bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nasopharyngeal Colonization, Associated Factors and Antimicrobial Resistance ofStreptococcus pneumoniaeamong Children under 5 Years of Age in the Southwestern Colombia
Authors: Gámez Gustavo , Rojas Juan Pablo , Cardona Santiago , Castillo Juan David , Palacio María Alejandra , Mejía Luis Fernando , Torres José Luis , Contreras Jaime , Muñoz Laura Mery , Criales Javier , Vélez Luis Felipe , Forero Angélica María , Zúñiga Yulieth Alexandra , Cuastumal María Eugenia , Acevedo Leidy Johanna , de Jesús Molina Álvaro , Bolivar Johan Alexis , Gómez-Mejia Alejandro , Morales Jessica Lorena , Hammerschmidt Sven 
Published: April 2020

AbstractStreptococcus pneumoniaediseases are important causes of children death worldwide. Nasopharyngeal carriage of this pathobiont promotes bacterial spread and infections in the community. Here, a cross-sectional surveillance study was done to determine the proportion of nasopharyngeal colonization, antimicrobial susceptibility profile and associated factors in pediatric outpatients (southwestern Colombia, 2019). Data on factors associated with pneumococcal nasopharyngeal carriage were obtained through survey-based interviews. Nasopharyngeal swabs were collected and bacteria were microbiologically characterized. Antimicrobial susceptibility tests were done by VITEK-2. A logistic regression analysis was performed to examine associated factors. Tests with ap-value <0.05 were considered statistically significant. 452 children from the southwestern Colombia were examined and 41.8% carriedS. pneumoniae. A higher pneumococcal carriage frequency was observed among participants <2-years and in individuals belonging to indigenous communities, which were not immunized against pneumococcus, because of lacking established immunization schemes. Additionally, children attending child-care institutions were also highly colonized by pneumococci.S. pneumoniaeshowed 57.7% non-susceptibility to benzyl-penicillin (meningitis-cut); 45.5% intermediate-sensitivity to benzyl-penicillin (oral-cut) and 21.7% to cefotaxime; and resistance to erythromycin (40.7%), tetracycline (36.0%), trimethoprim/sulfamethoxazole (24.9%), clindamycin (24.3%) and ceftriaxone (27.0%). The proportion of 41.8% of participants carryingS. pneumoniaeshows a scenario with the presence of strains resistant to different antimicrobial agents (MDR and XDR), which constitutes important reservoirs of bacterial transmission by children <5-years in the southwest of Colombia. This situation could potentially lead to an onset of pneumococcal diseases. Hence, the need to expand conjugate pneumococcal immunization in the community and ensure compliance with established immunization schedules.
Preliminary study to identify severe from moderate cases of COVID-19 using NLR&RDW-SD combination parameter
Authors: Wang Changzheng , Deng Rongrong , Gou Liyao , Fu Zhongxiao , Zhang Xiaomei , Shao Feng , Wang Guanzhen , Fu Weiyang , Xiao Jianping , Ding Xiao , Li Tao , Xiao Xiulin , Li Chengbin 
Published: April 2020

AbstractObjectivesInvestigate the characteristics and rules of hematology changes in patients with COVID-19, and explore the possibility to identify moderate and severe patients using conventional hematology parameters or combined parameters.MethodsThe clinical data of 45 moderate and severe type patients with SARS-CoV-2 infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between the two groups were analyzed, and the combination parameters with best diagnostic performance were selected using the LDA method.ResultsOf the 45 patients with COVID-19 (35 moderate and 10 severe cases), 23 were male and 22 female, aged 16-62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, WBC, Neu#, NLR, PLR, RDW-CV and RDW-SD parameters in the severe group were significantly higher than that in the moderate group (P<0.05); meanwhile, Lym#, Eos#, HFC%, RBC, HGB and HCT parameters in the severe group were significantly lower than that in the moderate group (P<0.05). For NLR, the AUC, the best cut-off value, the sensitivity and the specificity were 0.890, 13.39, 83.3% and 82.4% respectively, and for PLR, the AUC, the best cut-off, the sensitivity and the specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameter NLR&RDW-SD had the best diagnostic efficiency (AUC was 0.938) and when the cut-off value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the fitting parameter NLR&RDW-CV (AUC = 0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively.ConclusionsThe combined parameter NLR&RDW-SD is the best hematology index and can help clinicians to predict the severity of COVID-19 patients, and it can be used as a useful indicator to help prevent and control the epidemic.
Associations of clinical characteristics and antiviral drugs with viral RNA clearance in patients with COVID-19 in Guangzhou, China: a retrospective cohort study
Authors: Chen Xudan , Zhang Yuying , Zhu Baoyi , Zeng Jianwen , Hong Wenxin , He Xi , Chen Jingfeng , Zheng Haipeng , Qiu Shuang , Deng Ying , Chan Juliana CN , Wang Jian 
Published: April 2020

