bluuebunny/crossref_metadata_2025_split huggingface dataset:
Defining Facets of Social Distancing during the COVID-19 Pandemic: Twitter Analysis
Authors: Kwon Jiye , Grady Connor , Feliciano Josemari T. , Fodeh Samah J. 
Published: May 2020

AbstractSocial distancing has been one of the primary mitigation strategies in the United States to control the spread of novel coronavirus disease (COVID-19) and can be viewed as a multi-faceted public health measure. Using Twitter data, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine the most amplified tweets among social distancing facets. We analyzed a total of 259,529 unique tweets containing “coronavirus” from 115,485 unique users between January 23, 2020 and March 24, 2020 that were identified by the Twitter API as English and U.S.-based. Tweets containing specified keywords (determined a priori) were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. Tweets about social disruptiveness were most retweeted, and implementation tweets were most favorited. Social distancing tweets became overall more prevalent in the U.S. from late January to March but were not geographically uniform. In January and February, facets of social distancing appeared in Los Angeles, San Francisco, and Seattle, which were among the first cities impacted by the COVID-19 outbreak. Tweets related to the “implementation” and “negative emotions” facets of social distancing largely dominated in combination with topics of “social disruption” and “adaptation”, albeit to a lesser degree. Social distancing can be defined in terms of facets that respond and represent certain moments and events in a pandemic, including travel restrictions and rising COVID-19 case counts. For example, in February, Miami, FL had a low volume of social distancing tweets but grew in March which corresponded with the rise of COVID-19 cases in the city. This suggests that overall volume of social distancing tweets can reflect the relative case count in respective locations.
Estimates of the ongoing need for social distancing and control measures post-“lockdown” from trajectories of COVID-19 cases and mortality
Authors: Lonergan Mike , Chalmers James 
Published: May 2020

AbstractBy 29th April 2020, COVID-19 had caused more than 3 million cases across more than 200 countries. And most countries with significant outbreaks had introduced social distancing or “lockdown” measures to reduce viral transmission. So the key question now is when, how, and to what extent, these measures can be lifted.By fitting regression models to publically available data on daily numbers of newly-confirmed cases and mortality, trajectories, doubling times and reproduction number (R0) were estimated both before and under the control measures. These data ran up to 29th April 2020, and covered 73 countries that had provided sufficient data for modelling.The estimates of R0, before lockdown, based on these data were broadly consistent with those previously published at between 2.0 and 3.7 in the countries with the largest number of cases available for analysis (USA, Italy, Spain, France and UK). There was little evidence to suggest that the restrictions had reduced R far below 1 in many places, with France having the most rapid reductions – R0 0.77 (95%CI 0.68-0.87), based on cases and 0.78 (95%CI 0.68-0.88) based on mortality.Intermittent lockdown has been proposed as a means of controlling the outbreak while allowing periods of increase freedom and economic activity. These data suggest that few countries could have even one week per month unrestricted without seeing resurgence of the epidemic. Similarly, restoring 20% of the activity that has been prevented by the lockdowns looks difficult to reconcile with preventing the resurgence of the disease in most countries.
Modeling Return of the Epidemic: Impact of Population Structure, Asymptomatic Infection, Case Importation and Personal Contacts
Authors: Yu Xinhua 
Published: May 2020

AbstractBackgroundProactive interventions have halted the pandemic of coronavirus infected disease in some regions. However, without reaching herd immunity, the return of epidemic is possible. We investigate the impact of population structure, case importation, asymptomatic cases, and the number of contacts on a possible second wave of epidemic through mathematical modelling.Methodswe built a modified Susceptible-exposed-Infectious-Removed (SEIR) model with parameters mirroring those of the COVID-19 pandemic and reported simulated characteristics of epidemics for incidence, hospitalizations and deaths under different scenarios.ResultsA larger percent of elderly people leads to higher number of hospitalizations, while a large percent of prior infection will effectively curb the epidemic. The number of imported cases and the speed of importation have small impact on the epidemic progression. However, a higher percent of asymptomatic cases slows the epidemic down and reduces the number of hospitalizations and deaths at the epidemic peak. Finally, reducing the number of contacts among young people alone has moderate effects on themselves, but little effects on the elderly population. However, reducing the number of contacts among elderly people alone can mitigate the epidemic significantly in both age groups, even though young people remain active within themselves.ConclusionReducing the number of contacts among high risk populations alone can mitigate the burden of epidemic in the whole society. Interventions targeting high risk groups may be more effective in containing or mitigating the epidemic.
Compositional Cyber-Physical Epidemiology of COVID-19
Authors: Ro Jin Woo , Allen Nathan , Ai Weiwei , Prasad Debi , Roop Partha S. 
Published: May 2020

AbstractCOVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured.We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems (CPSs). Consequently, we term the new framework Compositional Cyber-Physical Epidemiology (CCPE). We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.
Mobility data can explain the entire COVID-19 outbreak course in Japan
Authors: Kurita Junko , Sugawara Tamie , Ohkusa Yasushi 
Published: May 2020

AbstractBackgroundIn Japan, as a countermeasure against the COVID-19 outbreak, voluntary restrictions against going out (VRG) have been applied.ObjectWe examined mobility information provided by Apple Inc. to a susceptible–infected–recovery model.MethodWhen applying a polynomial function to daily Apple data with the SIR model, we presumed the function up to a cubic term as in our earlier study.ResultsEstimation results demonstrated R0as 1.507 and its 95% confidence interval was [1.502, 1.509].. The estimated coefficients of Apple data was 1.748 and its 95% confidence interval was [1.731, 1.788].Discussion and ConclusionResults show that mobility data from Apple Inc. can explain the entire course of the outbreak in COVID-19 in Japan. Therefore, monitoring Apple data might be sufficient to adjust control measures to maintain an effective reproduction number of less than one.
Determining the source of transmission of SARS-CoV-2 infection in a healthcare worker
Authors: Safdar Nasia , Moreno Gage K. , Braun Katarina M. , Friedrich Thomas C. , O’Connor David H. 
Published: May 2020

BackgroundHealthcare workers (HCWs) are at the frontlines of the COVID-19 pandemic and are at risk of exposure to SARS-CoV-2 infection from their interactions with patients and in the community (1, 2). Limited availability of recommended personal protective equipment (PPE), in particular N95 respirators, has fueled concerns about whether HCWs are adequately protected from exposure while caring for patients. Understanding the source of SARS-CoV-2 infection in a HCW – the community or the healthcare system – is critical for understanding the effectiveness of hospital infection control and PPE practices. In Dane County, Wisconsin, community prevalence of SARS-CoV-2 is relatively low (cumulative prevalence of ~0.06% – positive cases / total population in Dane county as of April 17). Although SARS-CoV-2 infections in HCWs are often presumed to be acquired during the course of patient care, there are few reports unambiguously identifying the source of acquisition.ObjectiveTo determine the source of transmission of SARS-CoV-2 in a healthcare worker.
COVID-19 and depressive symptoms in students before and during lockdown
Authors: Meda Nicola , Pardini Susanna , Slongo Irene , Bodini Luca , Rigobello Paolo , Visioli Francesco , Novara Caterina 
Published: May 2020

ABSTRACTThe lockdown due to coronavirus pandemic may exacerbate depressive symptoms, experts argue. Here we report that students, a high-risk category for mental disorders, report on average worse depressive symptoms than six months before isolation. The prospective data reported herein should alert clinician of a possible aggravation as well as new-onsets of depressive symptoms in students.
Media representation of spiders may exacerbate arachnophobic sentiments by framing a distorted perception of risk
Authors: Mammola Stefano , Nanni Veronica , Pantini Paolo , Isaia Marco 
Published: May 2020

ABSTRACTSpiders are able to arouse strong emotional reactions in humans. While spider bites are statistically rare events, our perception is skewed towards the potential harm spiders can cause to humans. We examined the human dimension of spiders through the lens of traditional media, by analysing more than 300 spider-related news published online in Italian newspapers between 2010 and 2020. We observed a recent, exponential increase in the frequency of the news, particularly those focused on medically important spiders – the Mediterranean black widow (Latrodectus tredecimguttatus) and the Mediterranean recluse (Loxosceles refescens). The news quality was generally poor: 70% contained different types of error, 32% were exaggerated, and in virtually none was an expert consulted. Overstated news referring to spider bites were significantly more shared on social media, thus contributing to frame a distorted perception of the risk associated with a spider bite and possibly reducing general public tolerance of spiders.
Atypical meiosis can be adaptive in outcrossedS. pombedue towtfmeiotic drivers
Authors: Bravo Núñez María Angélica , Sabbarini Ibrahim M. , Eide Lauren E. , Unckless Robert L. , Zanders Sarah E. 
Published: May 2020

