bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of Spreads of COVID-19 in India from Current Trend
Authors: Shekhar Himanshu 
Published: May 2020

AbstractThe article describe modelling efforts for evaluating the current level of COVID-19 infections in India, using exponential model. The Data from 15 march 2020 to 30 April 2020 are used for validating the model, where intrinsic rise rate is kept constant. It is observed that some states of India, like MAharastra, Gujarat and Delhi have a much higher daily infection cases. This is modelled by assuming an initial higher infections, keeping rise rate same. The sudden outbursts are captured using offset of values for these three states. Data from other states like Madhya Pradesh, Uttar Pradesh and Rajasthan are also analysed and they are found to be following the same constants as India is following. Worldwide, many attempts are made to predict outburst of COVID-19 and in the model, described in this paper, turning point is not predicted, as cases in India are still rising. The developed model is based on daily confirmed infections and not on cumulative infections and rationalization is carried out for the population of various regions, while predicting infections for various states. Assigning a decay constant at this stage will be a premature exercise and keeping that in mind, exponential model predicts that India will attain 1 lakh case by 15 May 2020. The figure of 2 lakh and 3 lakh will be attained on 22 May 2020 and 26 May 2020, respectively.
Dietary disparities of urban immigrant schoolchildren in New York City: Results from a mixed-methods pilot study
Authors: Yi Stella S. , Edens Neile K. , Lederer Ashley , Pan Janet , Chong Stella K. , Wong Jennifer A. , Li Yan , Beasley Jeannette , Trinh-Shevrin Chau , Kwon Simona C. 
Published: May 2020

AbstractBackgroundObesity has been identified as an emerging health concern for Chinese American children; however, very little is known about diets in Asian American children.ObjectiveTo assess the feasibility of assessing diet of urban Chinese American children in an applied (school) setting and to gain insight on diet and drivers of dietary intakes from community nutrition experts.DesignData were from the Food Journal Project 2017, a school-based pilot study conducted by a multi-sector collaboration, and qualitative data from nutrition and community experts.Participants/SettingChildren aged 8-12 (n=83) completed two dietary assessments using a food diary from January-June 2017. Children were then interviewed using the food diary as a guide; dietary data were entered into the ASA24 system by study staff. Chinese American children were compared to non-Chinese peers with respect to nutrient intake and the Healthy Eating Index 2010 (HEI-2010). Six semi-structured interviews and one panel discussion including two registered dietitians and community leaders with working knowledge of the Chinese American community were conducted from January-June 2018.Main Outcome MeasuresNutrient intake and HEI-2010 scores.Statistical Analyses PerformedChi-squared and t-test comparisons, with statistical significance set at α=0.05.ResultsAdjusted for caloric intake, Chinese American children consumed 20% more sodium, 21% more protein but 27% less sugar compared to non-Chinese children. With regards to the HEI-2010, Chinese American children had less favorable whole grains and sodium scores; and more favorable seafood protein and empty calories scores compared to non-Chinese children. Qualitative data underscored the current burden of diet-related health disparities among Chinese American children and suggested Chinese American receptivity to family-based nutritional and cooking education interventions.ConclusionsSodium reduction and increasing whole grain intakes may be warranted in Chinese American children but should be verified with additional studies. Interventions to improve nutrition in this understudied population are critical.Research SnapshotResearch QuestionWhat are typical dietary intakes and current community and family-based challenges related to healthy eating in Chinese American children – an understudied disparity group?Key FindingsChinese American schoolchildren have high sodium and low whole grain intakes compared to non-Chinese peers. These specific dietary disparities work in concert with low physical activity levels and cultural norms in contributing to diet-related health disparities in this group. Family-based nutrition education and potential focus of dietetic counseling on sodium and whole grain intake areas are indicated to address these disparities.
Transient elastography assessed hepatic steatosis and fibrosis are associated with body composition in the United States
Authors: Unalp-Arida Aynur , Ruhl Constance E. 
Published: May 2020

