitoring of suspected COVID-19 cases and health care workers. This is especially important as the world faces critical shortages of viral RNA extraction reagents for the existing commercial extraction systems.
A first study on the impact of current and future control measures on the spread of COVID-19 in Germany
Authors: Barbarossa Maria Vittoria , Fuhrmann Jan , Heidecke Julian , Varma Hridya Vinod , Castelletti Noemi , Meinke Jan H. , Krieg Stefan , Lippert Thomas 
Published: April 2020

AbstractThe novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe, with about 571,700 confirmed cases and about 26,500 deaths as of March 28th, 2020. We present here the preliminary results of a mathematical study directed at informing on the possible application or lifting of control measures in Germany. The developed mathematical models allow to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions.
Development of a dual-gene loop-mediated isothermal amplification (LAMP) detection assay for SARS-CoV-2: A preliminary study
Authors: Butt Azeem Mehmood , Siddique Shafiqa , An Xiaoping , Tong Yigang 
Published: April 2020

AbstractSevere acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) has emerged as a rapidly spreading global pathogen stressing the need for development of rapid testing protocols ever than before. The aim of present study was to develop a SARS-CoV-2 detection protocol which can be performed within minimal resources and timeframe. For this purpose, we implemented the reverse transcription loop-mediated isothermal amplification (RT-LAMP) methodology for the qualitative detection of SARS-CoV-2 RNA. In order to improve the detection capability, the RT-LAMP assay was developed to simultaneously amplify two viral genes: ORF1a and N. A total of 45 SARS-CoV-2 associated coronavirus disease 2019 (COVID-19) and 25 non-COVID-19 cases were enrolled. Viral RNA was extracted from the nasopharyngeal swab samples and analyzed simultaneously using PCR and RT-LAMP protocols. Overall, our SARS-CoV-2 dual gene RT-LAMP assay was found to be 95% accurate in detecting positive cases and showed no cross-reactivity or false-positive results in non-COVID-19 samples. Further evaluation on larger and multi-centric cohorts is currently underway to establish the diagnostic accuracy and subsequent implementation into clinical practice and at point-of-care settings.
COVID-19 epidemic in Malaysia: Impact of lockdown on infection dynamics
Authors: Salim Naomie , Chan Weng Howe , Mansor Shuhaimi , Nazira Bazin Nor Erne , Amaran Safiya , Mohd Faudzi Ahmad Athif , Zainal Anazida , Huspi Sharin Hazlin , Jiun Hooi Eric Khoo , Shithil Shaekh Mohammad 
Published: April 2020

AbstractCOVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. It was followed by a bigger wave mainly from local transmissions resulting in 651 cases. The following wave saw unexpectedly three digit number of daily cases following a mass gathering urged the government to choose a more stringent measure. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. The lock-down causes a major socio-economic disruption thus the ability to forecast the infection dynamic is urgently required to assist the government on timely decisions. Limited testing capacity and limited epidemiological data complicate the understanding of the future infection dynamic of the COVID-19 epidemic. Three different epidemic forecasting models was used to generate forecasts of COVID-19 cases in Malaysia using daily reported cumulative case data up until 1st April 2020 from the Malaysia Ministry of Health. The forecasts were generated using a Curve Fitting Model with Probability Density Function and Skewness Effect, the SIR Model, and a System Dynamic Model. Method one based on curve fitting with probability density function estimated that the peak will be on 19th April 2020 with an estimation of 5,637 infected persons. Method two based on SIR Model estimated that the peak will be on 20th - 31st May 2020 if Movement Contro (MCO) is in place with an estimation of 630,000 to 800,000 infected persons. Method three based on System Dynamic Model estimated that the peak will be on 17th May 2020 with an estimation of 22,421 infected persons. Forecasts from each of model suggested the epidemic may peak between middle of April to end of May 2020.
Regulation of Heart Beats by the Autonomous Nervous System in Health and Disease: Point-Process-Theory based Models and Simulation [V-I]
Authors: Emmanuel Bosco 
Published: April 2020

