bluuebunny/crossref_metadata_2025 huggingface dataset:
Are there causal relationships between ADHD and BMI? Evidence from multiple genetically informed designs
Authors: Liu Chao-Yu , Schoeler Tabea , Davies Neil M , Peyre Hugo , Lim Kai-Xiang , Barker Edward D , Llewellyn Clare , Dudbridge Frank , Pingault Jean-Baptiste
Published: April 2020

Background

Attention-deficit/hyperactivity disorder (ADHD) and Body Mass Index (BMI) are associated. However, it remains unclear whether this association reflects causal relationships in either direction, or confounding. Here, we implemented genetically informed methods to examine bidirectional causality and potential confounding.

Methods

Three genetically informed methods were employed: (1) cross-lagged twin-differences analysis to assess bidirectional effects of ADHD symptoms and BMI at ages 8, 12, 14 and 16 years in 2,386 pairs of monozygotic twins from the Twins Early Development Study (TEDS), (2) within- and between-family ADHD and BMI polygenic score (PS) analysis in 3,320 pairs of dizygotic TEDS twins and (3) two-sample bidirectional Mendelian randomization (MR) using summary statistics from Genome-Wide Association Studies (GWAS) on ADHD (N=55,374) and BMI (N=806,834).

Results

Mixed results were obtained across the three methods. Twin-difference analyses provided little support for cross-lagged associations between ADHD symptoms and BMI over time. PS analyses were consistent with bidirectional relationships between ADHD and BMI with plausible time-varying effects from childhood to adolescence. MR findings were also consistent with bidirectional causal effects between ADHD and BMI. Multivariable MR suggested the presence of substantial confounding in bidirectional relationships.

Conclusions

The three methods converged to highlight multiple sources of confounding in the association between ADHD and BMI. PS and MR analyses suggested plausible causal relationships in both directions. Possible explanations for mixed causal findings across methods are discussed.

Key messages

Within-family polygenic score and Mendelian randomization analyses were consistent with bidirectional causal effects between ADHD and BMI.

Findings from different genetically informed methods suggested that multiple sources of confounding are at play, including genetic and shared environmental confounding, population stratification, assortative mating and dynastic effects.

The ADHD polygenic score increasingly associated with BMI phenotype from childhood to adolescence, suggesting an increasing role of ADHD in the aetiology of BMI across the development. Conversely, BMI polygenic score association with ADHD phenotypes tended to decrease across the development.

Addressing mixed evidence will require increased sample sizes to implement novel methods such as within-family MR.
Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool
Authors: Das Ashis Kumar , Mishra Shiba , Mishra Devi Kalyan , Gopalan Saji Saraswathy
Published: April 2020

Background

Acute myeloid leukemia (AML) accounts for a fifth of childhood leukemia. Although survival rates for AML have greatly improved over the past few decades, they vary depending on demographic and AML type factors.

Objectives

To predict the five-year survival among pediatric AML patients using machine learning algorithms and deploy the best performing algorithm as an online survival prediction tool.

Materials and methods

Pediatric patients (0 to 14 years) with a microscopically confirmed AML were extracted from the Surveillance Epidemiology and End Results (SEER) database (2000-2011) and randomly split into training and test datasets (80/20 ratio). Four machine learning algorithms (logistic regression, support vector machine, gradient boosting, and K nearest neighbor) were trained on features to predict five-year survival. Performances of the algorithms were compared, and the best performing algorithm was deployed as an online prediction tool.

Results

A total of 1,477 patients met our inclusion criteria. The gradient boosting algorithm was the best performer in terms of discrimination and predictive ability. It was deployed as the online survival prediction tool named OSPAM-C (https://ashis-das.shinyapps.io/ospam/).

Conclusions

Our study provides a framework for the development and deployment of an online survival prediction tool for pediatric patients with AML. While external validation is needed, our survival prediction tool presents an opportunity to reach informed clinical decision-making for AML patients.
Modeling projections for COVID-19 pandemic by combining epidemiological, statistical, and neural network approaches
Authors: Uhlig Steffen , Nichani Kapil , Uhlig Carsten , Simon Kirsten
Published: April 2020