ABSTRACTBackground & AimsWe examined transient elastography assessed hepatic steatosis and fibrosis distributions and relationships with body composition in a representative United States population sample.MethodsLiver stiffness and controlled attenuation parameter (CAP) were assessed on 4,870 non-Hispanic white, non-Hispanic black, non-Hispanic Asian, and Hispanic men and women aged 20 years and over in the National Health and Nutrition Examination Survey (NHANES) 2017-2018. Participants underwent anthropometry and dual-energy x-ray absorptiometry (DXA).ResultsCompared to women, men had higher mean CAP (274.2 dB/m vs. 254.4 dB/m) and liver stiffness (6.4 kPa vs. 5.5 kPa). CAP and liver stiffness increased with age and BMI. In multivariate-adjusted analysis, CAP in the upper quartile was associated with increased age, BMI, waist-to-hip ratio, ALT and C-reactive protein (p<0.001 for each). After adjustment, non-Hispanic blacks had lower CAP and non-Hispanic Asians had over twice the odds of higher CAP. In multivariate-adjusted analysis, liver stiffness in the upper quartile was associated with male sex, increased age, BMI, GGT, and CAP (p<0.001 for each), and hepatitis C virus positivity. Lower stiffness among Non-Hispanic Asians was not significant after adjustment for BMI. DXA trunk and extremity fat mass were positively related to both CAP and liver stiffness with adjustment for sex, race-ethnicity, and age (p<0.001 for each). Results were similar with CAP and liver stiffness as continuous characteristics.ConclusionIn the U.S. population, increased anthropometric and DXA body composition measures were associated with higher CAP and liver stiffness. Racial-ethnic differences observed merit continuation of NHANES transient elastography to further elucidate the burden of obesity and liver health disparities.
Buying time: an ecological survival analysis of COVID-19 spread based on the gravity model
Authors: Vigdorovits Alon 
Published: May 2020

SummaryCOVID-19 has spread in a matter of months to most countries in the world. Various social and economic factors determine the time in which a pandemic reaches a country. This time is essential, because it allows countries to prepare their response. This study considered a gravity model that expressed time to first case as a function of multiple socio-economic factors. First, Kaplan-Meier analysis was performed for each variable in the model by dividing countries into two groups according to the median of the respective variable. In order to measure the effect of these variables, parameters of the gravity model were estimated using accelerated failure time (AFT) survival analysis. In the Kaplan-Meier analysis the differences between high and low value groups were significant for every variable except population. The AFT analysis determined that increased personal freedom had the largest effect on lowering the survival time, controlling for detection capacity. Higher GDP per capita and a larger population also reduced survival time, while a greater distance from the outbreak source increased it. Understanding the influence of factors affecting time to index case can help us understand disease spread in the early stages of a pandemic.
Bacterial lipopolysaccharide as negative predictor of gemcitabine efficacy in advanced pancreatic cancer – translational results from the AIO-PK0104 phase 3 study
Authors: Guenther Michael , Haas Michael , Heinemann Volker , Kruger Stephan , Westphalen Christoph Benedikt , von Bergwelt-Baildon Michael , Mayerle Julia , Werner Jens , Kirchner Thomas , Boeck Stefan , Ormanns Steffen 
Published: May 2020

