bluuebunny/crossref_metadata_2025 huggingface dataset:
Using viral load and epidemic dynamics to optimize pooled testing in resource constrained settings
Authors: Cleary Brian , Hay James A. , Blumenstiel Brendan , Harden Maegan , Cipicchio Michelle , Bezney Jon , Simonton Brooke , Hong David , Senghore Madikay , Sesay Abdul K. , Gabriel Stacey , Regev Aviv , Mina Michael J.
Published: May 2020

Extensive virological testing is central to SARS-CoV-2 containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combine a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence, and to ratify losses in sensitivity against the time course of individual infections. Using this framework, we show that prevalence can be accurately estimated across four orders of magnitude using only a few dozen pooled tests without the need for individual identification. We then exhaustively evaluate the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many positives compared to individual testing with a given budget. We illustrate how pooling affects sensitivity and overall detection capacity during an epidemic and on each day post infection, finding that sensitivity loss is mainly attributed to individuals sampled at the end of infection when detection for public health containment has minimal benefit. Crucially, we confirm that our theoretical results can be accurately translated into practice using pooled human nasopharyngeal specimens. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect epidemiologically relevant infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings.
Objective extraction of movement features prompting expert identification of dystonia in ambulatory children with cerebral palsy
Authors: Aravamuthan Bhooma R. , Ueda Keisuke , Miao Hanyang , Gilbert Laura , Smith Sarah , Pearson Toni
Published: May 2020

Aim

To determine the specific movement features in children with cerebral palsy (CP) that prompt expert identification of dystonia

Methods

Dystonia identification in CP, particularly when co-morbid with spasticity, can be difficult. For this retrospective case-control study, we conducted a qualitative thematic analysis of consensus-building discussions between three pediatric movement disorders physicians as they attempted to identify the presence or absence of dystonia in gait videos of 40 subjects with spastic CP and periventricular leukomalacia.

Results

Initial unanimous consensus regarding the presence or absence of dystonia was achieved in 12 videos (30%). Following consensus building discussion, 22 additional videos (55%) yielded unanimous consensus. Two main themes were generated: 1) Unilateral leg or foot adduction that was variable over time, and 2) Difficulty in identifying dystonia. Codes contributing to Theme 1 were more likely to appear when a discussant was favoring the presence of dystonia in a video (Chi-square, p=0.004).

Discussion

These results provide specific movement features that could aid dystonia diagnosis in ambulatory children with CP. However, these results also suggest that, even amongst putative motor phenotyping experts, visual dystonia diagnosis in CP remains difficult, highlighting the need for developing and using objective dystonia diagnosis measures.

What this paper adds

-

Dystonia identification is visually difficult, even for experts

-

Unilateral lower extremity variable adduction could represent gait dystonia in cerebral palsy

-

Qualitative thematic analysis objectively identifies expert-cited dystonia features in cerebral palsy
Causal associations between salt intake with body mass, shape and composition: A two-sample Mendelian randomization study
Authors: Feng Qi , Yuan Shuai , Yang Qian , Lu Yingchang , Loos Ruth J.F. , Li Gloria H.Y. , Fei Yue , Tsoi Man Fung , Cheung Ching Lung , Cheung Bernard M.Y.
Published: May 2020

Background

Observational studies have found associations between salt intake with obesity, body shape and composition; but the findings may be biased by residual confounding.

Objective

To analyze the causal associations between salt intake and obesity measures in both sex-combined and sex-specific models.

Designs

This was a two-sample Mendelian randomization study. Genome-wide association (GWA) studies of urinary sodium secretion (UNa, a surrogate for salt intake), body mass index (BMI), BMI-adjusted waist-to-hip ratio (WHR), body fat (BF) percentage and estimated glomerular filtration rate (eGFR) were identified. We initially extracted fifty SNPs associated with UNa at GWA significance level of 5 × 10−8, but further removed those SNPs with potential horizontal pleiotropy. Univariable MR and multivariable MR with adjustment for eGFR were performed. Inverse-variance weighted MR was performed as the primary analysis, with MR-Egger methods as sensitivity analysis. The potential bidirectional association between BMI and UNa was investigated. All exposure and outcomes were continuous, and the effect measure was linear regression coefficients (beta) and their 95% confidence intervals (95%CI).

Results

UNa was causally associated with increased BMI in both men (eGFR-adjusted beta 0.443 (0.163 to 0.724)) and women (0.594 (0.333 to 0.855)). UNa caused BF percentage increase in men (0.622 (0.268 to 0.976)) and women (0.334 (0.007 to 0.662)). UNa significantly elevated BMI-adjusted WHR in men (0.321 (0.094 to 0.548)), but not in women (0.170 (−0.052 to 0.391)). Additionally, we found that BMI causally increased UNa (0.043 (0.023 to 0.063)).

