bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experience with Social Distancing Early in the COVID-19 Pandemic in the United States: Implications for Public Health Messaging
Authors: Moore Ryan C. , Lee Angela , Hancock Jeffrey T. , Halley Meghan , Linos Eleni 
Published: April 2020

Our goal is to inform ongoing public health policy on the design and communication of COVID-19 social distancing measures to maximize compliance. We assessed the US public’s early experience with the COVID-19 crisis during the period when shelter-in-place orders were widely implemented to understand non-compliance with those orders, sentiment about the crisis, and to compare across age categories associated with different levels of risk. We posted our survey on Twitter, Facebook, and NextDoor on March 14th to March 23rd that included 21 questions including demographics, impact on daily life, actions taken, and difficulties faced.1 We analyzed the free-text responses to the impact question using LIWC, a computational natural language processing tool2, and performed a thematic content analysis of the reasons people gave for non-compliance with social distancing orders. Stanford University’s IRB approved the study.In 9 days, we collected a total of 20,734 responses. 6,573 individuals provided a response (≥30 words) to the question, “Tell us how the coronavirus crisis is impacting your life.” Our data (Figure 1) show that younger people (18-31) are more emotionally negative, self-centered, and less concerned with family, while middle-aged people are group-oriented (32-44) and focused on family (32-64) (all p values < .05 corrected for multiple comparisons). Unsurprisingly, the oldest and most at-risk group (65+) are more focused on biological terms (e.g., health-related topics), but were surprisingly low in anxiety and high in emotionally positive terms relative to those at lower risk.We also content-analyzed 7,355 responses (kappa’s > .75) to the question, “What are the reasons you are not self-isolating more?” Of these participants, 39.8% reported not being compliant, with the youngest group (18-31) having the lowest compliance rate (52.4%) compared to the other age groups (all > 60%; all p values < .01). Table 1 describes the seven primary themes for non-compliance. Non-essential work requirements, concerns about mental and physical health, and the belief that other precautions were sufficient were the most common reasons, although other rationales included wanting to continue everyday activities and beliefs that society is over-reacting. Childcare was an important concern for a subset of respondents.Overall, our findings suggest that public health messages should focus on young people and 1) address their negative affect, 2) refocus their self-orientation by emphasizing the importance of individual behavior to group-level health outcomes, and 3) target the specific rationales that different people have regarding the pandemic to maximize compliance with social distancing.
On the corona infection model with contact restriction
Authors: Mimkes Juergen , Janssen Rainer 
Published: April 2020

SummaryThis article presents a mathematical infection model that is designed to estimate the course of coronavirus infection in Germany for several days in advance: How many people become ill or die, what is the temporal development? If the contact restriction is perfect, then the model predicts the development of the virus infection after the initial subsidence of the infection. However, since this restriction cannot always be strictly adhered to, the model is dynamically adapted to the development. This makes it possible to estimate the number of infected people, the number of new infections and deaths in Germany about a week in advance.
COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms
Authors: Punn Narinder Singh , Sonbhadra Sanjay Kumar , Agarwal Sonali 
Published: April 2020

AbstractThe catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.
Assessing the Hospital Surge Capacity of the Kenyan Health System in the Face of the COVID-19 Pandemic
Authors: Barasa Edwine , Ouma Paul , Okiro Emelda 
Published: April 2020

ABSTRACTIntroductionThe COVID-19 pandemic will test the capacity of health systems worldwide. Health systems will need surge capacity to absorb acute increases in caseload due to the pandemic. We assessed the capacity of the Kenyan health system to absorb surges in the number of people that will need hospitalization and critical care because of the COVID-19.MethodsWe assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments.ResultsThe capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19.ConclusionSignificant gaps exist in Kenya’s capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.
Prevalence of anxiety disorder among workers with cancer in Amman, Jordan
Authors: Abu Shreea Abdel Rahman Aref Ali , Khuan Lee , Syed Ismail Sharifah Norkhadijah , Subhi Nasrudin , Abu-Shanab Sobuh (Moh’d Sobhi) Ahmad , Mashrqi Zaher Juma’ Raja , Rasdi Irniza 
Published: April 2020