As the number of people affected by COVID-19 disease caused by the novel coronavirus SARS-CoV-2 ebbs and flows in different national and sub-national regions across the world, it is evident that our lifestyle and socio-economic trajectories will have to be adapted and adjusted to the changing scenarios. Novel forecasting tools and frameworks provide an arguable advantage to facilitate this adapting and adjusting process, by promoting efficient resource management at individual and institutional levels. Based on deterministic compartment models we propose an empirical top-down modeling approach to provide epidemic forecasts and risk calculations for (local) outbreaks. We use neural networks to develop leading indicators based on available data for different regions. These indicators are not only used to assess the risk of a (new) outbreak or to determine the effectiveness of a measure at an early stage, but also in parametric models to determine an effective forecast, along with the associated uncertainty. Based on initial results, we show the performance of such an approach and its robustness against inherent disturbances in epidemiological surveillance data. We foresee such a statistical framework to drive web-based automatic platforms to democratize the dissemination of prognosis results.
Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features
Authors: Yang Zhiqi , Lin Daiying , Chen Xiaofeng , Qiu Jinming , Li Shengkai , Huang Ruibin , Sun Hongfu , Liao Yuting , Xiao Jianning , Tang Yanyan , Liu Guorui , Wu Renhua , Chen Xiangguang , Dai Zhuozhi
Published: April 2020

Purpose

To identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage, and to identify the most valuable features in the differential diagnosis.

Materials and Method

A consecutive cohort of 73 COVID-19 and 48 influenza pneumonia patients were retrospectively recruited from five independent institutions. The courses of both diseases were confirmed to be in the early stages (2.66 ± 2.62 days for COVID-19 and 2.19 ± 2.10 days for influenza pneumonia after onset). The chi-square test, student’s t-test, and Kruskal-Wallis H-test were performed to compare CT imaging and clinical features between the two groups. Spearman or Kendall correlation tests between feature metrics and diagnosis outcomes were also assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. The corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were reported.

Results

The ground-glass opacification (GGO) was the most common imaging feature in COVID-19, including pure-GGO (75.3%) and mixed-GGO (78.1%), mainly in peripheral distribution. For clinical features, most COVID-19 patients presented normal white blood cell (WBC) count (89.04%) and neutrophil count (84.93%). Twenty imaging features and 6 clinical features were identified to be significantly different between the two diseases. The diagnosis outcomes correlated significantly with the WBC count (r=-0.526, P<0.001) and neutrophil count (r=-0.500, P<0.001). Four CT imaging features had absolute correlations coefficients higher than 0.300 (P<0.001), including crazy-paving pattern, mixed-GGO in peripheral area, pleural effusions, and consolidation.

Conclusions

Among a total of 1537 lesions and 62 imaging and clinical features, 26 features were demonstrated to be significantly different between COVID-19 and influenza pneumonia. The crazy-paving pattern was recognized as the most powerful imaging feature for the differential diagnosis in the early stage, while WBC count yielded the highest diagnostic efficacy in clinical manifestations.
COVID-19 diagnosis and study of serum SARS-CoV-2 specific IgA, IgM and IgG by chemiluminescence immunoanalysis
Authors: Ma Huan , Zeng Weihong , He Hongliang , Zhao Dan , Yang Yunru , Jiang Dehua , Qi Peigen Yingjie , He Weihuang , Zhao Changcheng , Yi Ruting , Wang Xiaofang , Wang Bo , Yang Yuanhong Yun , Kombe Kombe Arnaud John , Ding Chengchao , Xie Jiajia , Gao Yong , Cheng Linzhao , Li Yajuan , Ma Xiaoling , Jin Tengchuan
Published: April 2020

Background

The pandemic of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing great loss. Detecting viral RNAs is standard approach for SARS-CoV-2 diagnosis with variable success. Currently, studies describing the serological diagnostic methods are emerging, while most of them just involve the detection of SARS-CoV-2-specific IgM and IgG by ELISA or “flow immunoassay” with limited accuracy.

Methods

Diagnostic approach depends on chemiluminescence immunoanalysis (CLIA) for detecting IgA, IgM and IgG specific to SARS-CoV-2 nucleocapsid protein (NP) and receptor-binding domain (RBD) was developed. The approach was tested with 216 sera from 87 COVID-19 patients and 483 sera from SARS-CoV-2 negative individuals. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. Concentration kinetics of RBD-specific serum antibodies were characterized. The relationship of serum RBD-specific antibodies and disease severity was analyzed.

Results

The diagnostic accuracy based on RBD outperformed those based on NP. Adding IgA to a conventional serological test containing IgM and IgG improves sensitivity of SARS-CoV-2 diagnosis at early stage. CLIA for detecting RBD-specific IgA, IgM and IgG showed diagnostic sensitivities of 98.6%, 96.8% and 96.8%, and specificities of 98.1%, 92.3% and 99.8%, respectively. Median concentration of IgA and IgM peaked during 16-20 days after illness onset at 8.84 μg/mL and 7.25 μg/mL, respectively, while IgG peaked during 21-25 days after illness onset at 16.47 μg/mL. Furthermore, the serum IgA level positively correlates with COVID-19 severity.