ABSTRACTBackgroundGram-negative bacteria mediated gemcitabine resistance in pre-clinical models. We determined if intratumoral lipopolysaccharide (LPS) detection by immunohistochemistry is associated with outcome in advanced pancreatic ductal adenocarcinoma (PDAC) treated with gemcitabine and non-gemcitabine containing 1st-line chemotherapy.MethodsWe examined LPS on tumor tissue from 130 patients treated within the randomized phase 3 trial AIO-PK0104 and a validation cohort (n=113) from a prospective biomarker study and analyzed the association of LPS detection to patient outcome according to treatment subgroups.ResultsIn 24% of samples from the AIO-PK0104 study LPS was detected; in LPS-positive patients median OS was 4.4 months, compared to 7.3 months with LPS negative tumors (HR 1.732, p=0.010). A difference in OS was detected in the subgroup of patients treated with 1st-line gemcitabine-based treatment (n=71; HR 2.377, p=0.002), whereas no difference in OS was observed in the non-gemcitabine subgroup (n=59; HR 1.275, p=0.478). Within the validation cohort, the LPS positivity rate was 23%, and LPS detection was correlated with impaired OS in the gemcitabine subgroup (n=94; HR 1.993, p=0.008) whereas no difference in OS was observed in the non-gemcitabine subgroup (n=19; HR 2.596, p=0.219).ConclusionsThe detection of intratumoral LPS as a surrogate marker for gram-negative bacterial colonization may serve as a negative predictor for gemcitabine efficacy in advanced PDAC.Clinical trial registryNCT00440167
Identification of IgG antibody response to SARS-CoV-2 spike protein and its receptor binding domain does not predict rapid recovery from COVID-19
Authors: McAndrews Kathleen M. , Dowlatshahi Dara P. , Hensel Janine , Ostrosky-Zeichner Luis L. , Papanna Ramesh , LeBleu Valerie S. , Kalluri Raghu 
Published: May 2020

AbstractDiagnostic testing and evaluation of patient immunity against the novel severe acute respiratory syndrome (SARS) corona virus that emerged last year (SARS-CoV-2) are essential for health and economic crisis recovery of the world. It is suggested that potential acquired immunity against SARS-CoV-2 from prior exposure may be determined by detecting the presence of circulating IgG antibodies against viral antigens, such as the spike glycoprotein and its receptor binding domain (RBD). Testing our asymptomatic population for evidence of COVID-19 immunity would also offer valuable epidemiologic data to aid health care policies and health care management. Currently, there are over 100 antibody tests that are being used around the world without approval from the FDA or similar regulatory bodies, and they are mostly for rapid and qualitative assessment, with different degrees of error rates. ELISA-based testing for sensitive and rigorous quantitative assessment of SARS-CoV-2 antibodies can potentially offer mechanistic insights into the COVID-19 disease and aid communities uniquely challenged by limited financial resources and access to commercial testing products. Employing recombinant SARS-CoV-2 RBD and spike protein generated in the laboratory, we devised a quantitative ELISA for the detection of circulating serum antibodies. Serum from twenty SARS-CoV-2 RT-PCR confirmed COVID-19 hospitalized patients were used to detect circulating IgG titers against SARS-CoV-2 spike protein and RBD. Quantitative detection of IgG antibodies to the spike glycoprotein or the RBD in patient samples was not always associated with faster recovery, compared to patients with borderline antibody response to the RBD. One patient who did not develop antibodies to the RBD completely recovered from COVID-19. In surveying 99 healthy donor samples (procured between 2017-February 2020), we detected RBD antibodies in one donor from February 2020 collection with three others exhibiting antibodies to the spike protein but not the RBD. Collectively, our study suggests that more rigorous and quantitative analysis, employing large scale samples sets, is required to determine whether antibodies to SARS-CoV-2 spike protein or RBD is associated with protection from COVID-19 disease. It is also conceivable that humoral response to SARS-CoV-2 spike protein or RBD works in association with adaptive T cell response to determine clinical sequela and severity of COVID-19 disease.
Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain
Authors: Abdollahi Amir , Rahbaralam Maryam 
Published: May 2020

AbstractThe novel coronavirus (COVID-19) has already spread to almost every country in the world and has infected over 3 million people. To understand the transmission mechanism of this highly contagious virus, it is necessary to study the potential factors, including meteorological conditions. Here, we present a machine learning approach to study the effect of temperature, humidity and wind speed on the number of infected people in the three most populous autonomous communities in Spain. We find that there is a moderate inverse correlation between temperature and the daily number of infections. This correlation manifests for temperatures recorded up to 6 days before the onset, which corresponds well to the known mean incubation period of COVID-19. We also show that the correlation for humidity and wind speed is not significant.
Now casting and Forecasting of COVID-19 outbreak in the National Capital Region of Delhi
Authors: Nagappa Bharathnag , Sakthivel Manikandanesan , Marimuthu Yamini , Rastogi Aayushi , Ramalingam Archana , Sarin Shiv Kumar 
Published: May 2020

