bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, A Modelling Study
Authors: Arslan Şeyma , Özdemir Muhammed Yusuf , Uçar Abdullah 
Published: April 2020

AbstractBackgroundThis study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey.MethodsThis study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted via a SEIR-based simulator (TURKSAS) in four scenarios.ResultsAccording to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88 · 1%, and the mortality ratio is 0·54%. The ICU bed capacity in Turkey is expected to be exceeded by 4·4-fold and non-ICU bed capacity by 3·21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey’s capacity.DiscussionPredictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modelling epidemics and establishing decision support systems is an important requirement.
Using ICU data to improve the real-time estimation of the effective reproductive number of the COVID-19 epidemic in 9 European countries
Authors: Hurtado Samuel , Tinajero David 
Published: April 2020

1.SummaryWe replicate a recent study by the Imperial College COVID-19 Response Team (Flaxman et al, 2020) that estimates both the effective reproductive number, Rt, of the current COVID-19 epidemic in 11 European countries, and the impact of different nonpharmaceutical interventions that have been implemented to try to contain the epidemic, including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. The main indicator they use for measuring the evolution of the epidemic is the daily number of deaths by COVID-19 in each country, which is a better statistic than the number of identified cases because it doesn’t depend so much on the testing strategy that is in place in each country at each moment in time.We improve on their estimation by using data from the number of patients in intensive care, which provides two advantages over the number of deaths: first, it can be used to construct a signal with less bias: as the healthcare system of a country reaches saturation, the mortality rate would be expected to increase, which would bias the estimates of Rtand of the impact of measures implemented to contain the epidemic; and second, it is a signal with less lag, as the time from onset of symptoms to ICU admission is shorter than the time from onset to death (on average, 7.5 days shorter). The intensive care signal we use is not just the number of people in ICU, as this would also be biased if the healthcare system has reached saturation (in this case, biased downwards, as admissions are no longer possible when all units are in use). Instead, we estimate the daily demand of intensive care, as the sum of two components: the part that is satisfied (new ICU admissions) and the part that is not (which results in excess mortality).Thanks to the advantages of this ICU signal in terms of timeliness and bias, we find that most of the countries in the study have already reached Rt<1 with 95% confidence (Italy, Spain, Austria, Denmark, France, Norway and Switzerland, but not Belgium or Sweden), whereas the original methodology of Flaxman et al (2020), even with updated data, would only find Rt<1 with 95% confidence for Italy and Switzerland.
Multi-chain Fudan-CCDC model for COVID-19 – a revisit to Singapore’s case
Authors: Pan Hanshuang , Shao Nian , Yan Yue , Luo Xinyue , Wang Shufen , Ye Ling , Cheng Jin , Chen Wenbin 
Published: April 2020

AbstractBackgroundCOVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense world-wide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.MethodsWe propose the multi-chain Fudan-CCDC model based on the original single-chain model in [8] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify parameters of models by minimizing the penalty function.ResultsThe numerical simulation results exhibit the multichain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of ±25% fluctuation from simulation results. It is predicted by multi-chain models that Singapore are experiencing a nonnegligible risk of explosive outbreak, thus stronger measures are urgently needed to contain the epidemic.ConclusionThe multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data in those countries where the single-chain model shows deviation from the data.
Importance of untested infectious individuals for the suppression of COVID-19 epidemics
Authors: Pérez-Reche Francisco J. , Forbes Ken J. , Strachan Norval J. C. 
Published: April 2020

AbstractThe impact of the extent of testing infectious individuals on suppression of COVID-19 is illustrated from the early stages of outbreaks in Germany, the Hubei province of China, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 50% to 80% of the infected individuals. Even when unreported cases are taken into account, we estimate that less than 8% of the population would have been exposed to SARS-CoV-2 by 09/04/2020 in the analysed outbreaks. These levels are far below the 70-85% needed to ensure herd immunity and would predict a resurgence of infection if ongoing lockdowns in the outbreaks are fully lifted. We propose that partially lifted lockdowns together with early and thorough testing allowing for quick isolation of both symptomatic and asymptomatic cases could lead to suppression of secondary waves of COVID-19 epidemics.
The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts as of April 10, 2020
Authors: Yuan George Xianzhi , Di Lan , Gu Yudi , Qian Guoqi , Qian Xiaosong 
Published: April 2020