AbstractWe have advanced a point-process based framework for the regulation of heart beats by the autonomous nervous system and analyzed the model with and without feedback. The model without feedback was found amenable to several analytical results that help develop an intuition about the way the heart interacts with the nervous system. However, in reality, feedback, baroreflex and chemoreflex controls are important to model healthy and unhealthy scenarios for the heart. Based on the Hurst exponent as an index of health of the heart we show how the state of the nervous system may tune it in health and disease. Monte Carlo simulation is used to generate RR interval series of the Electrocardiogram (ECG) for different sympathetic and parasympathetic nerve excitations.
Same-Day Simultaneous Diagnosis of Bacterial and Fungal Infections in Clinical Practice by Nanopore Targeted Sequencing
Authors: Wang Ming , Fu Aisi , Hu Ben , Shen Gaigai , Liu Ran , Zhao Wanxu , Jiang Shupeng , Cai Xuan , Li Congrong , Li Juan , Wu Qing , Feng Kai , Gu Jiashuang , Chen Jia , Shu Mingyue , Zhang Binghong , Deng Zixin , Yu Lilei , Li Yan , Liu Tiangang 
Published: April 2020

AbstractBACKGROUNDAs approximately 19% of global deaths are attributable to infectious diseases, early diagnosis of infection is very important to reduce mortality. Traditional infection detection strategies have limited sensitivity, detection range, and turnaround times; a detection technology that can simultaneously detect bacterial and fungal infections within 24 h is urgently need in clinical settings.METHODSWe developed nanopore targeted sequencing (NTS) for same-day simultaneous Diagnosis of fungal and bacterial infections. NTS was developed by amplification of 16s rRNA gene (for bacteria), IST1/2 gene (for fungal), and rpoB (for Mycobacterium spp.) using multiple primers, and sequenced by a real-time nanopore sequencing platform. An in-house bioinformatic analyze pipeline was used to diagnose the infectious pathogens by mapping the sequencing results with the constructed databases.RESULTSComparison of 1312 specimens from 1257 patients using NTS and culture method; NTS detected pathogens in 58.71% of specimens from patients, compared to 22.09% detected using the culture method. NTS showed significantly higher sensitivity than culture methods for many pathogens. Importantly, a turnaround time of <24 h for all specimens, and a pre-report within 6 h in emergency cases was possible in clinical practice. Modification of antibiotic therapy and maintenance of original anti-infection regimens in 51.52% (17/33) and 36.36% (12/33) of patients was in accordance with NTS results, and quantitative monitoring of clinical treatment effects was evaluated in four patients by continuous NTS tests.CONCLUSIONSApplication of NTS in clinically detected pathogens can improve targeted antibiotic treatment and therapeutic monitoring.
Factors associated with hospitalization and critical illness among 4,103 patients with Covid-19 disease in New York City
Authors: Petrilli Christopher M. , Jones Simon A. , Yang Jie , Rajagopalan Harish , O’Donnell Luke , Chernyak Yelena , Tobin Katie A. , Cerfolio Robert J. , Francois Fritz , Horwitz Leora I. 
Published: April 2020

AbstractBackgroundLittle is known about factors associated with hospitalization and critical illness in Covid-19 positive patients.MethodsWe conducted a cross-sectional analysis of all patients with laboratory-confirmed Covid-19 treated at an academic health system in New York City between March 1, 2020 and April 2, 2020, with follow up through April 7, 2020. Primary outcomes were hospitalization and critical illness (intensive care, mechanical ventilation, hospice and/or death). We conducted multivariable logistic regression to identify risk factors for adverse outcomes, and maximum information gain decision tree classifications to identify key splitters.ResultsAmong 4,103 Covid-19 patients, 1,999 (48.7%) were hospitalized, of whom 981/1,999 (49.1%) have been discharged, and 292/1,999 (14.6%) have died or been discharged to hospice. Of 445 patients requiring mechanical ventilation, 162/445 (36.4%) have died. Strongest hospitalization risks were age ≥75 years (OR 66.8, 95% CI, 44.7-102.6), age 65-74 (OR 10.9, 95% CI, 8.35-14.34), BMI>40 (OR 6.2, 95% CI, 4.2-9.3), and heart failure (OR 4.3 95% CI, 1.9-11.2). Strongest critical illness risks were admission oxygen saturation <88% (OR 6.99, 95% CI 4.5-11.0), d-dimer>2500 (OR 6.9, 95% CI, 3.2-15.2), ferritin >2500 (OR 6.9, 95% CI, 3.2-15.2), and C-reactive protein (CRP) >200 (OR 5.78, 95% CI, 2.6-13.8). In the decision tree for admission, the most important features were age >65 and obesity; for critical illness, the most important was SpO2<88, followed by procalcitonin >0.5, troponin <0.1 (protective), age >64 and CRP>200.ConclusionsAge and comorbidities are powerful predictors of hospitalization; however, admission oxygen impairment and markers of inflammation are most strongly associated with critical illness.
Burden and prevalence of prognostic factors for severe covid-19 disease in sweden
Authors: Gémes Katalin , Talbäck Mats , Modig Karin , Ahlbom Anders , Berglund Anita , Feychting Maria , Matthews Anthony A. 
Published: April 2020

