bluuebunny/crossref_metadata_2025_split huggingface dataset:
Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation
Authors: Fiore Vincenzo G. , DeFelice Nicholas , Glicksberg Benjamin S. , Perl Ofer , Shuster Anastasia , Kulkarni Kaustubh , O’Brien Madeline , Andrea Pisauro M. , Chung Dongil , Gu Xiaosi 
Published: June 2020

AbstractWe used multi-agent simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population’s geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.
COVID 19 healthcare facility demand forecasts for rural residents
Authors: Adwibowo Andrio 
Published: June 2020

AbstractOne of the main challenges in dealing with the current COVID 19 pandemic is how to fulfill the healthcare facility demands especially for the residents living in the rural areas that have restricted healthcare access. Correspondingly, this study aims to record the daily COVID 19 cases and continue with the forecasting of the average daily demand (ADD) of healthcare facilities including beds, ICUs, and ventilators using ARIMA model. The forecasts were made for 3 rural populations located in the southern Amazon. The model shows that the healthcare ADD was different in each population. Likewise, the model forecasts that in a rural population that has the highest daily case with projected average cases equal to 67 cases/day (95%CI: 24, 110), that population has to fulfill healthcare ADD consisting of 57 beds/day (95%CI: 21, 93), 8 ICUs/day (95%CI: 2, 14), and 2 ventilators/day (95%CI: 2, 3). To conclude, the ARIMA model has addressed critical questions about ADD for beds, ICUs, and ventilators for rural residents. This ARIMA model based healthcare plan will hopefully provide versatile tool to improve healthcare resource allocations.
Opioid overdose in public libraries: Results from a five state survey
Authors: Feuerstein-Simon Rachel , Lowenstein Margaret , Dupuis Roxanne , Marti Xochitl Luna , Dolan Abby , Cannuscio Carolyn C. 
Published: June 2020

AbstractIntroductionPublic libraries are increasingly impacted by the overdose crisis. A 2017 survey of public librarians in the state of Pennsylvania revealed that 12% had reported an on-site overdose in the previous year. There are increasing public and private efforts to equip public libraries with the opioid overdose antidote, naloxone.MethodsWe conducted a cross-sectional web-based survey of all public library branches in Colorado, Connecticut, Florida, Michigan, and Virginia. Survey questions. We used descriptive statistics to report frequencies of responses and crude odds ratios were calculated to predict the dichotomized variable of endorsement of naloxone uptake.ResultsLibrary staff reported witnessing on-site alcohol (45%) and injection drug (14%) use in the past month. The one-year cumulative incidence (12% overall) of on-site overdose ranged from a low of 10% in MI, to a high of 17% in FL. Among libraries with on-site overdoses, a minority (21%) stocked naloxone, and 12% had administered naloxone. Overall, 11% of libraries stocked naloxone on-site. Although 24% of respondents reported attending at least one training regarding SUD in the past year, 91% wanted more training on the topic.ConclusionsPublic library staff routinely address issues related to substance use and overdose in their institutions. This work highlights the importance of including public libraries as part of a comprehensive public health strategy to address substance use-related morbidity and mortality in the U.S.
Predictors of disease duration and symptom course of outpatients with acute covid-19: a retrospective cohort study
Authors: O’Keefe James B. , Tong Elizabeth J. , O’Keefe Ghazala A. Datoo , Tong David C. 
Published: June 2020

ABSTRACTObjectiveDescribe the disease course in a cohort of outpatients with covid-19 and evaluate factors predicting duration of symptomsDesignRetrospective cohort studySettingTelemedicine clinic at a large medical system in Atlanta, GeorgiaParticipants273 patients with COVID-19. Exclusion criteria included: (1) intake more than 10 days after symptom onset, (2) hospitalization for covid-19, (3) symptoms at less than two visits.Main outcome measuresSymptom duration in daysResultsCommon symptoms at diagnosis are upper respiratory (64% cough, 53% loss of smell or taste, 50% sinus congestion, 22% sore throat), systemic (50% headache, 48% body aches, 36% chills, 22% dizziness, 18% fever). The most frequent remaining symptoms at 30 days were cough (7%), loss of smell or taste (5%), body aches (5%), nasal congestion (5%), shortness of breath with exertion (5%), and joint pain (5%). Day of symptom onset was earliest for upper respiratory symptoms (mean 1.26 days, 95% confidence interval 1.15 to 1.4), followed by systemic symptoms (1.54, 1.39 to 1.7), with later onset of lower respiratory (2.86, 2.54 to 3.22) and gastrointestinal symptoms (3.46, 3.07 to 3.89), when present. Cough had the longest duration when present with 12.2 days (10.9 to 13.6). Loss of smell or taste had the second longest duration with 11.0 days (9.9 to 12.2). Provider-Assessed Symptom Severity (PASS) is the best predictor of symptom duration (P <0.005 for multiple symptoms) and patients with “Moderate” PASS compared to “Mild” at their intake visit have higher rates of symptoms at 30 days, including cough (12%), nasal congestion (10%), joint pain (10%), body aches (9%), loss of taste or smell (7%), headache (7%), and shortness of breath with exertion (6%).ConclusionsCovid-19 illness in outpatients follows a pattern of progression from systemic symptoms to lower respiratory symptoms and persistent symptoms are common across categories. Provider-assessed symptom severity is the best predictor of disease duration.
Predicted COVID-19 fatality rates based on age, sex, comorbidities, and health system capacity
Authors: Ghisolfi Selene , Almås Ingvild , Sandefur Justin , von Carnap Tillmann , Heitner Jesse , Bold Tessa 
Published: June 2020