AbstractBased on a new concept called “Turning Period”, the goal of this report is to show how we can conduct the prediction for the outlook in the different stages for the battle with outbreak of COVID-19 currently in US, in particular, to identify when each of top 15 states in USA (basically on their populations) is going to enter into the stage that the outbreak of COVID-19 is under the control by the criteria such as daily change of new patients is less than 10% smoothly. Indeed, based on the data of April 10, 2020 with the numerical analysis, we are able to classify 15 states of US into the following four different categories for the Prevention and Control of Infectious Diseases Today and the main conclusion are:First, staring around April 14, 20202, three states which are Washington State, Louisiana and Indiana are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general.Second, staring aroundApril15, 20202, two states which are New Jersey, and New York are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general.Third, staring aroundApril16, 20202, seven states which are California, Florida, Georgia (GA), Illinois, Maryland, Indiana, Michigan, and Pennsylvania are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general.Fourth, staring aroundApril17, 20202, three states which are Texas, Massachusetts, and Connecticut are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general.Finally, we want to reinforce that emergency risk management is always associated with the implementation of an emergency plan. The identification of the Turning Time Period is key to emergency planning as it provides a timeline for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.
Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media
Authors: Mohammed Ali Al-Garadi , Yang Yuan-Chi , Cai Haitao , Ruan Yucheng , O’Connor Karen , Graciela Gonzalez-Hernandez , Perrone Jeanmarie , Sarker Abeed 
Published: April 2020

ABSTRACTPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.
Partial unlock for COVID-19-like epidemics can save 1-3 million lives worldwide
Authors: Shuler Robert L. , Koukouvitis Theodore , Suematsu Dyske 
Published: April 2020

AbstractBackgroundA large percentage of deaths in an epidemic or pandemic can be due to overshoot of population (herd) immunity, either from the initial peak or from planned or unplanned exit from lockdown or social distancing conditions.ObjectivesWe study partial unlock or reopening interaction with seasonal effects in a managed epidemic to quantify overshoot effects on small and large unlock steps and discover robust strategies for reducing overshoot.MethodsWe simulate partial unlock of social distancing for epidemics over a range of replication factor, immunity duration and seasonality factor for strategies targeting immunity thresholds using overshoot optimization.ResultsSeasonality change must be taken into account as one of the steps in an easing sequence, and a two step unlock, including seasonal effects, minimizes overshoot and deaths. It may cause undershoot, which causes rebounds and assists survival of the pathogen.ConclusionsPartial easing levels, even low levels for economic relief while waiting on a vaccine, have population immunity thresholds based on the reduced replication rates and may experience overshoot as well. We further find a two step strategy remains highly sensitive to variations in case ratio, replication factor, seasonality and timing. We demonstrate a three or more step strategy is more robust, and conclude that the best possible approach minimizes deaths under a range of likely actual conditions which include public response.
A blueprint for the implementation of a validated approach for the detection of SARS-Cov2 in clinical samples in academic facilities
Authors: Sridhar Sushmita , Forrest Sally , Kean Iain , Young Jamie , Scott Josefin Bartholdson , Maes Mailis , Pereira-Dias Joana , Parmar Surendra , Routledge Matthew , Rivett Lucy , Dougan Gordon , Weekes Michael , Curran Martin , Goodfellow Ian , Baker Stephen 
Published: April 2020

AbstractThe COVID-19 pandemic is expanding at an unprecedented rate. As a result, diagnostic services are stretched to their limit, and there is a clear need for the provision of additional diagnostic capacity. Academic laboratories, many of which are closed due to governmental lockdowns, may be in a position to support local screening capacity by adapting their current laboratory practices. Here, we describe the process of developing a SARS-Cov2 diagnostic workflow in a conventional academic Containment Level 2 (CL2) laboratory. Our outline includes simple SARS-Cov2 deactivation upon contact, the methods for a quantitative real-time reverse transcriptase PCR (qRT-PCR) detecting SARS-Cov2, a description of process establishment and validation, and some considerations for establishing a similar workflow elsewhere. This was achieved under challenging circumstances through the collaborative efforts of scientists, clinical staff, and diagnostic staff to mitigate to the ongoing crisis. Within 14 days, we created a validated COVID-19 diagnostics service for healthcare workers in our local hospital. The described methods are not exhaustive, but we hope may offer support to other academic groups aiming to set up something comparable in a short time frame.
Chloroquine Kills Hair Cells in Zebrafish Lateral Line and Murine Cochlear Cultures: Implications for Ototoxicity
Authors: Davis Samantha N. , Wu Patricia , Camci Esra D. , Simon Julian A. , Rubel Edwin W , Raible David W. 
Published: April 2020

AbstractHearing and balance deficits have been reported during and following treatment with the antimalarial drug chloroquine. However, experimental work examining the direct actions of chloroquine on mechanoreceptive hair cells in common experimental models is lacking. This study examines the effects of chloroquine on hair cells using two common experimental models: the zebrafish lateral line and neonatal mouse cochlear cultures. Zebrafish larvae were exposed to varying concentrations of chloroquine phosphate or hydroxychloroquine for 1 hr or 24 hr, and hair cells assessed by antibody staining. A significant, dose-dependent reduction in the number of surviving hair cells was seen across conditions for both exposure periods. Hydroxycholroquine showed similar toxicity. In mouse cochlear cultures, chloroquine damage was specific to outer hair cells in tissue from the cochlear basal turn, consistent with susceptibility to other ototoxic agents. These findings suggest a need for future studies employing hearing and balance monitoring during exposure to chloroquine and related compounds, particularly with interest in these compounds as therapeutics against viral infections including coronavirus.
Molecular Assays of Pollen Use Consistently Reflect Pollinator Visitation Patterns in a System of Flowering Plants
Authors: James Aubrie R. M. , Geber Monica A. , Toews David P. L. 
Published: April 2020