AbstractIntroductionCancer is now being recognised as a long term conditions due to advances in treatments that increase the survival rate of patients with cancer to as long as 10 years from the time of the disease. Anxiety is among the commonly discovered psychiatric illness in patients with cancer and is often neglected. Approximately 10% of patients with cancer are affected with anxiety worldwide. Hence, this study was aimed to determine the prevalence of anxiety disorder and its associated factors among workers with cancer in Jordanian population.MethodsA cross-sectional study was conducted at the King Husain Cancer Centre (KHCC) in Amman, Jordan. Proportional sampling technique was used to obtain the sample population of 355 workers with cancer. Data were collected through self-administered Generalized Anxiety Disorder (GAD-7) questionnaire and analyses were carried out using SPSS version 25.ResultsResponse rate of 100% were obtained from the participants. Prevalence of anxiety disorder among workers with cancer was recorded at 20.8% with male (23.1%) having the higher prevalence rate than female (17.3%) workers with cancers. A significant difference in anxiety between marital status (p=0.025), types of cancer (p=0.001), treatment types (p=0.024) were observed. A multiple regression was run to predict anxiety disorder from marital status, type of cancer and treatment types. These variables statistically significantly predicted anxiety disorder [F(3, 351) = 8.117, p < .001, R2 = 0.225].ConclusionThere is high prevalent of anxiety disorder among workers with cancer in Jordan. Predictors of anxiety among workers with cancer were also identified in this report.
Long-term predictions for COVID-19 pandemic dynamics in Ukraine, Austria and Italy
Authors: Nesteruk Igor 
Published: April 2020

ABSTRACTThe SIR (susceptible-infected-removed) model, statistical approach to the parameter identification and the official WHO daily data about the confirmed cumulative number of cases were used to make some estimations for the dynamics of the coronavirus pandemic dynamics in Ukraine, Italy and Austria. The volume of the data sets and the influence of the information about the initial stages of the epidemics were discussed in order to have reliable long-time predictions. The final sizes and durations for the pandemic in these countries are estimated.
Is the spread of COVID-19 across countries influenced by environmental, economic and social factors?
Authors: Hossain Mohammad Alamgir 
Published: April 2020

AbstractThe SARS-CoV-2 virus, emerged from Wuhan, China is spreading all over the world in an unprecedented manner, causing millions of infections and thousands of deaths. However, the spread of the disease across countries and regions are not even. Why some countries and regions are more affected than some other countries and regions? We employ simple statistical methods to investigate any linkage between the severity of the disease and the environmental, economic and social factors of countries. The estimation results indicate that the number of confirmed cases of Coronavirus infection is higher in countries with lower yearly average temperatures, higher economic openness and stronger political democracy. However, findings of this analysis should be interpreted carefully keeping in mind the fact that statistical relations do not necessarily imply causation. Only clinical experiments with medical expertise can confirm how the virus behaves in the environment.
U.S. county-level characteristics to inform equitable COVID-19 response
Authors: Chin Taylor , Kahn Rebecca , Li Ruoran , Chen Jarvis T. , Krieger Nancy , Buckee Caroline O. , Balsari Satchit , Kiang Mathew V. 
Published: April 2020

AbstractBackgroundThe spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individual’s susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy.Methods and FindingsUsing publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context.Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage.The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds.ConclusionCOVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities’ attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.
Work-related Covid-19 transmission
Authors: Lan Fan-Yun , Wei Chih-Fu , Hsu Yu-Tien , Christiani David C. , Kales Stefanos N. 
Published: April 2020