ABSTRACTObjectivesDescribe the burden and prevalence of prognostic factors of severe COVID-19 disease at national and county level in Sweden.DesignCross sectional studySettingSwedenParticipants9,624,428 individuals living in Sweden on 31st December 2014 and alive on 1st January 2016Main outcome measuresBurden and prevalence of prognostic factors for severe COVID-19 based on the guidelines from the World Health Organization and European Centre for Disease Prevention and Control, which are age 70 years and older, cardiovascular disease, cancer, chronic obstructive pulmonary disease, severe asthma, and diabetes. Prognostic factors were identified based on records for three years before 1st January 2016 from the Swedish National Inpatient and Outpatient Specialist Care Register, Prescribed Drug Register, and Cancer Register.Results22.1% of the study population had at least one prognostic factor for severe COVID-19 (2,131,319 individuals), and 1.6% had at least three factors (154,746 individuals). The prevalence of underlying medical conditions in the whole study population ranged from 0.8% with chronic obstructive pulmonary disease (78,516 individuals) to 7.4% with cardiovascular disease (708,090 individuals), and the county specific prevalence of at least one prognostic factor ranged from 19.2% in Stockholm (416,988 individuals) to 25.9% in Kalmar (60,005 individuals).ConclusionsThe prevalence of prognostic factors for severe COVID-19 disease will aid authorities in optimally planning healthcare resources during the ongoing pandemic. Results can also be applied to underlying assumptions of disease burden in modelling efforts to support COVID-19 planning. This information is crucial when deciding appropriate strategies to mitigate the pandemic and reduce both the direct mortality burden from the disease itself, and the indirect mortality burden from potentially overwhelmed health systems.
Increased gametocyte production and mosquito infectivity in chronic versus incident Plasmodium falciparum infections
Authors: Barry Aissata , Bradley John , Stone Will , Guelbeogo Moussa W. , Lanke Kjerstin , Ouedraogo Alphonse , Soulama Issiaka , Nébié Ouedraogo Issa , Serme Samuel S. , Grignard Lynn , Patterson Katie , Awandu Shehu S. , Ouedraogo Mireille , Tarama Casimire W. , Kargougou Désiré , Zoumanaba Zongo , Sirima Sodiomon B. , Marti Matthias , Drakeley Chris , Tiono Alfred B. , Bousema Teun 
Published: April 2020

AbstractWe longitudinally assessed P. falciparum parasite kinetics, gametocyte production and infectivity in incident infections that were naturally acquired following infection clearance and in chronic asymptomatic infections in Burkina Faso. 92% (44/48) of the incident cohort developed symptoms and were treated within 35 days, compared to 23% (14/60) of the chronic cohort. All but two individuals with chronic infection were gametocytaemic at enrollment, whereas only 35% (17/48) in the incident cohort developed gametocytes within 35 days. The relative abundance of ap2-g transcripts was positively associated with conversion to gametocyte production (i.e. the ratio of gametocytes at day 14 to ring stage parasites at baseline) and was higher in chronic infections. Parasite multiplication rate, assessed by daily molecular parasite quantification, was positively associated with prospective gametocyte production. Most incident infections were cleared before gametocyte density was sufficiently high to infect mosquitoes. In contrast, chronic, asymptomatic infections represented a significant source of mosquito infections. If present, gametocytes were significantly less infectious if concurrent with malaria symptoms. Our observations support the notion that malaria transmission reduction may be expediated by enhanced case management, involving both symptom-screening and infection detection.
Estimating the Growth Rate and Doubling Time for Short-Term Prediction and Monitoring Trend During the COVID-19 Pandemic with a SAS Macro
Authors: Xu Stanley , Clarke Christina , Shetterly Susan , Narwaney Komal 
Published: April 2020