Conclusions

Salt intake increased BMI and BF percentage. Salt intake affects male body shape by increasing BMI-adjusted WHR, but showed no effects on female body shape. The bidirectional association between BMI and UNa suggested that salt reduction measures and weight reduction measures should be implemented simultaneously to break the vicious cycle and gain more health benefits.

Abbreviations

ALM

: appendicular lean mass

BF

: body fat

BMI

: body mass index

eGFR

: estimated glomerular filtration rate

GWA

: genome-wide association

HC

: hip circumference

MR

: Mendelian randomization

PRESSO test

: Pleiotropy RESidual Sum and Outlier test

SNP

: single nucleotide polymorphism

UNa

: urinary sodium secretion

WC

: waist circumference

WHR

: waist-to-hip ratio

WLM

: whole body lean mass
Robust estimation of diagnostic rate and real incidence of COVID-19 for European policymakers
Authors: Català M. , Pino D. , Marchena M. , Palacios P. , Urdiales T. , Cardona P.J. , Alonso S. , López-Codina D. , Prats C. , Alvarez-Lacalle E.
Published: May 2020

Policymakers need a clear and fast assessment of the real spread of the epidemic of COVID-19 in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths immediately indicate that countries like Italy and Spain have the worst situation as of mid April 2020, on its own, reported cases do not provide a correct picture of the situation. The reason is that different countries diagnose diversely and present very distinctive reported case fatality rate (CFR). The same levels of reported incidence and mortality might hide a very different underlying picture. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain an uniform unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empiric. The key assumption of the method is that the real CFR in Europe of COVID-19 is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor the way total deaths are reported. The estimation protocol has a dynamic nature, and it has been giving converging numbers for diagnostic rates in all European countries as of mid April 2020. From this diagnostic rate, policy makers can obtain an Effective Potential Growth (EPG) updated everyday providing an unbiased assessment of the countries with more potential to have an uncontrolled situation. The method developed will be used to track possible improvements on the diagnostic rate in European countries as the epidemic evolves.
The use of facemasks by the general population to prevent transmission of Covid 19 infection: A systematic review
Authors: Gupta Madhu , Gupta Khushi , Gupta Sarika
Published: May 2020

Background

The pandemic of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a serious worldwide public health emergency. This systematic review aims to summarize the available evidence regarding the role of face mask in community settings in slowing the spread of respiratory viruses such as SARS-CoV-2.

Methods

The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review. Literature search using PUBMED, Google Scholar and Cochrane database was performed using Medical subject heading (MeSH) words from the year 2000-2020. The articles focused on the use of masks and N95 respirators in healthcare workers were excluded.

Results

A total of 305 records were identified, out of which 14 articles were included in the review based upon quality and eligibility criteria. All the articles mentioned about role of face masks in preventing the spread of respiratory viruses like influenza, SARS and SARS-CoV-2, in the community or experimental setting. Studies also suggested that early initiation of face mask usage was more effective. Masks were also reported to be more effective in viruses which transmit easily from asymptomatic individuals, as is now known in SARS-CoV-2.

Conclusion

Theoretical, experimental and clinical evidence suggested that usage of face masks in general population offered significant benefit in preventing the spread of respiratory viruses especially in the pandemic situation, but it’s utility is limited by inconsistent adherence to mask usage.
Early phases of COVID-19 are characterized by a reduction of lymphocyte populations and the presence of atypical monocytes
Authors: Lombardi Andrea , Trombetta Elena , Cattaneo Alessandra , Castelli Valeria , Palomba Emanuele , Tirone Mario , Mangioni Davide , Lamorte Giuseppe , Manunta Maria , Prati Daniele , Ceriotti Ferruccio , Gualtierotti Roberta , Costantino Giorgio , Aliberti Stefano , Scaravilli Vittorio , Grasselli Giacomo , Gori Andrea , Porretti Laura , Bandera Alessandra ,   for the COVID-19 Network
Published: May 2020

Background

Severe acute respiratory syndrome coronavirus 2 is a recently discovered pathogen responsible of coronavirus disease 2019 (COVID-19). The immunological changes associated with this infection are largely unknown.

Methods

We evaluated the peripheral blood mononuclear cells profile of 63 patients with COVID-19 at diagnosis and the presence of association with inflammatory biomarkers and 28-days mortality.