Conclusion

CLIA for detecting SARS-CoV-2 RBD-specific IgA, IgM and IgG in blood provides additional values for diagnosing and monitoring of COVID-19.

Chemiluminescence immunoanalysis of SARS-CoV-2 RBD-specific serum IgA as well as IgM and IgG improves accuracy of COVID-19 diagnosis. Concentration kinetics of serum RBD-specific IgA, IgM and IgG are revealed. Serum IgA levels positively correlate with COVID-19 severity.
Classification of Parkinson’s disease and essential tremor based on gait and balance characteristics from wearable motion sensors: A data-driven approach
Authors: Moon Sanghee , Song Hyun-Je , Sharma Vibhash D. , Lyons Kelly E. , Pahwa Rajesh , Akinwuntan Abiodun E. , Devos Hannes
Published: April 2020

Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether gait and balance variables obtained with wearable sensors can be utilized to differentiate between PD and ET using machine learning techniques. Additionally, we compared classification performances of several machine learning models. A balance and gait data set collected from 567 people with PD or ET was investigated. Performance of several machine learning techniques including neural networks (NN), support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosting (GB), were compared using F1-scores. Machine learning models classified PD and ET based on balance and gait characteristics better than chance or logistic regression. The highest F1-score was 0.61 of NN, followed by 0.59 of GB, 0.56 of RF, 0.55 of SVM, 0.53 of DT, and 0.49 of kNN. The results demonstrated the utility of machine learning models to classify different movement disorders. Further study will provide a more accurate clinical tool to help clinical decision-making.
Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources
Authors: Eastman Brydon , Meaney Cameron , Przedborski Michelle , Kohandel Mohammad
Published: April 2020

The outbreak of SARS-CoV-2 in China has spread around the world, infecting millions and causing governments to implement strict policies to counteract the spread of the disease. One of the most effective strategies in reducing the severity of the pandemic is social distancing, where members of the population systematically reduce their interactions with others to limit the transmission rate of the virus. However, the implementation of social distancing can be difficult and costly, making it imperative that both policy makers and the citizenry understand the potential benefits if done correctly and the risks if not. In this work, a mathematical model is developed to study the effects of social distancing on the spread of the SARS-CoV-2 virus in Canada. The model is based upon a standard epidemiological SEIRD model that has been stratified to directly incorporate the proportion of individuals who are following social distancing protocols. The model parameters characterizing the disease are estimated from current epidemiological data on COVID-19 using machine learning techniques. The results of the model show that social distancing policies in Canada have already saved thousands of lives and that the prolonged adherence to social distancing guidelines could save thousands more. Importantly, our model indicates that social distancing can significantly delay the onset of infection peaks, allowing more time for the production of a vaccine or additional medical resources. Furthermore, our results stress the importance of easing social distancing restrictions gradually, rather than all at once, in order to prevent a second wave of infections. Model results are compared to the current capacity of the Canadian healthcare system by examining the current and future number of ventilators available for use, emphasizing the need for the increased production of additional medical resources.
Statistical and network-based analysis of Italian COVID-19 data: communities detection and temporal evolution
Authors: Milano Marianna , Cannataro Mario
Published: April 2020

Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across China and many other countries. Italy is one of the European countries most affected by the COVID-19 disease, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period February 24 to March 29, 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices (reported in Supplementary file). Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behaviour. Then, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behaviour. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e. how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.
Coronametrics: The UK turns the corner
Authors: Goliński Adam , Spencer Peter
Published: April 2020

*

There are many ways of analyzing the progress of an epidemic, but when it comes to short term forecasting, it is very hard to beat a simple time series regression model. These are good at allowing for the noise in day to day observations, extracting the trend and projecting it forward.

*

Our regression models are designed to exploit this, using the daily statistics released by PHE and NHSE. These strongly suggest that the tide has turned and that taking one day with the next, the national figures for deaths from this virus will now fall back noticeably, easing the pressure on the NHS and its staff.

*

There is still a huge range of uncertainty associated with any forecast. The model is currently predicting a total of 113,000 admissions to UK hospitals by the end of April and that 19,000 people will die from the virus in English hospitals by then. There is a 1 in 20 chance that the mortality figures could flatten out more quickly, with around 1,000 more deaths occurring by the end of April. However, there is the same risk that this figure continues to mount, rising to a total of 24,000 by the end of the month. On current trends, the number of deaths in the UK is likely to be 10% higher than the number in England.