AbstractObjectivesThe study aimed to estimate the disease burden due to COVID-19 in the scenarios of unchecked spread and with various public health interventions in New Delhi.MethodsWe adopted Susceptible, Exposed, Infected and Recovered (SEIR) model to estimate the course of COVID-19 outbreak in Delhi population and effect of public health intervention on the pandemic. We first estimated the basic reproductive rate (R0) based on the evidence from Wuhan, then ran the model considering no intervention implemented, followed by case isolation, social distancing, and lockdown, each implemented in isolation and in combinations to estimate the number of cases. Markov’s model was used to estimate the number of cases in various clinical scenarios of the disease. Sensitivity analysis conducted to estimate the effect of asymptomatic cases on case based interventions.ResultsEstimated R0 in Delhi population was 6.18 (range 4.15 – 12.2). Effective reproductive rate (Rt) was least for case isolation (3.5). Lockdown showed highest reduction (28%) in number of prevalent cases on peak day and 22% reduction in patients in need of intensive care unit (ICU). Case isolation and lockdown together resulted in 50% reduction in number of prevalent cases and 42% reduction in patients in need of ICU care. Sensitivity analysis showed that the effect of case isolation was inversely proportionate to the proportion of asymptomatic (hidden) cases.ConclusionsInterventions should be implemented in combinations of individual and community level interventions to gain better outcome. Identifying and isolation of all cases as early as possible is important to flatten the pandemic curve.
Health service inequalities during the COVID-19 pandemic among elderly people living in large urban and non-urban areas in Florida, US
Authors: Yu Xinhua 
Published: May 2020

AbstractObjectivesHealth inequalities were often exacerbated during the emerging epidemic. This study examined urban and non-urban inequalities in health services among COVID-19 patients aged 65 or above in US Florida from March 2 to May 27, 2020.MethodsA retrospective time series analysis was conducted using individual patient records. Multivariable Poisson and logistic models were used to calculate adjusted incidence of COVID-19 and the associated rates of emergency department (ED) visits, hospitalizations and deaths.ResultsAs of May 27, 2020, there were 13,659 elderly COVID-19 patients (people aged 65 or above) in Florida and 14.9% of them died. Elderly people living in small metropolitan areas might be less likely to be confirmed with COVID-19 infection than those living in large metropolitan areas. The ED visit and hospitalization rates decreased significantly across metropolitan statuses for both men and women. Those patients living in small metropolitan or rural areas were less likely to be hospitalized than those living in large metropolitan areas (35% and 34% versus 41%). Elderly women aged 75 or above living in rural areas had 113% higher adjusted incidence of COVID-19 than those living in large metropolitan areas, and the rates of hospitalizations were lower compared with those counterparts living in large metropolitan areas (29% versus 46%; OR: 0.37 [0.25-0.54]; p <0.001).ConclusionsFor elderly people living in US Florida, those who living in small metropolitan or rural areas were less likely to receive adequate health care than those who living in large or medium metropolitan areas during the COVID-19 pandemic.
Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic
Authors: Yan Youpei , Malik Amyn A. , Bayham Jude , Fenichel Eli P. , Couzens Chandra , Omer Saad B. 
Published: May 2020