Results

Lymphocytopenia was present in 51 of 63 (80.9%) patients. This reduction was mirrored also on CD8+ lymphocytes (128 cells/μL), natural killer cells (67 cells/μL) and natural killer T cells (31 cells/μL). Monocytes were preserved in total number but displayed a subpopulation composed mainly of cells with a reduced expression of both CD14 and HLA-DR. A direct correlation was found between serum values of IL-6 and the frequency of Th2 lymphocytes (R=0.17; p=0.04) but not with the monocytes count (R=0.01; p=0.60). Patients who died in the 28 days from admission (N=10, 15.9%), when compared to those who did not, displayed lower mean values of CD3+ (p=0.028) and CD4+ cells (p=0.042) and higher mean percentages of CD8+/CD38+/HLA-DR+ lymphocytes (p=0.026).

Conclusions

The early phases of COVID-19 are characterized by lymphocytopenia, predominance of Th2 lymphocytes and less immunocompetent monocytes, which include atypical mononuclear cells.

eTOC

-At diagnosis patients with COVID-19 have lymphocytopenia

-Monocytes with both normal or altered scatter properties display a reduced expression of CD14 and HLA-DR in most of COVID-19 patients

-Patients who die in the 28 days from admission have lower values of CD3+ and CD4+ cells and higher percentages of activated CTL cells compared to those who survive
Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images
Authors: de Moura Joaquim , Novo Jorge , Ortega Marcos
Published: May 2020

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease.

In this work, we propose complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To face these classification tasks, we exploited and adapted to this topic a densely convolutional network architecture, which connects each layer to every other layer in a feed-forward fashion. To validate the designed approaches, several representative experiments were performed using images retrieved from different public chest X-ray images datasets. overall, satisfactory results were obtained from the designed experiments, facilitating the doctors’ work and allowing better an early diagnostic/screening and treatment of this relevant pandemic pathology.
Monitoring of circulating monocyte HLA-DR expression in a large cohort of intensive care patients: relation with outcome and secondary infections
Authors: de Roquetaillade C. , Dupuis C. , Faivre V. , Lukaszewicz AC. , Brumpt C. , Payen D.
Published: May 2020

Background

post-injury acquired immunodepression (AID) is frequently assessed by the diminished expression of Human Leukocyte Antigen-D Related on circulating monocytes (mHLA-DR). The relation with mortality and the occurrence of ICU-acquired infections (IAI) requires confirmation in large cohorts of patients. This study tested in a large number of ICU patients from a single center the association of mHLA-DR with mortality and secondary infections.

Methods

This prospective, observational study in a surgical ICU of a French tertiary hospital reports mHLA-DR measurements (fixed flow cytometry protocol) done 1st within the 3 days post-admission and 2nd after the 7th day. The other collected parameters were: the SAPS II and SOFA scores, sex, age, comorbidity, mortality and ICU-acquired infections. The associations between mHLA-DR and outcomes were tested by adjusted Fine and Gray sub-distribution competing risk models.

Findings

1053 patients were subdivided into 4 subgroups depending on the main motif for admission. Overall, 151 patients (14.3%) died in the ICU with an independent association with the amplitude of the first mHLA-DR decrease (HR = 0.71 [0.57; 0.95], p < 0.01); 592 patients had a 2nd mHLA-DR measurement of whom 223 patients (37.7%) complicated by IAI. These patients had a lower mHLA-DR than other patients (mHLA-DR = 9.0 log vs. 9.3 log, p < 0.01). IAI occurrence was independently associated with first, the 2nd mHLA-DR level regardless the initial severity (HR = 0.66 [0.51; 0.84], p = 0.001) and second, with the slope between the 1st and 2nd values (HR = 0.62 [0.43; 0.89], p = 0.009).

Interpretation

the association between the early mHLA-DR expression and ICU mortality does not improve the prediction given by the severity scores. The persistence or a decrease of low mHLA-DR expression are independent and reliable predictors of ICU-acquired infection.

Funding

no financial interest
Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks
Authors: Kennedy-Shaffer Lee , Lipsitch Marc
Published: May 2020

Randomized controlled trials are crucial for the evaluation of interventions such as vaccinations, but the design and analysis of these studies during infectious disease outbreaks is complicated by statistical, ethical, and logistical factors. Attempts to resolve these complexities have led to the proposal of a variety of trial designs, including individual randomization and several types of cluster randomization designs: parallel-arm, ring vaccination, and stepped wedge designs. Because of the strong time trends present in infectious disease incidence, however, methods generally used to analyze stepped wedge trials may not perform well in these settings. Using simulated outbreaks, we evaluate various designs and analysis methods, including recently proposed methods for analyzing stepped wedge trials, to determine the statistical properties of these methods. While new methods for analyzing stepped wedge trials can provide some improvement over previous methods, we find that they still lag behind parallel-arm cluster-randomized trials and individually-randomized trials in achieving adequate power to detect intervention effects. We also find that these methods are highly sensitive to the weighting of effect estimates across time periods. Despite the value of new methods, stepped wedge trials still have statistical disadvantages compared to other trial designs in epidemic settings.
Prediction of Spreads of COVID-19 in India from Current Trend
Authors: Shekhar Himanshu
Published: May 2020

The 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

Background

Obesity has been identified as an emerging health concern for Chinese American children; however, very little is known about diets in Asian American children.