*

Longer term, the impact of the virus will depend critically upon the likely relaxation of the current government strategy of suppression.
Quantitative Trait Loci on Chromosome 21 have Pleiotropic Effects on %FEV1 and Allergen Polysensitization; asthma related traits in the EGEA study
Authors: Ulgen Ayse , Amos Christopher
Published: April 2020

To investigate whether the 21q21 region may contain a quantitative trait locus (QTL) with pleiotropic effect on % predicted FEV1 (forced expiratory volume in 1 second) and SPTQ (number of positive skin test responses to 11 allergens), in 295 EGEA families ascertained through asthmatic probands, we conducted a bivariate linkage analysis using two approaches:

(1) a bivariate variance components (VC) analysis and (2) A combined principal components (CPC) analysis, with 13 microsatellites covering the whole chromosome 21. To identify the genetic variants associated with these traits, we performed family-based association analysis (FBAT) for the second principal component (PC2) using two microsatellites and 27 SNPs belonging to three candidate genes, located in the vicinity of the linkage peak. Univariate linkage analyses showed suggestive evidence of linkage to %FEV1 and SPTQ at two positions. Bivariate VC linkage analysis of these phenotypes led to an increase in linkage signals as compared to univariate analysis at the same positions. The peaks obtained by the CPC led to similar results as the full bivariate VC approach; evidence for linkage mainly coming from PC2. The strongest association signal, using single marker analysis for PC2, was obtained with D21S1252 (p=0.003 for global test; p=0.004 for allele 2 and p=0.001 for allele 11) and rs2834213 of IFNGR2 (p=0.003), these two loci being 3 Mb apart. Multi-marker analysis further strengthened this finding. These results indicate that at least two genetic factors may be involved in SPTQ and %FEV1 variability but further genotyping is needed to better understand these findings.
Self-Burnout – A New Path to the End of COVID-19
Authors: Shayak B , Rand Richard H
Published: April 2020

In this work we use mathematical modeling to describe a possible route to the end of COVID-19, which does not feature either vaccination or herd immunity. We call this route self-burnout. We consider a region with (a) no influx of corona cases from the outside, (b) extensive social distancing, though not necessarily a full lockdown, and (c) high testing capacity relative to the actual number of new cases per day. These conditions can make it possible for the region to initiate the endgame phase of epidemic management, wherein the disease is slowly made to burn itself out through a combination of social distancing, sanitization, contact tracing and preventive testing. The dynamics of the case trajectories in this regime are governed by a single-variable first order linear delay differential equation, whose stability criterion can be obtained analytically. Basis this criterion, we conclude that the social mobility restrictions should be such as to ensure that on the average, one person interacts closely (from the transmission viewpoint) with at most one other person over a 4-5 day period. If the endgame can be played out for a long enough time, we claim that the Coronavirus can eventually get completely contained without affecting a significant fraction of the region’s population. We present estimates of the duration for which the epidemic is expected to last, finding an interval of approximately 5-15 weeks after the self-burnout phase is initiated. South Korea, Austria, Australia, New Zealand and the states of Goa, Kerala and Odisha in India appear to be well on the way towards containing COVID by this method.
Effects of Screening for Colorectal Cancer: Development and Validation of a Multistate Markov Model
Authors: Heisser Thomas , Hoffmeister Michael , Brenner Hermann
Published: April 2020

Simulation models are a powerful tool to overcome gaps of evidence needed to inform medical decision making. Here, we present development and application of a multistate Markov model to simulate effects of colorectal cancer (CRC) screening, along with a thorough assessment of the model’s ability to reproduce real-life outcomes. Firstly, we provide a comprehensive documentation of the model development, structure and assumptions. Secondly, to assess the model’s external validity, we compared model-derived cumulative incidence and prevalences of colorectal neoplasms to (1) results from KolosSal, a study in German screening colonoscopy participants, (2) registry-based estimates of CRC incidence in Germany, and (3) outcome patterns of randomized sigmoidoscopy screening studies. We found that (1) more than 90% of model-predicted neoplasm prevalences were within the 95% confidence intervals of the observed prevalences in the KolosSal study; (2) the 15-year cumulative CRC incidences estimated by simulations for the German population deviated by 0.0-0.2 percent units in men and 0.0-0.3 percent units in women when compared to corresponding registry-derived estimates; and (3) the time course of cumulative CRC incidence and mortality in the modelled intervention group and control group closely resembles the time course reported from sigmoidoscopy screening trials. Summarized, our model adequately predicted colorectal neoplasm prevalences and incidences in a German population for up to 25 years, with estimated patterns of the effect of screening colonoscopy resembling those seen in registry data and real-world studies. This suggests that the model represents a valid tool to assess the comparative effectiveness of strategies for CRC screening.
A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions
Authors: Aadhityaa M. , Kasiviswanathan K. S. , Ilampooranan Idhayachandhiran , Soundharajan B. , Balamurugan M. , He Jianxun
Published: April 2020

The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.
Persistent viral shedding of SARS-CoV-2 in faeces - a rapid review
Authors: Gupta S , Parker J , Smits S , Underwood J , Dolwani S
Published: April 2020

Aim

In addition to respiratory symptoms, COVID-19 can present with gastrointestinal complaints suggesting possible faeco-oral transmission. The primary aim of this review was to establish the incidence and timing of positive faecal samples for SARS-CoV-2 in patients with COVID-19.