ABSTRACTDetermining how pollinators visit plants versus how they carry and transfer pollen is an ongoing project in pollination ecology. The differences between how pollinators visit flowers versus how they carry pollen can be appreciable, but the current tools for identifying the pollens that bees carry have different strengths and weaknesses when used for ecological inference. In this study we use three methods to better understand a system of congeneric, co-flowering plants in the genusClarkiaand their bee pollinators: observations of plant-pollinator contact in the field, and two different molecular methods to estimate the relative abundance of eachClarkiapollen in samples collected from pollinators. We use these methods to investigate if observations of plant-pollinator contact in the field correspond to the pollen bees carry; if individual bees carryClarkiapollens in predictable ways, based on previous knowledge of their foraging behaviors; and how the three approaches differ for understanding plant-pollinator interactions. We find that observations of plant-pollinator contact are generally predictive of the pollens that bees carry while foraging, and network topologies using the three different methods are statistically indistinguishable from each other. Results from molecular pollen analysis also show that while bees can carry multiple species ofClarkiaat the same time, they often carry one species of pollen. Our work contributes to the growing body of literature aimed at resolving how pollinators use floral resources. We suggest our novel relative amplicon quantification method as another tool in the developing molecular ecology and pollination biology toolbox.
Carbon Sequestration Potential of Oil Palm Plantations in Southern Philippines
Authors: Borbon Sheila Mae C. , Medina Michael Arieh P. , Patricio Jose Hermis P. , Toledo-Bruno Angela Grace 
Published: April 2020

AbstractAside from the greenhouse gas reduction ability of palm oil-based biofuel as alternative to fossil fuels, another essential greenhouse gas mitigation ability of oil palm plantation is in terms of offsetting anthropogenic carbon emissions through carbon sequestration. In this context, this study was done to determine the carbon sequestration potential of oil palm plantations specifically in two areas in Mindanao, Philippines. Allometric equation was used in calculating the biomass of oil palm trunk. Furthermore, destructive methods were used to determine the biomass in other oil palm parts (fronds, leaves, and fruits). Carbon stocks from the other carbon pools in the oil palm plantations were measured which includes understory, litterfall, and soil. Results revealed that the average carbon stock in the oil palm plantations is 40.33 tC/ha. Majority of the carbon stock is found in the oil palm plant (53%), followed by soil (38%), litterfall (6%), and understory, (4%). The average carbon sequestration rate of oil palm plants is estimated to be 4.55 tC/ha/year. It is advised that oil palm expansions should be done on grasslands and barren lands instead of forest lands to avoid incurring “carbon debts”. It is also recommended that oil palms should be mixed with shrub crops species to enhance soil organic carbon as well as increase the aboveground biomass in oil palm plantations.
Spatiotemporal functional interactivity among large-scale brain networks
Authors: Xu Nan , Doerschuk Peter C. , Keilholz Shella D. , Spreng R. Nathan 
Published: April 2020

AbstractThe macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting-state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N=200) we applied prediction correlation techniques to four resting state fMRI runs (total TRs = 4800). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region’s role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.
Distinct early IgA profile may determine severity of COVID-19 symptoms: an immunological case series
Authors: Dahlke Christine , Heidepriem Jasmin , Kobbe Robin , Santer René , Koch Till , Fathi Anahita , Ly My L. , Schmiedel Stefan , Seeberger Peter H. ,   ID-UKE COVID-19 study group, Addo Marylyn M. , Loeffler Felix F. 
Published: April 2020

AbstractSARS-CoV-2 is the causative agent of COVID-19 and is a severe threat to global health. Patients infected with SARS-CoV-2 show a wide range of symptoms and disease severity, while limited data is available on its immunogenicity.Here, the kinetics of the development of SARS-CoV-2-specific antibody responses in relation to clinical features and dynamics of specific B-cell populations are reported. Immunophenotyping of B cells was performed by flow cytometry with longitudinally collected PBMCs. In parallel, serum samples were analyzed for the presence of SARS-CoV-2-specific IgA, IgG, and IgM antibodies using whole proteome peptide microarrays. Soon after disease onset in a mild case, we observed an increased frequency of plasmablasts concomitantly with a strong SARS-CoV-2-specific IgA response. In contrast, a case with more severe progression showed a delayed, but eventually very strong and broad SARS-CoV-2-specific IgA response.This case study shows that determining SARS-CoV-2-specific antibody epitopes can be valuable to monitor the specificity and magnitude of the early B-cell response, which could guide the development of vaccine candidates. Follow-up studies are required to evaluate whether the kinetics and strength of the SARS-CoV-2-specific IgA response could be potential prognostic markers of viral control.