AbstractKiller meiotic drivers are genetic parasites that destroy ‘sibling’ gametes lacking the driver allele. The fitness costs of drive can lead to selection of unlinked suppressors. This suppression could involve evolutionary tradeoffs that compromise gametogenesis and contribute to infertility.Schizosaccharomyces pombe, an organism containing numerous gamete-killingwtfdrivers, offers a tractable system to test this hypothesis. Here, we demonstrate that in scenarios analogous to outcrossing,wtfdrivers generate a fitness landscape in which atypical gametes, such as aneuploids and diploids, are advantageous. In this context,wtfdrivers can decrease the fitness cost of mutations that disrupt meiotic fidelity and, in some circumstances, can even make such mutations beneficial. Moreover, we find thatS. pombeisolates vary greatly in their ability to make haploid gametes, with some isolates generating more than 25% aneuploid or diploid gametes. This work empirically demonstrates the potential for meiotic drivers to shape the evolution of gametogenesis.
Preparedness and response to Pediatric CoVID-19 in European Emergency Departments: a survey of the REPEM and PERUKI networks
Authors: Bressan Silvia , Buonsenso Danilo , Farrugia Ruth , Parri Niccolo’ , Oostenbrink Rianne , Titomanlio Luigi , Roland Damian , Nijman Ruud G. , Maconochie Ian , Da Dalt Liviana , Mintegi Santiago 
Published: May 2020

AbstractStudy objectiveWe aimed to describe the preparedness and response to the COVID-19 pandemic in referral EDs caring for children across Europe.MethodsWe did a cross-sectional point prevalence survey, which was developed and disseminated through the pediatric emergency medicine research networks for Europe (REPEM) and the United Kingdom and Ireland (PERUKI). We included a pre-determined number of centers based on each country population: five to ten EDs for countries with > 20 million inhabitants and one to five EDs for the other countries. ED directors or named delegates completed the survey between March 20th and 21st to report practice in use one month after the outbreak in Northern Italy. We used descriptive statistics to analyse data.ResultsOverall 102 centers from 18 countries completed the survey: 34% did not have an ED contingency plan for pandemics and 36% had never had simulations for such events. Wide variation on PPE items was shown for recommended PPE use at pre-triage and for patient assessment, with 62% of centers experiencing shortage in one or more PPE items. COVID-19 positive ED staff was reported in 25% of centers. Only 17% of EDs had negative pressure isolation rooms.ConclusionWe identified variability and gaps in preparedness and response to the COVID-19 epidemic across European referral EDs for children. Early availability of a documented contingency plan, provision of simulation training, appropriate use of PPE, and appropriate isolation facilities emerged as key factors that should be optimized to improve preparedness and inform responses to future pandemics.
Deciphering the causal relationship between blood metabolites and Alzheimer’s Disease: a Mendelian Randomization study
Authors: Lord Jodie , Jermy Bradley , Green Rebecca , Wong Andrew , Xu Jin , Legido-Quigley Cristina , Dobson Richard , Richards Marcus , Proitsi Petroula 
Published: May 2020

AbstractThere are currently no disease modifying treatments for Alzheimer’s Disease (AD). Epidemiological studies have highlighted blood metabolites as potential biomarkers, but possible confounding and reverse causation prevent causal conclusions. Here, we investigated whether nineteen metabolites previously associated with midlife cognitive function, are on the causal pathway to AD.Summary statistics from the largest Genome-Wide Association Studies (GWAS) for AD and for metabolites were used to perform bi-directional univariable Mendelian Randomisation (MR). Bayesian model averaging MR (MR-BMA) was additionally performed to address high correlation between metabolites and to identify metabolite combinations which may be on the AD causal pathway.Univariable MR indicated three Extra-Large High-Density Lipoproteins (XL.HDL) to be on the causal pathway to AD: Free Cholesterol (XL.HDL.FC: OR=0.86, 95% CI=0.78-0.94), Total Lipids (XL.HDL.L: OR=0.88, 95% CI=0.80-0.97), and Phospholipids (XL.HDL.PL: OR=0.87, 95% CI=0.81-0.97); significant at an adjusted threshold of p<0.009. MR-BMA corroborated XL.HDL.FC to be amongst the top three causal metabolites, additionally to Total Cholesterol in XL.HDL (XL.HDL.C) and Glycoprotein Acetyls (GP) (posterior probabilities=0.112, 0.113, 0.287 respectively). Both XL.HDL.C and GP also demonstrated suggestive evidence of univariable causal associations (XL.HDL.C:OR=0.88, 95% CI=0.79-0.99; GP:OR=1.2, 95% CI=1.05-1.38); significant at the 5% level.This study offers insight into the causal relationship between metabolites previously demonstrating association with mid-life cognition, and AD. It highlights GP in addition to several XL.HDLs as causal candidates which warrant further investigation. As the pathological changes underpinning AD are thought to develop decades prior to symptom onset, progressing these findings could hold special value in informing future risk reduction strategies.
Fractional SIR Epidemiological Models
Authors: Taghvaei Amirhossein , Georgiou Tryphon T. , Norton Larry , Tannenbaum Allen 
Published: May 2020