AbstractBackgroundThe novel coronavirus disease 2019 (COVID-19) characterized by respiratory symptoms has become a global pandemic although factors influencing viral RNA clearance remained unclear to inform optimal isolation period and treatment strategies.MethodsIn this retrospective study, we included patients with confirmed COVID-19 admitted to Guangzhou Eighth People’s Hospital from 20th January 2020 to 15th March 2020. The associations of clinical characteristics and treatment regimens on time to viral RNA clearance were analyzed.ResultsWe examined 284 consecutive COVID-19 cases, accounting for 82% of confirmed cases in Guangzhou during this period. At the time of reporting (20th March 2020), 276 (97.2%) had recovered and were discharged from hospital with a median hospital stay of 18 days (interquartile range [IQR]:13-24). Overall, 280 patients achieved viral RNA clearance with a median length of 12 days (IQR: 8-16) after onset of illness. Amongst them, 66.1% had viral RNA cleared within 14 days, and 89.3% within 21 days. Older age, severity of disease, time lag from illness onset to hospital admission, high body temperature, and corticosteroid use were associated with delayed clearance of viral RNA. None of the antiviral regimens (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) improved viral RNA clearance. The use of lopinavir/ritonavir was associated with delayed clearance of viral RNA after adjusting for confounders.ConclusionIn patients with COVID-19, isolation for a minimum of 21 days after onset of illness may be warranted, while the use of antiviral drugs does not enhance viral RNA clearance.Brief SummaryViral RNA was cleared in 89% of the COVID-19 patients within 21 days after illness onset. The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases.
Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data
Authors: Distante Cosimo , Pereira Igor Gadelha , Gonçalves Luiz M. Garcia , Piscitelli Prisco , Miani Alessandro 
Published: April 2020

AbstractBackgroundEpidemiological figures of Covid-19 epidemic in Italy are worse than those observed in China.MethodsWe modeled the Covid-19 outbreak in Italian Regions vs. Lombardy to assess the epidemics progression and predict peaks of new daily infections and total cases by learning from the entire Chinese epidemiological dynamics. We trained an artificial neural network model, a modified auto-encoder with Covid-19 Chinese data, to forecast epidemic curve of the different Italian regions, and use the susceptible–exposed–infected–removed (SEIR) compartment model to predict the spreading and peaks. We have estimated the basic reproduction number (R0) - which represents the average number of people that can be infected by a person who has already acquired the infection - both by fitting the exponential growth rate of the infection across a 1-month period, and also by using a day by day assessment, based on single observations.ResultsThe expected peak of SEIR model for new daily cases was at the end of March at national level. The peak of overall positive cases is expected by April 11th in Southern Italian Regions, a couple of days after that of Lombardy and Northern regions. According to our model, total confirmed cases in all Italy regions could reach 160,000 cases by April 30th and stabilize at a plateau.ConclusionsTraining neural networks on Chinese data and use the knowledge to forecast Italian spreading of Covid-19 has resulted in a good fit, measured with the mean average precision between official Italian data and the forecast.
COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness
Authors: Rai Balram , Shukla Anandi , Dwivedi Laxmi Kant 
Published: April 2020

AbstractIntroductionThe COVID-19 has emerged as a global concern for public health due to large scale outbreak. The number of confirmed cases has also been increased in India in past few weeks. The predictions for the COVID-19 can provide insights into the epidemiology of the disease, which helps policymakers to check health system capacities.MethodsWe obtained data on daily confirmed, recovered and deaths cases for a period of 21 days and have implemented the exponential growth model to predict the future cases for all the three components. The mathematical model was used to calculate the average reproduction number and herd immunity. We estimated the number of active cases till 30th of April. We have also tried to analyze the public health capacity to combat COVID-19 in India.ResultsIf the exponential growth in number of cases continue then the total number of active cases will be 2,49,635 until the end of April. The reproduction number for COVID-19 in India was found to be 2.56 and herd immunity as 61%. The cumulative cases predicted by the mathematical model was 1,20,203.DiscussionThis prediction provides an alarming situation for India in terms of public health preparedness. The number of tests is needed to increase to detect all the cases of COVID-19 in India. Though some serious preventive measures have been implemented, but India should be ready to face any sudden community outbreak.
Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
Authors: Chakraborty Tanujit , Ghosh Indrajit 
Published: April 2020