AbstractEarly reports suggest the fatality rate from COVID-19 varies greatly across countries, but non-random testing and incomplete vital registration systems render it impossible to directly estimate the infection fatality rate (IFR) in many low- and middle-income countries. To fill this gap, we estimate the adjustments required to extrapolate estimates of the IFR from high- to lower-income regions. Accounting for differences in the distribution of age, sex, and relevant comorbidities yields substantial differences in the predicted IFR across 21 world regions, ranging from 0.11% in Western Sub-Saharan Africa to 0.95% for High Income Asia Pacific. However, these predictions must be treated as lower bounds, as they are grounded in fatality rates from countries with advanced health systems. In order to adjust for health system capacity, we incorporate regional differences in the relative odds of infection fatality from childhood influenza. This adjustment greatly diminishes, but does not entirely erase, the demography-based advantage predicted in the lowest income settings, with regional estimates of the predicted COVID-19 IFR ranging from 0.43% in Western Sub-Saharan Africa to 1.74% for Eastern Europe.
A shred of evidence that BCG vaccine may protect against COVID-19: Comparing cohorts in Spain and Italy
Authors: Levine David I. 
Published: June 2020

IntroductionThere is evidence that the BCG vaccine against tuberculosis also helps prevent other diseases – perhaps including COVD-19. Spain had a program for universal BCG vaccination until 1981.ObjectiveTo see whether cohorts born when Spain had a program of universal BCG vaccination had lower rates of confirmed cases of COVID-19 and mortality (relative to similar cohorts in Italy).MethodsWe compare COVID-19 mortality and confirmed cases for those born roughly a decade before and after 1981. We compare the outcomes to the same age cohorts in Italy, which never had universal BCG vaccination.ResultsThe Spanish cohort that received BCG had a relative risk of 0.962 of having a confirmed case of COVID-19. This risk is statistically significantly below unity (95% CI 0.952 to 0.972, P< 0.001). There is also suggestive evidence the BCG cohort in Spain had lower mortality (relative risk 0.929, CI 0.850 to 1.01, P = 0.11). The small sample size makes this test underpowered.ConclusionThese suggestive results provide a shred of evidence that BCG vaccinations help protect against COVID. I outline many limitations to this study and point how better data can help be more convincing.
Non-COVID-19 Deaths After Social Distancing in Norway
Authors: Catalano Ralph , Casey Joan A. , Bruckner Tim A. , Gemmill Alison 
Published: June 2020

AbstractLay persons and policy makers have speculated on how the imposition of social distancing to reduce SARS CoV-2 (severe acute respiratory syndrome coronavirus 2) infection has affected non-COVID-19 (coronavirus disease of 2019) deaths. No rigorous estimation of the effect appears in the scholarly literature. We use time-series methods to compare non-COVID-19 deaths observed in Norway before and during the epidemic to those expected from non-COVID-19 deaths in Sweden as well as from the history of Norwegian mortality trends. We find that in the first 6 weeks after the divergence between Swedish and Norwegian policies -- the only period for which dependable data can be had – approximately 414 fewer Norwegians died than expected.
Evaluation of Knowledge, Practices, Attitude and Anxiety of Pakistan’s Nurses towards COVID-19 during the Current Outbreak in Pakistan
Authors: Alwani SS , Majeed MM , Hirwani MZ , Rauf S , Saad SM , Shah H , Hamirani F 
Published: June 2020