Methods

A systematic literature review identified studies describing COVID-19 patients tested for faecal virus. Search terms for Medline included ‘clinical’, ‘faeces’, ‘gastrointestinal secretions’, ‘stool’, ‘COVID-19’, ‘SARS-CoV-2’ and ‘2019-nCoV’. Additional searches were done in AJG, Gastroenterology, Gut, Lancet Gastroenterology and Hepatology, The WHO Database, CEBM, NEJM, social media and the NICE, bioRxiv and medRxiv preprints. Data were extracted concerning the type of test, number and timing of positive samples, incidence of positive faecal tests after negative nasopharyngeal swabs and evidence of viable faecal virus or faeco-oral transmission of the virus.

Results

There were 26 relevant articles identified. Combining study results demonstrated that 53·9% of those tested for faecal RNA were positive. Duration of faecal viral shedding ranged from 1 to 33 days after a negative nasopharyngeal swab with one result remaining positive 47 days after onset of symptoms. There is insufficient evidence to suggest that COVID-19 is transmitted via faecally shed virus.

Conclusion

There is a high rate of positive PCR tests with persistence of SARS-CoV-2 in faecal samples of patients with COVID-19. Further research is needed to confirm if this virus is viable and the degree of transmission through the faeco-oral route. This may have important implications on isolation, recommended precautions and protective equipment for interventional procedures involving the gastrointestinal tract.
Routine Early Antibiotic use in SymptOmatic preterm Neonates (REASON): a prospective randomized controlled trial
Authors: Ruoss J. Lauren , Bazacliu Catalina , Russell Jordan T. , de la Cruz Diomel , Li Nan , Gurka Matthew J. , Filipp Stephanie L. , Polin Richard A. , Triplett Eric W. , Neu Josef
Published: April 2020

Objective

We aim to assess the feasibility of a pragmatic randomized trial of antibiotics vs. no antibiotics in symptomatic premature infants after birth. Most premature infants are exposed to antibiotics after birth without evidence of benefit or harm for this practice. No study to date has attempted to randomize premature infants to antibiotics vs no antibiotics after birth.

Study Design

Infants <33 weeks’ gestation admitted to the University of Florida Neonatal Intensive Care Unit were assigned to one of three groups after birth: (A) high risk – antibiotics indicated (B) low risk – antibiotics not indicated (C) eligible for un-blinded randomization (no antibiotics vs antibiotics). The primary outcome is a composite of serious adverse events including (necrotizing enterocolitis, late onset sepsis, bronchopulmonary dysplasia, and death). Odds ratios (and 95% CI’s) were calculated to compare adverse event rates between the two randomized groups.

Results

186 subjects were enrolled (98 infants and 88 mothers) were enrolled over a 2-year period. 56% of infants (n=55) were randomized; 48% of infants randomized to the no antibiotics arm were switched and received antibiotics within the first 48 hours after birth. Serious adverse events were not significantly different between the randomization arms.

Conclusion

This is the first prospective randomized trial of antibiotics vs no antibiotics after birth in symptomatic premature infants. The results of this trial establish a framework of feasibility for a larger multicentered trial that is needed to evaluate the risks and benefits of routine antibiotic exposure in premature infants.
Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in U.S. Counties: The Significant Effects of Black Race and Temperature
Authors: Li Adam Y. , Hannah Theodore C , Durbin John , Dreher Nickolas , McAuley Fiona M , Marayati Naoum Fares , Spiera Zachary , Ali Muhammad , Gometz Alex , Kostman JT , Choudhri Tanvir F.
Published: April 2020

Objectives

Coronavirus disease-19 (COVID-19) has spread rapidly around the world, and many risk factors including patient demographics, social determinants of health, environmental variables, underlying health conditions, and adherence to social distancing have been hypothesized to affect case and death rates. However, little has been done to account for the potential confounding effects of these factors. Using a large multivariate analysis, this study illuminates modulators of COVID-19 incidence and mortality in U.S. counties while controlling for risk factors across multiple domains.