Objective

To 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.

Design

Data 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/Setting

Children 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 Measures

Nutrient intake and HEI-2010 scores.

Statistical Analyses Performed

Chi-squared and t-test comparisons, with statistical significance set at α=0.05.

Results

Adjusted 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.

Conclusions

Sodium 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 Snapshot

Research Question

What are typical dietary intakes and current community and family-based challenges related to healthy eating in Chinese American children – an understudied disparity group?

Key Findings

Chinese 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

Background & Aims

We examined transient elastography assessed hepatic steatosis and fibrosis distributions and relationships with body composition in a representative United States population sample.

Methods

Liver 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).

Results

Compared 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.

Conclusion

In 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

COVID-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

Background

Gram-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.

Methods

We 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.

Results

In 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).

Conclusions

The 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 registry

NCT00440167
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

Diagnostic 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

The 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

Objectives

The 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.

Methods

We 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.

Results

Estimated 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.

Conclusions

Interventions 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

Objectives

Health 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.

Methods

A 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.

Results

As 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).

Conclusions

For 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

Staying 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 Statement

Americans 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

Public 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

Background

The 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.

Objective

To 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.

Methods

We 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.

Results

CURVE 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.

Conclusions

CURVE provides a powerful, interactive interface that provides locally relevant, dynamic, timely information to guide health system decision making and pandemic preparedness.
The reproduction number of COVID-19 and its correlation with public health interventions
Authors: Linka Kevin , Peirlinck Mathias , Kuhl Ellen
Published: May 2020

Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22±1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67±0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24±2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.
Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks
Authors: Asif Sohaib , Wenhui Yi , Jin Hou , Tao Yi , Jinhai Si
Published: May 2020

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.
Neural network pattern recognition of ultrasound image gray scale intensity histogram of breast lesions to differentiate between benign and malignant lesions
Authors: Ramachandran Arivan , Shiva Balan KR , Kiran Swathi , Azharudeen Mohamed
Published: May 2020

The aim of this study is to analyze the effectiveness of grayscale intensity histogram to differentiate benign and malignant lesions using a convolutional neural network. Data (200 USG images, 100-malignant, 100-benign) was downloaded from an online access repository. The images were despeckled using ImageJ software and the grayscale intensity histogram values were extracted. In-built neural network pattern recognition application in Matlab R2019b was used to classify the images, which is a two-layer feed-forward network, with sigmoid hidden and softmax output neurons. The positive predictive value of the CNN was 95%. The best performance of 0.078264 was achieved at 36 epochs in the validation set. This study suggests that the grayscale intensity histogram of a USG image is an easy and feasible method to identify malignant lesions through an artificial neural network.
Impact of non-pharmaceutical interventions against COVID-19 in Europe: A quasi-experimental study
Authors: Hunter Paul R , Colón-González Felipe J , Brainard Julii , Rushton Steven
Published: May 2020

The current epidemic of COVID-19 is unparalleled in recent history as are the social distancing interventions that have led to a significant halt on the economic and social life of so many countries. However, there is very little empirical evidence about which social distancing measures have the most impact. We report a quasi-experimental study of the impact of various interventions for control of the outbreak. Data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models (GAMM) and in Stata using multi-level mixed effects regression models. From both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay at home orders and closure of all non-businesses was not associated with any independent additional impact. Our results could help inform strategies for coming out of lockdown.
Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer
Authors: Chen Robert
Published: May 2020

While machine learning has shown promise in prediction of mortality in situations such as intensive care units, there is limited evidence of its application towards ovarian cancer.

In this study, we extracted clinical covariates from a cohort of 273 patients with stage I and II ovarian cancer, and trained a machine learning algorithm, L2-regularized logistic regression, on the set of patients in prediction problem for mortality less than 20 months, representing the 25th percentile of overall survival.

Our model achieves an AUC of 0.621, accuracy 0.761, sensitivity 0.130, positive predictive value 0.659, and F1 score 0.216. This study serves as a proof of concept for a predictive model customized towards mortality prediction for malignant neoplasm of the left testis, and can be adapted and generalized to related tumors such as spermatic cord and scrotal tumor types.