bluuebunny/crossref_metadata_2025_split huggingface dataset:
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Authors: Agarwal Dewang Kumar , De Soham , Shukla Ojas , Checker Archit , Mittal Apoorvi , Borah Ankita , Gupta Debayan 
Published: July 2020

AbstractBackgroundNumerous models have tried to predict the spread of COVID-19. Many involve myriad assumptions and parameters which cannot be reliably calculated under current conditions. We describe machine-learning and curve-fitting based models using fewer assumptions and readily available data.MethodsInstead of relying on highly parameterized models, we design and train multiple neural networks with data on a national and state level, from 9 COVID-19 affected countries, including Indian and US states and territories. Further, we use an array of curve-fitting techniques on government-reported numbers of COVID-19 infections and deaths, separately projecting and collating curves from multiple regions across the globe, at multiple levels of granularity, combining heavily-localized extrapolations to create accurate national predictions.FindingsWe achieve an R2 of 0·999 on average through the use of curve-fits and fine-tuned statistical learning methods on historical, global data. Using neural network implementations, we consistently predict the number of reported cases in 9 geographically- and demographically-varied countries and states with an accuracy of 99·53% for 14 days of forecast and 99·1% for 24 days of forecast.InterpretationWe have shown that curve-fitting and machine-learning methods applied on reported COVID-19 data almost perfectly reproduce the results of far more complex and data-intensive epidemiological models. Using our methods, several other parameters may be established, such as the average detection rate of COVID-19. As an example, we find that the detection rate of cases in India (even with our most lenient estimates) is 2.38% - almost a fourth of the world average of 9% [1].
The Epidemiology Workbench: a Tool for Communities to Strategize in Response to COVID-19 and other Infectious Diseases
Authors: Núñez-Corrales Santiago , Jakobsson Eric 
Published: July 2020

AbstractCOVID-19 poses a dramatic challenge to health, community life, and the economy of communities across the world. While the properties of the virus are similar from place to place, the impact has been dramatically different from place to place, due to such factors as population density, mobility, age distribution, etc. Thus, optimum testing and social distancing strategies may also be different from place to place. The Epidemiology Workbench provides access to an agent-based model in which a community’s demographic, geographic, and public health information together with a social distancing and testing strategy may be input, and a range of possible outcomes computed, to inform local authorities on coping strategies. The model is adaptable to other infectious diseases, and to other strains of coronavirus. The tool is illustrated by scenarios for the cities of Urbana and Champaign, Illinois, the home of the University of Illinois at Urbana-Champaign. Our calculations suggest that massive testing is the most effective strategy to combat the likely increase in local cases due to mass ingress of a student population carrying a higher viral load than that currently present in the community.
Renin-Angiotensin-Aldosterone-System inhibitor use in patients with COVID-19 infection and prevention of serious events: a cohort study in commercially insured patients in the US
Authors: Schneeweiss Maria C , Leonard Sandra , Weckstein Andrew , Schneeweiss Sebastian , Rassen Jeremy A 
Published: July 2020

AbstractObjectivesThere is lack of clarity regarding the role of angiotensin receptor blockers (ARB) or angiotensin converting enzyme inhibitors (ACEi) in interfering with the SARS-COV-2 binding on human cells and the resulting change in disease severity. We sought to assess the risk of hospitalization for COVID-19 and serious complications in current users of ARB or ACEi compared to users of dihydropyridine calcium channel blockers (dhpCCB).DesignCohort studySettingThe analysis used de-identified, patient level data from HealthVerity, linking longitudinal data from US medical and pharmacy claims, which contain information on inpatient or outpatient diagnoses, procedures and medication dispensing.ParticipantsWe identified patients aged 40+ and free of chronic kidney disease (CKD) who were newly diagnosed COVID-19, between March 1, 2020 and May 30, 2020, and adherent to ACEi, ARB, or dhpCCB therapy.InterventionsCurrent use of an ACEi, ARB, or dhpCCB.Main outcome measuresWe compared the 30-day risk of hospitalization for COVID-19 and serious complications.ResultsOf 24,708 patients identified, 7,571 were current users of an ARB, 8,484 of an ACEi, and 8,653 of a dhpCCB. The unadjusted 30-day risk of hospitalization for COVID-19 was 2.66% among ARB users, and 2.90% among ACEi users and 3.68% in dhpCCB users. In the PS-matched cohort, the risk of hospitalization among ARB users was 17% lower as compared to dhpCCB (RR=0.83; 0.68-1.00), and the risk among ACE users was 10% lower as compared to dhpCCB (RR=0.90; 0.76-1.07). When including patients with pre-existing CKD, the protective effect of ARB (RR= 0.74; 0.62-0.88) and ACEi (RR=0.84; 0.71-0.99) was more pronounced.ConclusionsThis cohort study showed that neither ARB nor ACEi use increase the risk of severe COVID-19 disease among those infected, and instead suggests that current use of ARB may offer a protective effect. This study found no evidence to support the discontinuation of ARB/ACEi therapy.
Sentiment Informed Timeseries Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston
Authors: Desai Prathamesh S. 
Published: July 2020

AbstractCoronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level, as opposed to the state or country level, is needed. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the latter is normalized by the number of tests. Positivity gives a better picture of the spread of this pandemic as with time more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD lacks information about the sentiment of people with respect to coronavirus. Thirdly, models that make use of social media posts might have too much noise. News sentiment, on the other hand, can capture long term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new AI model, viz., Sentiment Informed Timeseries Analyzing AI (SITALA), that has been trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for the Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76 and that for the test dataset of 22 consecutive days is 9.6. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.One Sentence SummaryAn AI model based on news sentiment and COVID-19 test positivity is developed to predict the spread of coronavirus in Houston and to guide future local public policy.
An analysis of mortality in Ontario using cremation data: Rise in cremations during the COVID-19 pandemic
Authors: Postill Gemma , Murray Regan , Wilton Andrew S. , Wells Richard A. , Sirbu Renee , Daley Mark J. , Rosella Laura C 
Published: July 2020

AbstractBackgroundThe impact of coronavirus disease 2019 (COVID-19) on mortality in Ontario is unknown. Cremations are performed for most deaths in Ontario and require coroner certification before the cremation can take place. Our objective was to provide timely analysis of deaths during the COVID-19 pandemic using cremation data.MethodsWe analyze cremation certificate data from January 1, 2017, to June 30, 2020, in Ontario. 2020 cremation records were compared to historical records from 2017-2019 by age, month, and place of death and COVID-19 status. A time series model was fit to quantify the deviation in cremation trends during the COVID-19 period.ResultsThere have been 39 760 cremations in Ontario in 2020 with the highest number of seen in April (N = 7 527 cremations) when peak COVID-19 cases were seen. Over the study period, the proportion of cremations from deaths in hospitals decreased whereas cremations from long-term care and residences increased. In April there were 1 839 more cremations compared to historical averages over 2017-2019, representing a 32% increase. Time series modelling of cremations from January 2017 demonstrated that cremations in April and May 2020 exceeded the projections based on modelled estimates.ConclusionWe demonstrate the utility of cremation data for providing timely mortality information during a public health emergency. Cremations were higher in the pandemic months compared to previous years, and there was a shift in deaths occurring in hospitals to long-term care and residences. These timely estimates of mortality are critical for understanding the impact of COVID-19.
The IeDEA Data Exchange Standard: a common data model for global HIV cohort collaboration
Authors: Duda Stephany N. , Musick Beverly S. , Davies Mary-Ann , Sohn Annette H. , Ledergerber Bruno , Wools-Kaloustian Kara , McGowan Catherine C. , Maxwell Nicola J. , Kariminia Azar , Ostinelli Cam Ha Dao , Hogan Brenna C. , Shi Qiuhu , Malateste Karen , Goodall Ruth L. , Kristensen Dennis Karsten , Hansen Erik V. , Williams Carolyn F. M. , Lewis Judith T. , Yiannoutsos Constantin T. 
Published: July 2020

AbstractObjectiveTo describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community.MethodsIn 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter.FindingsThe resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub.ConclusionsThe IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.HighlightsThe IeDEA Data Exchange Standard is a data model for HIV epidemiology research.The model has expedited 95 projects using data from >400 HIV clinics worldwide.A browsable and adaptable version and data collection templates are available online.
Mathematical model study of a pandemic: Graded lockdown approach
Authors: Chatterjee S. , Vani V.C. , Banyal Ravinder K. 
Published: July 2020