AbstractThe coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.
A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany
Authors: Shen Julia 
Published: April 2020

AbstractPrediction on the peak time of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities and states. In this paper we design a recursive bifurcation model for analyzing COVID-19 virus spread in different countries. The bifurcation facilitates a recursive processing of infected population through linear least-squares fitting. In addition, a nonlinear least-squares fitting is utilized to predict the future values of infected populations. Numerical results on the data from three countries (South Korea, United States and Germany) indicate the effectiveness of our approach.
Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply
Authors: Liu Pai , Beeler Payton , Chakrabarty Rajan K. 
Published: April 2020

AbstractSocial distancing has been adopted as a non-pharmaceutical intervention to prevent the COVID-19 pandemic from overwhelming the medical resources across the United States (US). The catastrophic socio-economic impacts of this intervention could outweigh its benefits if the timing and duration of implementation are left uncontrolled and ill-strategized. Here we investigate the dynamics of social distancing on age-stratified US population and benchmark its effectiveness in reducing the burden on hospital and ICU beds. Our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. We determine an optimal intermittent social-to-no-distancing ratio of 5:1 corresponding to ∼80% reduction in healthcare demands – beyond this ratio, benefit of social distancing diminishes to a negligible level.COVID-19 Medical Demand Forecasthttps://eece.wustl.edu/chakrabarty-group/covid/
Sero-epidemiological evaluation of malaria transmission in The Gambia before and after mass drug administration
Authors: Wu Lindsey , Mwesigwa Julia , Affara Muna , Bah Mamadou , Correa Simon , Hall Tom , Beeson James G. , Tetteh Kevin K.A. , Kleinschmidt Immo , D’Alessandro Umberto , Drakeley Chris 
Published: April 2020

AbstractBackgroundAs The Gambia aims to achieve elimination by 2030, serological assays are a useful surveillance tool to monitor trends in malaria incidence and evaluate community-based interventions.MethodsWithin a mass drug administration (MDA) study in The Gambia, where reduced malaria infection and clinical disease were observed after the intervention, a serological sub-study was conducted in four study villages. Spatio-temporal variation in transmission was measured with a panel of recombinant Pf antigens on a multiplexed bead-based assay. Village-level antibody levels were quantified as under-15 sero-prevalence, sero-conversion rates, and age-adjusted antibody acquisition rates. Antibody levels prior to MDA were assessed for association with persistent malaria infection after community chemoprophylaxis.ResultsSeasonal changes in antibodies to Etramp5.Ag1 were observed in children under 15 in two transmission settings – the West Coast and Upper River Regions (4·32% and 31·30% Pf prevalence, respectively). At the end of the malaria season, short-lived antibody responses to Etramp5.Ag1, GEXP18, HSP40.Ag1, EBA175 RIII-V, and Rh2.2030 were lower amongst 1-15 year olds in the West Coast compared to the Upper River, reflecting known differences in transmission. Prior to MDA, individuals in the top 50th percentile of antibody levels had two-fold higher odds of clinical malaria during the transmission season, consistent with previous findings where individuals infected pre-MDA had 2-fold higher odds of re-infection post-MDA.ConclusionSerological markers can serve dual functions as indicators of malaria exposure and incidence. Further studies, particularly cluster randomised trials, can help establish standardised serological protocols to measure transmission across endemic settings.
Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification
Authors: Fisman David N. , Greer Amy L. , Tuite Ashleigh R. 
Published: April 2020