Methods

Data on COVID-19 and various risk factors in all U.S. counties was collected from publicly available data sources through April 14, 2020. Counties with at least 50 COVID-19 cases were included in case analyses and those with at least 10 deaths were included in mortality models. The 661 counties meeting inclusion criteria for number of cases were grouped into quartiles and comparisons of risk factors were made using t-tests between the highest and lowest quartiles. Similar comparisons for 217 counties were made for above average and below average deaths/100,000. Adjusted linear and logistic regression analyses were performed to evaluate the independent effects of factors that significantly impacted cases and deaths.

Results

Univariate analyses demonstrated numerous significant differences between cohorts for both cases and deaths. Risk factors associated with increased cases and/or deaths per 100,000 included increased GDP per capita, decreased social distancing, increased age, increased percent Black, decreased percent Hispanic, decreased percent Asian, decreased health, increased poverty, increased diabetes, increased coronary heart disease, increased physical inactivity, increased alcohol consumption, increased tobacco use, and decreased access to primary care. Multivariate regression analyses demonstrated Black race is a risk factor for worse COVID-19 outcome independent of comorbidities, poverty, access to health care, and other mitigating factors. Lower daily temperatures was also an independent risk factor in case load but not deaths.

Conclusions

U.S. counties with a higher proportion of Black residents are associated with increased COVID-19 cases and deaths. However, the various suggested mechanisms, such as socioeconomic and healthcare predispositions, did not appear to drive the effect of race in our model. Counties with higher average daily temperatures are also associated with decreased COVID-19 cases but not deaths. Several theories are posited to explain these findings, including prevalence of vitamin D deficiency. Additional studies are needed to further understand these effects.
Analysis and Estimation of Length of In-Hospital Stay Using Demographic Data of COVID-19 Recovered Patients in Singapore
Authors: Sreevalsan-Nair Jaya , Vangimalla Reddy Rani , Ghogale Pritesh Rajesh
Published: April 2020

Singapore is one of the countries which has taken early, systematic, and rigorous nationwide responses to slowing the COVID-19 contagion down. By February 4, 2020, the government of Singapore restricted the mobility of the vulnerable age-groups. In the current study, we study the influence of the age-based vulnerability of the population with respect to COVID-19 conditions on the recovery of COVID-19 patients. We study 245 patients in Singapore recovered and discharged during January 23–April 01. We first study the descriptive statistics of the length of in-hospital stay (LOS) of the COVID-19 patients based on demographic variables, namely age, and gender. Then, we determine the distribution of LOS, using local and generalized linear regression models. We take the approach of periodization based on critical changes in the disease transmission model. Even though the overall recovery rate has reduced drastically after a sudden spike in daily confirmations, our analysis shows that there is a considerable shift in the COVID-19 confirmations to the population in the non-vulnerable age-groups. We show that the LOS of the non-vulnerable age group is considerably lower at 9 days, as opposed to 15 or 20 days in the existing literature.
COVID-19 Outbreak Prediction with Machine Learning
Authors: Ardabili Sina F. , Mosavi Amir , Ghamisi Pedram , Ferdinand Filip , Varkonyi-Koczy Annamaria R. , Reuter Uwe , Rabczuk Timon , Atkinson Peter M.
Published: April 2020

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.
Social Distancing and Personal Protective Measures Decrease Influenza Morbidity and Mortality
Authors: Young Grant , Peng Xiaohua , Rebeza Andre , Bermejo Santos , De Chang , Sharma Lokesh , Cruz Charles S. Dela
Published: April 2020

Seasonal influenza (flu) is an underappreciated source of disease morbidity and mortality worldwide. While vaccination remains the cornerstone of influenza prevention, common measures practiced during the COVID-19 pandemic such as social distancing, the use of protective face masks, and frequent hand washing are rarely utilized during flu season. In this investigation, we examined the effect of these preventative measures in decreasing influenza burden this year. We examined three countries with major COVID-19 outbreaks i.e. China, Italy and the United States, and compared the flu activity this year to the average of the last 4 years (2015-2019). We found that this year in China and Italy, there was a significantly steeper decline of flu cases than average, which correlated with an increase in positive COVID-19 case reports in those countries. These "averted" cases can be translated into a substantial decrease in morbidity and mortality. As such, we conclude that the current COVID-19 pandemic is a reminder that behavioral measures can decrease the burden of communicable respiratory infections, and these measures should be adopted to an extent during normal influenza season.
DETECTION AND SEGMENTATION OF ENDOSCOPIC ARTEFACTS AND DISEASES USING DEEP ARCHITECTURES
Authors: Nguyen Nhan T. , Tran Dat Q. , Nguyen Dung B.
Published: April 2020