AbstractImportanceOur study helps fill the knowledge gap related to work-related transmission in the emerging coronaviral pandemic.ObjectiveTo demonstrate high-risk occupations for early coronavirus disease 2019 (Covid-19) local transmission.MethodsIn this observational study, we extracted confirmed Covid-19 cases from governmental investigation reports in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam. We followed each country/area for 40 days after its first locally transmitted case, and excluded all imported cases. We defined a possible work-related case as a worker with evidence of close contact with another confirmed case due to work, or an unknown contact history but likely to be infected in the working environment (e.g. an airport taxi driver). We calculated the case number for each occupation, and illustrated the temporal distribution of all possible work-related cases and healthcare worker (HCW) cases. The temporal distribution was further defined as early outbreak (the earliest 10 days of the following period) and late outbreak (11th to 40th days of the following period).ResultsWe identified 103 possible work-related cases (14.9%) among a total of 690 local transmissions. The five occupation groups with the most cases were healthcare workers (HCWs) (22%), drivers and transport workers (18%), services and sales workers (18%), cleaning and domestic workers (9%) and public safety workers (7%). Possible work-related transmission played a substantial role in early outbreak (47.7% of early cases).Occupations at risk varied from early outbreak (predominantly services and sales workers, drivers, construction laborers, and religious professionals) to late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, police officers, and religious professionals).ConclusionsWork-related transmission is considerable in early Covid-19 outbreaks, and the elevated risk of infection was not limited to HCW. Implementing preventive/surveillance strategies for high-risk working populations is warranted.
A Comprehensive Analysis of COVID-19 Outbreak situation in India
Authors: Gupta Rajan , Pal Saibal K. , Pandey Gaurav 
Published: April 2020

AbstractThe outbreak of COVID-19 in different parts of the world is a major concern for all the administrative units of respective countries. India is also facing this very tough task for controlling the virus outbreak and has managed its growth rate through some strict measures. This study presents the current situation of coronavirus spread in India along with the impact of various measures taken for it. With the help of data sources (till 7th-8th April 2020) from various state units of India and Ministry of Health and Family Welfare, Government of India, this study presents various trends and patterns. This study answers six different research questions in a comprehensive manner. It has been reported that growth rate of infected cases has been controlled with the help of National Lockdown, however some uncontrolled mass level events had a negative impact on the infected cases. With the help of exponential and polynomial regression modelling, the predictions of up to 75000 cases have been done by the end of April 2020. It has also been seen that there are some prominent clusters and patient nodes in the network of patients which are the major influencers for COVID-19 spread. Also, death rate case predictions have been done through two-class classification models with an accuracy of 60%. At the end, strategies for continuation for lockdown has been discussed and presented. It appears that only essential services should be open for the citizens of India and the national lockdown should be carried on for next 2-4 weeks. This study will be useful for the Government of India and various states of India, Administrative Units of India, Frontline health workforce of India, researchers and scientists. This study will also be favorable for the administrative units of other countries to consider various aspects related to the control of COVID-19 outspread in their respective regions.
Through the Eyes of a Patient: Visuospatial Perspective Taking and Empathy in Medical Students
Authors: Bukowski Henryk , Ahmad Kamal Nor Faizaah , Bennett Deirdre , Rizzo Gabriella , O’Tuathaigh Colm M.P. 
Published: April 2020