AbstractBackgroundEpidemiological data from the COVID-19 pandemic has demonstrated variability in attack rates by age, and country-to-country variability in case fatality ratio (CFR).ObjectiveTo use direct and indirect standardization for insights into the impact of age-specific under-reporting on between-country variability in CFR, and apparent size of COVID-19 epidemics.DesignPost-hoc secondary data analysis (“case studies”), and mathematical modeling.SettingChina, global.InterventionsNone.MeasurementsData were extracted from a sentinel epidemiological study by the Chinese Center for Disease Control (CCDC) that describes attack rates and CFR for COVID-19 in China prior to February 12, 2020. Standardized morbidity ratios (SMR) were used to impute missing cases and adjust CFR. Age-specific attack rates and CFR were applied to different countries with differing age structures (Italy, Japan, Indonesia, and Egypt), in order to generate estimates for CFR, apparent epidemic size, and time to outbreak recognition for identical age-specific attack rates.ResultsSMR demonstrated that 50-70% of cases were likely missed during the Chinese epidemic. Adjustment for under-recognition of younger cases decreased CFR from 2.4% to 0.8% (assuming 50% case ascertainment in older individuals). Standardizing the Chinese epidemic to countries with older populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher CFR and earlier outbreak recognition. The opposite effect was demonstrated for countries with younger populations (Indonesia, and Egypt).LimitationsSecondary data analysis based on a single country at an early stage of the COVID-19 pandemic, with no attempt to incorporate second order effects (ICU saturation) on CFR.ConclusionDirect and indirect standardization are simple tools that provide key insights into between-country variation in the apparent size and severity of COVID-19 epidemics.FundingThe research was supported by a grant to DNF from the Canadian Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360).
Implementation of a Novel Remote Physician SBRT Coverage Process during the Coronavirus Pandemic
Authors: Price Alex , Henke Lauren E. , Maraghechi Borna , Kim Taeho , Spraker Matthew B. , Hugo Geoffrey D. , Robinson Clifford G. , Knutson Nels C. 
Published: April 2020

AbstractINTRODUCTIONDuring the COVID-19 pandemic, alternative methods of care are needed to reduce the relative risk of transmission in departments. Also needed is the ability to provide vital radiation oncological care if radiation oncologists (RO) are reallocated to other departments. We implemented a novel remote RO SBRT coverage practice, requiring it to be reliable, of high audio and visual quality, timely, and the same level of specialty care as our current in-person treatment coverage practice.METHODSAll observed failure modes were recorded during implementation over the first 15 sequential fractions. The time from CBCT to treatment was calculated before and after implementation to determine timeliness of remote coverage. Image quality metrics were calculated between the imaging console screen and the RO’s shared screen. Comfort levels with audio/visual communication as well as overall comfort in comparison to in-person RO coverage was evaluated using Likert scale surveys after treatment.RESULTSRemote RO SBRT coverage was successfully implemented in 14/15 fractions with 3 observed process failures that were all corrected before treatment. Average times of pre-treatment coverage before and after implementation were 8.74 and 8.51min, respectively. The cross correlation between the imaging console screen and RO’s shared screen was r=0.96 and lag was 0.05s. The average value for all survey questions was above 4.5, approaching in-person RO coverage comfort levels.CONCLUSIONSOur novel method of remote RO SBRT coverage permits reduced personnel and patient interactions surrounding RT procedures. This may help to reduce transmission of COVID-19 in our department and provides a means for SBRT coverage if ROs are reallocated to other areas of the hospital for COVID-19 support.
Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain
Authors: Monleon-Getino Antonio , Canela-Soler Jaume 
Published: April 2020

AbstractBackground and objectivesSARS-CoV-2 is a new type of coronavirus that can affect people and causes respiratory disease, COVID-19. It is affecting the entire planet and we focus in Spain, where the first case was detected at the end of January 2020 and in recent weeks it has increased in many cases. We need predictive models in order to be efficient and take actions. The general goal of this work is present a new model of SARS-CoV-2 to predict different scenarios of accumulated cases in Spain.Material and methodsIn this short report is used a model proposed previously, based on a parametric model Weibull and in a the library BDSbiost3 developed in R to infer and predict different scenarios of the evolution of SARS-CoV-2 for the accumulated cases in Spain after the spread that affects Spain detected at the end of January of this year.ResultsIn the analyses presented, projective curves have been generated for the evolution of accumulated cases in which they reach about 4,000 cases or about 15,000 cases, for which the lines of the day in which the value for 90 will be reached can be seen vertically 90, 95 and 99% of the asymptote (maximum number of cases, from that day they will begin to descend or remain the same), that is why the vertical lines would indicate the brake of the disease. For the worst-case scenario, it takes 118, 126 or 142 days to reach the maximum number of cases (n = 15,000) to reach 90, 95 and 99% of the asymptote (maximum number of cases), respectively. This means translated in a time scale that in the worst case the virus will not stop its progress, in Spain, until summer 2020, hopefully before.Comments and conclusionsThis model could be used to plan the resources and see if the policies or means dedicated to the virus are slowing the progress of the virus or it is necessary to implement others that are more effective, and can also validate a method for future outbreaks of diseases such as these.