We describe in this paper our deep learning-based approach for the EndoCV2020 challenge, which aims to detect and segment either artefacts or diseases in endoscopic images. For the detection task, we propose to train and optimize EfficientDet—a state-of-the-art detector—with different EfficientNet backbones using Focal loss. By ensembling multiple detectors, we obtain a mean average precision (mAP) of 0.2524 on EDD2020 and 0.2202 on EAD2020. For the segmentation task, two different architectures are proposed: UNet with EfficientNet-B3 encoder and Feature Pyramid Network (FPN) with dilated ResNet-50 encoder. Each of them is trained with an auxiliary classification branch. Our model ensemble reports an sscore of 0.5972 on EAD2020 and 0.701 on EDD2020, which were among the top submitters of both challenges.
COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India
Authors: Pant Rashmi , Choudhury Lincoln P. , Rajesh Jammy G. , Yeldandi Vijay V.
Published: April 2020

India reported its first COVID19 case on 30 January 2020. Since then the epidemic has taken different trajectories across different geographical locations in the country. This study explores the population aggregated trajectories of COVID19 susceptible, infected and recovered or dead cases in the south Indian state of Telangana with a population of approximately 40 million. Information on cases reported from March 2 to April 4 was collated from government records. The susceptible-infected-removed (SIR) model for the spread of an infectious disease was used. Transmission parameters were extracted from existing literature that has emerged over past weeks from other regions with similar population densities as Telangana. Optimisation algorithms were used to get basic reproduction rate for different phases of nonpharmaceutical interventions rolled by the government. Peak accumulation is projected towards end of July with 36% of the population being infected by August 2020 if the population lockdown or social distancing mechanism is not continued. The number of deaths assuming no intervention is projected to be 488000 (95% CI: (329400, 646600)). A draconian enforcement of population lockdown combined with hand and face hygiene adherence would reduce the transmission by at least 99.7% whereas partial social distancing and hygiene would reduce it by 51.2%. Transmission parameters reported should be interpreted with caution as they are population aggregated and do not consider unique characteristics of susceptibility among micro-clusters and vulnerable individuals. More data will need to be collected to optimize transmission parameters and evaluate the full complexity, to simulate real world scenarios in the models.
Identity-by-descent detection across 487,409 British samples reveals fine-scale population structure, evolutionary history, and trait associations
Authors: Saada Juba Nait , Kalantzis Georgios , Shyr Derek , Robinson Martin , Gusev Alexander , Palamara Pier Francesco
Published: April 2020

Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of genomic analyses. We developed a new method, called FastSMC, that enables accurate biobank-scale detection of IBD segments transmitted by common ancestors living up to several hundreds of generations in the past. FastSMC combines a fast heuristic search for IBD segments with accurate coalescent-based likelihood calculations and enables estimating the age of common ancestors transmitting IBD regions. We applied FastSMC to 487,409 phased samples from the UK Biobank and detected the presence of ∼214 billion IBD segments transmitted by shared ancestors within the past 1,500 years. We quantified time-dependent shared ancestry within and across 120 postcodes, obtaining a fine-grained picture of genetic relatedness within the past two millennia in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the localization of a sample’s birth coordinates from genomic data. We sought evidence of recent positive selection by identifying loci with unusually strong shared ancestry within recent millennia and we detected 12 genome-wide significant signals, including 7 novel loci. We found IBD sharing to be highly predictive of the sharing of ultra-rare variants in exome sequencing samples from the UK Biobank. Focusing on loss-of-function variation discovered using exome sequencing, we devised an IBD-based association test and detected 29 associations with 7 blood-related traits, 20 of which were not detected in the exome sequencing study. These results underscore the importance of modelling distant relatedness to reveal subtle population structure, recent evolutionary history, and rare pathogenic variation.
Extracellular levels of glucose in the hippocampus and striatum during maze training for food or water reward in male rats
Authors: Scavuzzo C.J. , Newman L.A. , Gold P.E. , Korol D.L.
Published: April 2020

Peripheral and central administration of glucose potently enhance cognitive functions. The present experiments examined changes in brain extracellular glucose levels while rats were trained to solve hippocampus-sensitive place or striatum-sensitive response learning tasks for food or water reward. During the first minutes of either place or response training, extracellular glucose levels declined in both the hippocampus and striatum, an effect not seen in untrained, rewarded rats. Subsequently, glucose increased in both brain areas under all training conditions, approaching asymptotic levels ∼15-25 min into training. Compared to untrained-food controls, training with food reward resulted in significant glucose increases in the hippocampus but not striatum; striatal glucose levels exhibited large increases to food intake in both trained and untrained groups. In rats trained to find water, glucose levels increased significantly above the values seen in untrained rats in both hippocampus and striatum. In contrast to results seen with lactate measurements, the magnitude of training-related increases in hippocampus and striatum glucose levels did not differ by task under either reward condition. The decreases in glucose early in training might reflect an increase in brain glucose consumption, perhaps triggering increased brain uptake of glucose from blood, as evident in the increases in glucose later in training. Together with past findings measuring lactate levels under the same conditions, the initial decreases in glucose may also stimulate increased production of lactate from astrocytes to support neural metabolism directly and/or to act as a signal to increase blood flow and glucose uptake into the brain.