AbstractA kinetic approach is developed, in a “tutorial style” to describe the evolution of an epidemic with spread taking place through contact. The “infection - rate” is calculated from the rate at which an infected person approaches an uninfected susceptible individual, i.e. a potential recipient of the disease, up to a distance p, where the value of p may lie between pmin≤ p ≤ pmax. We consider a situation with a total population of N individuals, living in an area A, x(t) amongst them being infected while xd(t) = β′x(t) is the number that have died in the course of transmission and evolution of the epidemic. The evolution is developed under the conditions (1) a faction α(t) of the [N-x(t) – xd(t)] uninfected individuals and (2) a β(t) fraction of the x(t) infected population are quarantined, while the “source events” that spread the infection are considered to occur with frequency υ0. The processes of contact and transmission are considered to be Markovian. Transmission is assumed to be inhibited by several processes like the use of “masks”, “hand washing or use of sanitizers” while “physical distancing” is described by p. The evolution equation for x(t) is a Riccati - type differential equation whose coefficients are time-dependent quantities, being determined by an interplay between the above parameters. A formal solution for x(t) is presented, for a “graded lockdown” with the parameters, 0≤ α(t), β(t)≤1 reaching their respective saturation values in time scales, τ1, τ2 respectively, from their initial values α(0)=β(0)=0. The growth is predicted for several BBMP wards in Bengaluru and in urban centers in Chikkaballapur district, as an illustrative case. Above selections serve as model cases for high, moderate and thin population densities. It is seen that the evolution of [x(t)/N] with time depends upon (a) the initial time scale of evolution, (b) the time scale of cure and (c) on the time dependence of the Lockdown function Q(t) = {[1-α(t)]·[1-β(t)]}. The formulae are amenable to simple computations and show that in order to curb the spread one must ensure that Q(∞) must be below a critical value and the vigilance has to be continued for a long time (at least 100 to 150 days) after the decay starts, to avoid all chances of the infection reappearing.
Remdesivir for COVID-19: match-population analysis with compassionate use of Remdesivir for severe COVID-19
Authors: Vasylyeva Olga , Chen Tara , Hanna John 
Published: July 2020

AbstractWe retrospectively compared outcomes between patients who received compassionate care Remdesivir and whose who met criteria for Remdesivir, but did not received it due to period of unavailability. We observed comparable mortality rate and significantly higher mortality rate among patients with CrCl < 30 ml per minute.
Evaluating Scenarios for School Reopening under COVID19
Authors: Baxter Arden , Oruc Buse Eylul , Keskinocak Pinar , Asplund John , Serban Nicoleta 
Published: July 2020

AbstractThousands of school systems have been struggling with the decisions about how to safely and effectively deliver education during the fall semester of 2020, amid the COVID19 pandemic. The objective of this study is to evaluate the public health impact of reopening schools on the spread of COVID19. An agent-based simulation model was adapted and used to project the number of infections and deaths under multiple school reopening dates and scenarios, including different cohorts receiving in-person instruction on alternating days, only younger children returning to in-person instruction, regular schedule (all students receiving in-person instruction), and school closure (all students receiving online instruction). The study period was February 18th-November 24th, 2020 and the state of Georgia was used as a case study. Across all scenarios, the number of COVID19-related deaths ranged from approximately 17 to 22 thousand during the study period, and on the peak day, the number of new infections ranged from 43 to 68 thousand. An alternating school day schedule performed: (i) almost as well as keeping schools closed, with the infection attack rate ranging from 38.5% to 39.8% compared to that of 37.7% under school closure; (ii) slightly better than only allowing children 10 years or younger to return to in-person instruction. Delaying the reopening of schools had a minimal impact on reducing infections and deaths under most scenarios.Significance StatementThis study provides insights on the impact of various school reopening dates and scenarios on the spread of COVID19, incorporating differences between children and adults in terms of disease progression and community transmission. School districts are faced with these challenging decisions considering the complex tradeoffs of their impact between public health, education, and society. While the number of new COVID19 confirmed cases continue to increase in many states, so are concerns about the negative impact of school closures on the children’s education and development. The systematic analysis of school reopening scenarios provided in this study will support school systems in their decision-making regarding if, when, and how to return to in-person instruction.
Decreasing median age of COVID-19 cases in the United States – changing epidemiology or changing surveillance?
Authors: Greene Dina N. , Jackson Michael L. , Hillyard David R. , Delgado Julio C. , Schmidt Robert 
Published: July 2020