ABSTRACTCoronavirus disease (COVID-19) has spread around the world causing tremendous stress to the US health care system. Knowing the trend of the COVID-19 pandemic is critical for the federal and local governments and health care system to prepare plans. Our aim was to develop an approach and create a SAS macro to estimate the growth rate and doubling time in days if growth rate is positive or half time in days if growth rate is negative. We fit a series of growth curves using a rolling approach. This approach was applied to the hospitalization data of Colorado State during March 13th and April 13th. The growth rate was 0.18 (95% CI=(0.11, 0.24)) and the doubling time was 5 days (95% CI= (4, 7)) for the period of March 13th-March 19th; the growth rate reached to the minimum −0.19 (95% CI= (−0.29, −0.10)) and the half time was 4 days (95% CI= (2, 6)) for the period of April 2nd – April 8th. This approach can be used for regional short-term prediction and monitoring the regional trend of the COVID-19 pandemic.
Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations
Authors: Ayoub Houssein H. , Chemaitelly Hiam , Mumtaz Ghina R , Seedat Shaheen , Awad Susanne F. , Makhoul Monia , Abu-Raddad Laith J. 
Published: April 2020

AbstractBackgroundA novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China in late 2019. The resulting disease, Coronavirus Disease 2019 (COVID-2019), soon became a pandemic. This study aims to characterize key attributes of the epidemiology of this infection in China.MethodsAn age-stratified mathematical model was constructed to describe the transmission dynamics and estimate the age-specific differences in the biological susceptibility to the infection, age-assortativeness in transmission mixing, case fatality rate (CFR), and transition in rate of infectious contacts (and reproduction number R0) following introduction of mass interventions.ResultsThe model estimated the infectious contact rate in early epidemic at 0.59 contacts per day (95% uncertainty interval (UI)=0.48-0.71). Relative to those 60-69 years of age, susceptibility to the infection was only 0.06 in those ≤19 years, 0.34 in 20-29 years, 0.57 in 30-39 years, 0.69 in 40-49 years, 0.79 in 50-59 years, 0.94 in 70-79 years, and 0.88 in ≥80 years. The assortativeness in transmission mixing by age was very limited at 0.004 (95% UI=0.002-0.008). Final CFR was 5.1% (95% UI=4.8-5.4%). R0 rapidly declined from 2.1 (95% UI=1.8-2.4) to 0.06 (95% UI=0.05-0.07) following onset of interventions.ConclusionAge appears to be a principal factor in explaining the patterns of COVID-19 transmission dynamics in China. The biological susceptibility to the infection seems limited among children, intermediate among young to mid-age adults, but high among those >50 years of age. There was no evidence for differential contact mixing by age, consistent with most transmission occurring in households rather than in schools or workplaces.
Accounting for incomplete testing in the estimation of epidemic parameters
Authors: Betensky R.A. , Feng Y. 
Published: April 2020

AbstractAs the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.
Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid
Authors: Chao Dennis L. , Oron Assaf P. , Srikrishna Devabhaktuni , Famulare Michael 
Published: April 2020

AbstractBackgroundThe novel coronavirus SARS-CoV-2 has rapidly spread across the globe and is poised to cause millions of deaths worldwide. There are currently no proven pharmaceutical treatments, and vaccines are likely over a year away. At present, non-pharmaceutical interventions (NPIs) are the only effective option to reduce transmission of the virus, but it is not clear how to deploy these potentially expensive and disruptive measures. Modeling can be used to understand the potential effectiveness of NPIs for both suppression and mitigation efforts.Methods and FindingsWe developed Corvid, an adaptation of the agent-based influenza model called FluTE to SARS-CoV-2 transmission. To demonstrate features of the model relevant for studying the effects of NPIs, we simulated transmission of SARS-CoV-2 in a synthetic population representing a metropolitan area in the United States. Transmission in the model occurs in several settings, including at home, at work, and in schools. We simulated several combinations of NPIs that targeted transmission in these settings, such as school closures and work-from-home policies. We also simulated three strategies for testing and isolating symptomatic cases. For our demonstration parameters, we show that testing followed by home isolation of ascertained cases reduced transmission by a modest amount. We also show how further reductions may follow by isolating cases in safe facilities away from susceptible family members or by quarantining all family members to prevent transmission from likely infections that have yet to manifest.ConclusionsModels that explicitly include settings where individuals interact such as the home, work, and school are useful for studying the effectiveness of NPIs, as these are more dependent on community structure than pharmaceutical interventions such as vaccination. Corvid can be used to help evaluate complex combinations of interventions, although there is no substitute for real-world observations. Our results on NPI effectiveness summarize the behavior of the model for an assumed set of parameters for demonstration purposes. Model results can be sensitive to the assumptions made about disease transmission and the natural history of the disease, both of which are not yet sufficiently characterized for SARS-CoV-2 for quantitative modeling. Models of SARS-CoV-2 transmission will need to be updated as the pathogen becomes better-understood.