Highlights

Glucose levels in hippocampus and striatum decrease at the start of training.

Glucose levels increase in both brain areas later in training.

Glucose changes in both brain areas were similar for place and response tasks.

Glucose levels responded similarly to training for either food and water rewards.

Early decreases in glucose may trigger increased production astrocytic lactate.
Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals
Authors: Zhu Yuanmei , Yu Danwei , Han Yang , Yan Hongxia , Chong Huihui , Ren Lili , Wang Jianwei , Li Taisheng , He Yuxian
Published: April 2020

The current COVID-19 pandemic, caused by a novel coronavirus SARS-CoV-2, poses serious threats to public health and social stability, calling for urgent need for vaccines and therapeutics. SARS-CoV-2 is genetically close to SARS-CoV, thus it is important to define the between antigenic cross-reactivity and neutralization. In this study, we firstly analyzed 20 convalescent serum samples collected from SARS-CoV infected individuals during the 2003 SARS outbreak. All patient sera reacted strongly with the S1 subunit and receptor-binding domain (RBD) of SARS-CoV, cross-reacted with the S ectodomain, S1, RBD, and S2 proteins of SARS-CoV-2, and neutralized both SARS-CoV and SARS-CoV-2 S protein-driven infections. Multiple panels of antisera from mice and rabbits immunized with a full-length S and RBD immunogens of SARS-CoV were also characterized, verifying the cross-reactive neutralization against SARS-CoV-2. Interestingly, we found that a palm civet SARS-CoV-derived RBD elicited more potent cross-neutralizing responses in immunized animals than the RBD from a human SARS-CoV strain, informing a strategy to develop a universe vaccine against emerging CoVs.

Serum antibodies from SARS-CoV infected patients and immunized animals cross-neutralize SARS-CoV-2 suggests strategies for universe vaccines against emerging CoVs.
A Scalable, Easy-to-Deploy, Protocol for Cas13-Based Detection of SARS-CoV-2 Genetic Material
Authors: Rauch Jennifer N. , Valois Eric , Solley Sabrina C. , Braig Friederike , Lach Ryan S. , Audouard Morgane , Ponce-Rojas Jose Carlos , Costello Michael S. , Baxter Naomi J. , Kosik Kenneth S. , Arias Carolina , Acosta-Alvear Diego , Wilson Maxwell Z.
Published: April 2020

The COVID-19 pandemic has created massive demand for widespread, distributed tools for detecting SARS-CoV-2 genetic material. The hurdles to scalable testing include reagent and instrument accessibility, availability of highly-trained personnel, and large upfront investment. Here we showcase an orthogonal pipeline we call CREST (Cas13-based, Rugged, Equitable, Scalable Testing) that addresses some of these hurdles. Specifically, CREST pairs commonplace and reliable biochemical methods (PCR) with low-cost instrumentation, without sacrificing detection sensitivity. By taking advantage of simple fluorescence visualizers, CREST allows for a binary interpretation of results. CREST may provide a point- of-care solution to increase the distribution of COVID-19 surveillance.
Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails
Authors: Platt Daniel E. , Parida Laxmi , Zalloua Pierre
Published: April 2020

An opportunity exists in exploring epidemic modeling as a novel way to determine physiological and demic parameters for genetic association studies on a population/environmental (quasi) epidemiological study level. First, the spread of SARS-COV-2 has produced population specific lineages; second, epidemic spread model parameters are tied directly to these physiological and demic rates (e. g. incubation time, recovery time, transmission rate); and third, these parameters may serve as novel phenotypes to associate with region-specific genetic mutations as well as demic characteristics (e. g. age structure, cultural observance of personal space, crowdedness). Therefore, we sought to understand whether the parameters of epidemic models could be determined from the trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the quality and comparability of data between jurisdictions reporting their statistics necessary for the analysis of model parameters across populations. We found that, analytically, the pre-peak growth of an epidemic is limited by a subset of the model variates, and that the rate limiting variables are dominated by the expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth rate, which determines the doubling time. There are 9 parameters and 4 independent components in the eigenmode, leaving 5 undetermined parameters. Those parameters can be strikingly population dependent, and can have significant impact on estimates of hospital loads downstream. Without a sound framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates to uneven counting and reporting of relevant values. From the standpoint of phenotype parameters, this means that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models that accurately predict critical quantities such as hospitalization loads.