ABSTRACTBackgroundUnderstanding and monitoring the demographics of SARS-CoV-2 infection can inform strategies for prevention. Surveillance monitoring has suggested that the age distribution of people infected with SARS-CoV-2 has changed since the pandemic began, but no formal analysis has been performed.MethodsRetrospective review of SARS-CoV-2 molecular testing results from a national reference laboratory was performed. Result distributions by age and positivity were compared between early period (March-April 2020) and late periods (June-July 2020) of the COVID-19 pandemic. Additionally, a sub-analysis compared changing age distributions between inpatients and outpatients.ResultsThere were 277,601 test results of which 19320 (7.0%) were positive. The median age of infected people declined over time (p < 0.0005). In March-April, the median age of positive people was 40.8 years (Interquartile range (IQR): 29.0 – 54.1). In June-July, the median age of positive people was 35.8 years (IQR: 24.0 – 50.2). The positivity rate of patients under 50 increased from 6.0 to 10.6 percent and the positivity rate for those over 50 decreased from 6.3 to 5.0 percent between the early and late periods. The trend was only observed for outpatient populations.ConclusionsWe confirm that there is a trend toward decreasing age among persons with laboratory- confirmed SARS-CoV-2 infection, but that these trends seem to be specific to the outpatient population. Overall, this suggests that observed age-related trends are driven by changes in testing patterns rather than true changes in the epidemiology of SARS-CoV-2 infection. This calls for caution in interpretation of routine surveillance data until testing patterns stabilize.SummaryWe used national reference laboratory data to compare ages of patients tested for SARS-CoV-2 in March/April 2020 vs. June/July. Median age declined overall, but increased for inpatients, suggesting that declining age is due to changes in surveillance, not COVID-19 epidemiology.
Forecasting hospitalizations due to COVID-19 in South Dakota, USA
Authors: Wesner Jeff S. , Peursem Dan Van , Flores José D. , Lio Yuhlong , Wesner Chelsea A. ,   University of South Dakota
Published: July 2020

1AbstractAnticipating the number of hospital beds needed for patients with COVID-19 remains a challenge. Early efforts to predict hospital bed needs focused on deriving predictions from SIR models, largely at the level of countries, provinces, or states. In the United States, these models rely on data reported by state health agencies. However, predictive disease and hospitalization dynamics at the state level are complicated by geographic variation in disease parameters. In addition it is difficult to make forecasts early in a pandemic due to minimal data. However, Bayesian approaches that allow models to be specified with informed prior information from areas that have already completed a disease curve can serve as prior estimates for areas that are beginning their curve. Here, a Bayesian non-linear regression (Weibull function) was used to forecast cumulative and active COVID-19 hospitalizations for South Dakota, USA. As expected, early forecasts were dominated by prior information, which was derived from New York City. Importantly, hospitalization trends also differed within South Dakota due to early peaks in an urban area, followed by later peaks in other rural areas of the state. Combining these trends led to altered forecasts with relevant policy implications.
Longitudinal study about low back pain, mental health, and access to healthcare system during COVID-19 pandemic: protocol of an ambispective cohort
Authors: Feter Natan , Caputo Eduardo L , Doring Igor R , Leite Jayne S , Cassuriaga Júlia , Reichert Felipe F , da Silva Marcelo C , Rombaldi Airton J 
Published: July 2020

AbstractThis study aims to investigate the effects of physical activity before, during, and after social distancing due to the COVID-19 pandemic on low back pain (LBP), mental health and healthcare access. The PAMPA Cohort (Prospective Study About Mental and Physical Health) is a state-level ambispective longitudinal observational study that will be conducted in Rio Grande do Sul, Brazil. An online-based questionnaire will be used to assess LBP, mental health, healthcare access and physical activity at four time points: 1) pre-COVID-19 social distancing, 2) during COVID-19 social distancing, 3) 6 months and 4) 12 months after baseline. A proportional sample size calculation was conducted, and the final sample size was estimated in 1,767 people, distributed in seven state regions. Participants will be recruited by a four-arm approach: contact with universities, social media, local media and personal contacts. Descriptive analyzes will be reported as mean or proportion and respective 95% confidence interval (CI), when appropriate. Comparison between pre- and during COVID-19 social distancing, and after baseline assessments will be performed using two-way ANOVA with repeated measures. Proportions will be compared by Chi-squared test.