AbstractSince the emergence of the novel corona virus, the front line soldiers during this pandemic are the healthcare professionals because of their direct association with COVID19 patients. In the management of such patients, nurses play a significant role through proper care and preventive measures. Due to its contagious nature, fatality and no proper medicine, it is a risk to the health and life of nurses and has an impact on their psychological health.In the current study we assessed the knowledge, attitude, practices and anxiety levels of nurses who are directly involved in the management of COVID-19 patients.It was an online questionnaire based cross sectional survey targeting only those nurses involved in the management of COVID-19 patients from different hospitals of Karachi, Pakistan. SPSS 21 was used for data analysis. Descriptive analysis, Chi Square and t-tests were applied. P value < 0.05 was considered significant.Data of 78 nurses was analyzed. We observed that nurses possess good knowledge about COVID-19, its sources, symptoms and routes of transmission of the Virus etc. The knowledge mean score was calculated 14.67±3.36. Health department /Hospital and social media are the main sources of information regarding COVID-19. We investigated that 92.3% of the nurses had mild to very severe anxiety and anxiety levels are significantly higher among females (P< 0.05).We concluded that the nurses performing their duties with COVID-19 positive patients have good knowledge and attitude. But their anxiety levels are high. Psychological interventions along with training should be given.
Prevention of household transmission crucial to stop the catastrophic spread of COVID-19 in cities
Authors: Brizuela Noel Gutiérrez , Pulido Humberto Gutiérrez , Roosa Kimberlyn , Chan Néstor García , Hernández-Bello Jorge , Muñoz-Valle José Francisco , Macedo-Ojeda Gabriela , González-Estevez Guillermo , López-Chávez Javier Alonso , Villanueva-Lomelí Ricardo , Chowell Puente Gerardo 
Published: June 2020

AbstractAfter weeks under lockdown, metropolitan areas fighting the spread of COVID-19 aim to balance public health goals with social and economic standards for well-being. Mathematical models of disease transmission seeking to evaluate mitigation strategies must assess the possible impacts of social distancing, economic lockdowns and other measures. However, obscure relations between model parameters and real-world phenomena complicate such analyses. Here, we use a high-resolution metapopulation model of Guadalajara (GDL, Western Mexico) to represent daily mobility patterns driven by economic activities and their relation to epidemic growth. Given the prominence of essential activities in the city’s economy, we find that strategies aiming to mitigate the risk of out-of-home interactions are insufficient to stop the catastrophic spread of COVID-19. Using baseline reproduction numbers R0 = [2.5, 3.0] in the absence of interventions, our simulations suggest that household transmission alone can make Rt ∼ 1, and is estimated to drive 70 ±15% of current epidemic growth. This sets an upper bound for the impact of mobility-based interventions, which are unlikely to lower Rt below 1.3 and must be complemented with aggressive campaigns for early case detection and isolation. As laboratory testing and health services become insufficient to meet demand in GDL and most other cities, we propose that cities facilitate guidelines and equipment to help people curb spreading within their own homes. Postponing these actions will increase their economic cost and decrease their potential returns.Author summaryPublic health strategies to mitigate the spread of COVID-19 in metropolitan areas have focused on preventing transmission in schools, work sites and other public spaces. Here, we use a demographically- and spatially-explicit model of Guadalajara (GDL, Western Mexico) to represent economic lockdowns and their impact on disease spread. Our findings suggest that viral exposure within households accounts for 70±15% of the epidemic’s current growth rate. This highlights the importance of early case detection and isolation as necessary measures to prevent the spread of COVID-19 between strangers and close contacts alike.
Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients – a retrospective study
Authors: Kamaleswaran Rishikesan , Sadan Ofer , Kandiah Prem , Li Qiao , Blum James M. , Coopersmith Craig M. , Buchman Timothy G. 
Published: June 2020