ABSTRACTPhysicians’ cognitive empathy is associated with improved diagnosis and better patient outcomes. The relationship between self-reported and performance-based measures of cognitive empathic processes is unclear. This study examined the association between medical students’ empathy scale scores and their empathic performance in a perspective-taking task and communication skills assessment. Undergraduate medical students (N=91) completed the following: Jefferson Scale of Physician Empathy (JSPE); Empathy Quotient (EQ); a Level-1 visual perspective-taking task (VPT). Clinical communication skills were measured in a simulation-enhanced ‘breaking bad news’ assessment. Pearson’s or Spearman’s correlation coefficients and t-tests were used to determine correlation and group differences, respectively. Higher scores on the “Standing in Patients’ Shoes” sub-scale of the JSPE were associated with lower egocentric bias (r = -0.299, p < 0.05) in the VPT, which relates to capacity to adopt someone else’s perspective without being erroneously influenced by our own point of view. Additionally, a lower self-perspective advantage in the VPT, reflecting less attentional priority given to the self-perspective, was associated with higher scores on the the “Cognitive Empathy” (r = -0.283, p < 0.05) and “Emotional Empathy” (r = -0.342, p < 0.01) sub-scales of the EQ. Improved communication skills performance was associated with higher scores on the “Social Skills” EQ sub-scale (r = 0.298, p < 0.005). We show that self-assessment scores are moderately associated with performance-based measures of perspective-taking and communication skills. These results are expected to lead to improved experimental designs and a better understanding of empathy in medical education.
Biological aging measures based on blood DNA methylation and risk of cancer: a prospective study
Authors: Dugué Pierre-Antoine , Bassett Julie K , Wong Ee Ming , Joo JiHoon E , Li Shuai , Yu Chenglong , Schmidt Daniel F , Makalic Enes , Doo Nicole Wong , Buchanan Daniel D , Hodge Allison M , English Dallas R , Hopper John L , Giles Graham G , Southey Melissa C , Milne Roger L 
Published: April 2020

ABSTRACTWe previously investigated the association between five ‘first-generation’ measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. The present study assesses cancer risk associations for three recently developed methylation-based measures of aging:PhenoAge, GrimAge, and predicted telomere length. We estimated rate ratios (RRs) for risk of colorectal (N=814), gastric (N=166), kidney (N=139), lung (N=327), mature B-cell (N=426), prostate (N=847) and urothelial (N=404) cancer, using conditional logistic regression models. We observed relatively strong associations ofPhenoAgewith risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per standard deviation ∼ 1.2-1.3). Similar findings were obtained forGrimAge, but the association with lung cancer risk was remarkably stronger (RR ∼ 1.8 after adjustment for smoking status, pack-years, starting age, time since quitting and other cancer risk factors). The methylation-based measuresPhenoAgeandGrimAgemay provide insights into the relationship between biological aging and cancer.
Deep learning discerns cancer mutation exclusivity
Authors: Gupta Prashant , Jindal Aashi , Jayadeva  , Sengupta Debarka 
Published: April 2020

ABSTRACTThe exclusivity of a vast majority of cancer mutations remains poorly understood, despite the availability of large amounts of whole genome and exome sequencing data. In clinical settings, this markedly hinders the identification of the previously uncharacterized deleterious mutations due to the unavailability of matched normal samples. We employed state of the art deep learning algorithms for cross-exome learning of mutational embeddings and demonstrated their utility in sequence based detection of cancer-specific Single Nucleotide Variants (SNVs).
Maternal exposure to the cannabinoid agonist WIN 55,12,2 during lactation induces lasting behavioral and synaptic alterations in the rat adult offspring of both sexes
Authors: Scheyer Andrew F , Borsoi Milene , Pelissier-Alicot Anne-Laure , Manzoni Olivier JJ 
Published: April 2020

AbstractConsumption of cannabis during pregnancy and the lactation period is a rising public health concern (Scheyer et al., 2019). We have previously shown that exposure to synthetic or plant-derived cannabinoids via lactation disrupts perinatal programming of the gamma-aminobutyric acid trajectory in the prefrontal cortex and early-life behaviors (Scheyer et al., 2020b). Recently, we described lasting behavioral and neuronal consequences of Δ9-tetrahydrocannabinol (THC) perinatal exposure via lactation (Scheyer et al., 2020a).Here, we extend upon these findings by testing the effects in offspring of maternal exposure to the synthetic cannabinoid agonist WIN 55,12,2 (WIN). The data demonstrate that rats exposed during lactation to WIN display social, cognitive and motivational deficits at adulthood. These behavioral changes were paralleled by a specific loss of endocannabinoid-mediated long-term depression in the prefrontal cortex and nucleus accumbens, while other forms of synaptic plasticity remained intact. Thus, similarly to THC, perinatal WIN exposure via lactation induces behavioral and synaptic abnormalities lasting into adulthood.