AbstractStaying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing, and some mandated staying home. Understanding how these policies interact with individuals’ voluntary responses to the COVID-19 epidemic is critical for estimating the transmission dynamics of the pathogen and assessing the impact of policies. We use the variation in policy responses along with smart device data, which measures the amount of time Americans stayed home, to show that there was substantial voluntary avoidance behavior. We disentangle the extent to which observed shifts in behavior are induced by policy and find evidence of a non-trivial voluntary response to local reported COVID-19 cases and deaths, such that around 45 cases in a home county is associated with the same amount of time at home as a stay-at-home order. People responded to the risk of contracting COVID-19 and to policy orders, though the response to policy orders crowds out or displaces a large share of the voluntary response, suggesting that, during early stages of the U.S. outbreak, better compliance with social distancing recommendations could have been achieved with policy crafted to complement voluntary behavior.Significance StatementAmericans are spending substantially more time at home to reduce the spread of COVID-19. This behavioral shift is a mix of voluntary disease avoidance and policy-induced behavioral changes. Both need to be accounted for. Disentangling voluntary from policy-induced behavioral changes is critical for governments relaxing or renewing restrictions. A substantial share of the behavioral response appears to be voluntary, but this behavior was offset by strong stay-at-home orders. Local testing and rapid reporting is a first step to making better use of voluntary behavioral changes.
Benchmarking the CoVID-19 pandemic across countries and states in the U.S.A. under heterogeneous testing
Authors: Asahi Kenzo , Undurraga Eduardo A. , Wagner Rodrigo 
Published: May 2020

AbstractPublic health officials need to make urgent decisions to reduce the potential impact of the CoVID-19 pandemic. Benchmarking based on the increase in total cases or case fatality rates is one way of comparing performance across countries or territories (such as states in the USA), and could inform policy decisions about COVID-19 mitigation strategies. But comparing cases and fatality across territories is challenging due to heterogeneity in testing and health systems. We show two complementary ways of benchmarking across countries or US states. First, we used multivariate regressions to estimate the test-elasticity-of-COVID-19-case-incidence. We found a 10% increase in testing yielded ~9% (95% CI:4.2–3.4%; p<0.001) increase in reported cases across countries, and ~2% (95%CI:0.1-3.4%; p=0.03) increase across US states during the week ending April 10th, 2020. We found comparable negative elasticities for fatality rates (across countries: β =-0.77, 95%CI:-1.40– -0.14; p=0.02; US states: β=-0.15, 95%CI:-0.30-0.01; p=0.06). Our results were robust to various model specifications. Second, we decomposed the growth in cases into test growth and positive test ratio (PTR) growth to intuitively visualize the components of case growth. We hope these results can help support evidence-based decisions by public health officials as more consistent data hopefully becomes available.
COVID-19 Utilization and Resource Visualization Engine (CURVE) to Forecast In-Hospital Resources
Authors: Chou Shih-Hsiung , Kearns James T , Turk Philip , Kowalkowski Marc A. , Roberge Jason , Priem Jennifer S. , Taylor Yhenneko J. , Burns Ryan , Palmer Pooja , McWilliams Andrew D. 
Published: May 2020

AbstractBackgroundThe emergence of COVID-19 has created an urgent threat to public health worldwide. With rapidly evolving demands on healthcare resources, it is imperative that healthcare systems have the ability to access real-time local data to predict, plan, and effectively manage resources.ObjectiveTo develop an interactive COVID-19 Utilization and Resource Visualization Engine (CURVE) as a data visualization tool to inform decision making and guide a large health system’s proactive pandemic response.MethodsWe designed and implemented CURVE using R Shiny to display real-time parameters of healthcare utilization at Atrium Health with projections based upon locally derived models for the COVID-19 pandemic. We used the CURVE app to compare predictions from two of our models –one created before and one after the statewide stay-at-home and social distancing orders (denoted before- and after-SAH-order model). We established parameter settings for best-, moderate-, and worst-case scenarios for pandemic spread and resource use, leveraging two locally developed forecasting models to determine peak date trajectory, resource use, and root mean square error (RMSE) between observed and predicted results.ResultsCURVE predicts and monitors utilization of hospital beds, ICU beds, and number of ventilators in the context of up-to-date local resources and provides Atrium Health leadership with timely, actionable insights to guide decision-making during the COVID-19 pandemic. The after-SAH-order model demonstrated the lowest RMSE in total bed, ICU bed, and patients on ventilators.ConclusionsCURVE provides a powerful, interactive interface that provides locally relevant, dynamic, timely information to guide health system decision making and pandemic preparedness.