AbstractObjectiveTo measure heart rate variability metrics in critically ill COVID-19 patients with comparison to all-cause critically ill sepsis patients.Design and patientsRetrospective analysis of COVID-19 patients admitted to an ICU for at least 24h at any of Emory Healthcare ICUs between March and April 2020. The comparison group was a cohort of all-cause sepsis patients prior to COVID-19 pandemic.Interventionsnone.MeasurementsContinuous waveforms were captured from the patient monitor. The EKG was then analyzed for each patient over a 300 second (s) observational window, that was shifted by 30s in each iteration from admission till discharge. A total of 23 HRV metrics were extracted in each iteration. We use the Kruskal–Wallis and Steel–Dwass tests (p < 0.05) for statistical analysis and interpretations of HRV multiple measures.ResultsA total of 141 critically-ill COVID-19 patients met inclusion criteria, who were compared to 208 patients with all-cause sepsis. Demographic parameters were similar apart from a high proportion of African-Americans in the COVID-19 cohort. Three non-linear markers, including SD1:SD2, sample entropy, approximate entropy and four linear features mode of Beat-to-Beat interval (NN), Acceleration Capacity (AC), Deceleration Capacity (DC), and pNN50, were statistical significance between more than one binary combinations of the sub-groups (comparing survivors and non-survivors in both the COVID-19 and sepsis cohorts). The three nonlinear features and AC, DC, and NN (mode) were statistically significant across all four combinations. Temporal analysis of the main markers showed low variability across the 5 days of analysis, compared with sepsis patients.ConclusionsHeart rate variability is broadly implicated across patients infected with SARSCoV-2, and admitted to the ICU for critical illness. Comparing these metrics to patients with all-cause sepsis suggests a unique set of expressions that differentiate this viral phenotype. This finding could be investigated further as a potential biomarker to predict poor outcome in this patient population, and could also be a starting point to measure potential autonomic dysfunction in COVID-19.
Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics
Authors: Adiga Aniruddha , Wang Lijing , Sadilek Adam , Tendulkar Ashish , Venkatramanan Srinivasan , Vullikanti Anil , Aggarwal Gaurav , Talekar Alok , Ben Xue , Chen Jiangzhuo , Lewis Bryan , Swarup Samarth , Tambe Milind , Marathe Madhav 
Published: June 2020

AbstractThis work quantifies mobility changes observed during the different phases of the pandemic world-wide at multiple resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in flows. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility.Taking two very different countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the effect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible Reffvalues and mobility flows combined with daily prevalence data and census data to obtain an estimate of new cases that might arrive on a college campus. We find that maintaining social distancing at existing levels would be effective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in Reff) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.
Multi-model evaluation of phenology prediction for wheat in Australia
Authors: Wallach Daniel , Palosuo Taru , Thorburn Peter , Hochman Zvi , Andrianasolo Fety , Asseng Senthold , Basso Bruno , Buis Samuel , Crout Neil , Dumont Benjamin , Ferrise Roberto , Gaiser Thomas , Gayler Sebastian , Hiremath Santosh , Hoek Steven , Horan Heidi , Hoogenboom Gerrit , Huang Mingxia , Jabloun Mohamed , Jansson Per-Erik , Jing Qi , Justes Eric , Kersebaum Kurt Christian , Launay Marie , Lewan Elisabet , Luo Qunying , Maestrini Bernardo , Moriondo Marco , Padovan Gloria , Olesen Jørgen Eivind , Poyda Arne , Priesack Eckart , Pullens Johannes Wilhelmus Maria , Qian Budong , Schütze Niels , Shelia Vakhtang , Souissi Amir , Specka Xenia , Srivastava Amit Kumar , Stella Tommaso , Streck Thilo , Trombi Giacomo , Wallor Evelyn , Wang Jing , Weber Tobias K.D. , Weihermüller Lutz , de Wit Allard , Wöhling Thomas , Xiao Liujun , Zhao Chuang , Zhu Yan , Seidel Sabine J. 
Published: June 2020

AbstractPredicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
Partial rescue of neuronal genes deregulated in Cornelia de Lange Syndrome by cohesin
Authors: Weiss Felix D. , Calderon Lesly , Wang Yi-Fang , Georgieva Radina , Guo Ya , Cvetesic Nevena , Kaur Maninder , Dharmalingam Gopuraja , Krantz Ian D. , Lenhard Boris , Fisher Amanda G. , Merkenschlager Matthias 
Published: June 2020

AbstractCornelia de Lange Syndrome (CdLS) is a human developmental disorder caused by mutations that compromise the function of cohesin, a major regulator of 3D genome organization. Cognitive impairment is a universal and as yet unexplained feature of CdLS. We characterized the transcriptional profile of cortical neurons from CdLS patients and found deregulation of hundreds of genes enriched for neuronal functions related to synaptic transmission, signalling processes, learning and behaviour. Inducible proteolytic cleavage of cohesin disrupted 3-D genome organization and transcriptional control in post-mitotic cortical mouse neurons. The genes affected belonged to similar gene ontology classes and showed significant numerical overlap with those deregulated in CdLS. Interestingly, gene expression was largely rescued by subsequent reconstitution of cohesin function. These experiments show that cohesin is continuously required for neuronal gene expression and provide a tractable approach for addressing mechanisms of neuronal dysfunction in CdLS.