AbstractThe purpose of this work is to make a case for epidemiological models with fractional exponent in the contribution of sub-populations to the transmission rate. More specifically, we question the standard assumption in the literature on epidemiological models, where the transmission rate dictating propagation of infections is taken to be proportional to the product between the infected and susceptible sub-populations; a model that relies on strong mixing between the two groups and widespread contact between members of the groups. We content, that contact between infected and susceptible individuals, especially during the early phases of an epidemic, takes place over a (possibly diffused) boundary between the respective sub-populations. As a result, the rate of transmission depends on the product of fractional powers instead. The intuition relies on the fact that infection grows in geographically concentrated cells, in contrast to the standard product model that relies on complete mixing of the susceptible to infected sub-populations. We validate the hypothesis of fractional exponents i) by numerical simulation for disease propagation in graphs imposing a local structure to allowed disease transmissions and ii) by fitting the model to a COVID-19 data set provided by John Hopkins University (JHUCSSE) for the period Jan-31-20 to Mar-24-20, for the countries of Italy, Germany, Iran, and France.
Bayesian Inference of COVID-19 Spreading Rates in South Africa
Authors: Mbuvha Rendani , Marwala Tshilidzi 
Published: May 2020

AbstractThe Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for the development of prompt mitigating responses under conditions of high uncertainty. Fundamental to the design of rapid state reactions is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.
Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models
Authors: Pourghasemi Hamid Reza , Pouyan Soheila , Farajzadeh Zakariya , Sadhasivam Nitheshnirmal , Heidari Bahram , Babaei Sedigheh , Tiefenbacher John P. 
Published: May 2020

AbstractInfectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-2019) has caused a global health emergency. Identification of regions with high risk for COVID-19 outbreak is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak and identify areas with a high risk of human infection with virus in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran. The daily observations of infected cases was tested in the third-degree polynomial and the autoregressive and moving average (ARMA) models to examine the patterns of virus infestation in the province and in Iran. The results of disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors including minimum temperature of coldest month (MTCM), maximum temperature of warmest month (MTWM), precipitation in wettest month (PWM), precipitation of driest month (PDM), distance from roads, distance from mosques, distance from hospitals, distance from fuel stations, human footprint, density of cities, distance from bus stations, distance from banks, distance from bakeries, distance from attraction sites, distance from automated teller machines (ATMs), and density of villages – were selected for spatial modelling. The predictive ability of an SVM model was assessed using the receiver operator characteristic – area under the curve (ROC-AUC) validation technique. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) a good prediction of change detection. The growth rate (GR) average for active cases in Fars for a period of 41 days was 1.26, whilst it was 1.13 in country and the world. The results of the third-degree polynomial and ARMA models revealed an increasing trend for GR with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although an explosive growth of the infected cases is expected in the country. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
Visible implant elastomer (VIE) success in early larval stages of a tropical amphibian species
Authors: Fouilloux Chloe , Garcia-Costoya Guillermo , Rojas Bibiana 
Published: May 2020

AbstractAnimals are often difficult to distinguish at an individual level, but being able to identify individuals can be crucial in ecological or behavioral studies. In response to this challenge, biologists have developed a range of marking (tattoos, brands, toe-clips) and tagging (PIT, VIA, VIE) methods to identify individuals and cohorts. Animals with complex life cycles are notoriously hard to mark because of the distortion or loss of the tag across metamorphosis. In frogs, few studies have attempted larval tagging and none have been conducted on a tropical species. Here, we present the first successful account of VIE tagging in early larval stages (Gosner stage 25) of the dyeing poison frog (Dendrobates tinctorius) coupled with a novel anaesthetic (2-PHE) application for tadpoles that does not require buffering. Mean weight of individuals at time of tagging was 0.12g, which is the smallest and developmentally youngest anuran larvae tagged to date. We report 81% tag detection over the first month of development, as well as the persistence of tags across metamorphosis in this species. Cumulative tag retention versus tag observation differed by approximately 15% across larval development demonstrating that “lost” tags can be found later in development. Tagging had no effect on tadpole growth rate or survival. Successful application of VIE tags onD. tinctoriustadpoles introduces a new method that can be applied to better understand early life development and dispersal in various tropical species.