bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 Growth Rate Decreases with Social Capital
Authors: Varshney Lav R. , Socher Richard 
Published: April 2020

AbstractBackgroundSocial capital has been associated with many public health variables including mortality, obesity, diabetes, and sexually-transmitted disease rates. However, the relationship of social capital to the spread of infectious disease like COVID-19 is lacking. The COVID-19 pandemic presents an unprecedented threat to global health and economy, for which control strategies have relied on aggressive social distancing. However, an understanding of how social capital is related to changes in human mobility patterns for adherence to social distancing is lacking.ObjectiveThis study examines the association between state- and county-level social capital indices and community health indices in the United States, and the growth rate of COVID-19 cases. It also examines changes in human mobility.MethodsUsing publicly available state- and county-specific time series data for COVID-19 cases from March 13 to March 31, we used exponential fits to determine growth rate. We obtained publicly available mobility change data, originally measured from GPS-enabled mobile devices. The design was then state- and county-level correlation analysis with social capital and community health indices from the Social Capital Project (United States Senate).ResultsIn bivariate linear correlation analyses, we find social capital and community health indices were negatively associated with COVID-19 growth rates at both the state and county levels. The correlation was strongest at the county level for the community health index: a one-unit increase in the county community health index was associated with a decrease in the COVID-19 growth rate exponent by 0.045. In further bivariate correlation analyses, we find that social capital indices were negatively associated with retail/recreation movement and positively associated with residential movement. That is, an increase in social capital is correlated with slower COVID-19 infection spread and more adherence to social distancing protocols.ConclusionOur results indicate the potential benefit of incorporating social capital concepts in planning policies to control the spread of COVID-19, e.g. different social distancing requirements in different communities. The results also indicate a need for further research into this potentially causal relationship, including examining interventions to increase social capital, community health, and institutional health.
Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil
Authors: de Almeida G.B. , Vilches T.N. , Ferreira C.P. , Fortaleza C.M.C.B. 
Published: April 2020

ABSTRACTEarly 2020 and the world experiences its very first pandemic of globalized era. A novel coronavirus, SARS-Cov-2, is the causative agent of severe pneumonia and rapidly spread through many nations, crashing health systems. In Brazil, the emergence of local epidemics in major metropolitan areas is a concern. In a huge and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for an inner Brazil and what can we do to control infection transmission in each one of these locations? In this paper, a mathematical model was developed to simulate disease transmission among individuals in several scenarios, differing by the intensity and type of control measures. Mitigation strategies rely on social distancing of all individuals, and detection and isolation of infected ones. The model shows that control effort varies among cities. The social distancing is the most efficient method to control disease transmission but improving detection and isolation of infected individuals can help loosening this mitigation strategy.
Assessing the community risk perception toward COVID-19 outbreak in South Korea: evidence from Google and NAVER relative search volume
Authors: Husnayain Atina , Shim Eunha , Fuad Anis , Su Emily Chia-Yu 
Published: April 2020

AbstractThis study aimed to explore the patterns of community health risk perception of coronavirus disease 2019 (COVID-19) in South Korea using Internet search data. Google and NAVER relative search volume data were collected using COVID-19-related terms in Korean language. Online queries were compared with the number of new COVID-19 cases and tests. Time series trends and Spearman’s rank correlation coefficients showed that the number of COVID-19-related queries in South Korea increased during the local and international events; higher in women, certain age groups; and higher in affected areas, which represented the community health risk perception. Greater correlations were found in mobile searches compared to that of desktop searches, indicating the changing behavior in searching health online information. The use of both Google and NAVER RSV to explore the patterns of community health risk perception could be beneficial for targeting risk communication in several perspectives including time, population characteristics, and location.Article Summary LineThe use of both Google and NAVER RSV to explore the patterns of community health risk perception toward COVID-19 in South Korea could be beneficial for targeting risk communication in several perspectives including time, population characteristics, and location.
Scalable In-hospital Decontamination of N95 Filtering Facepiece Respirator with a Peracetic Acid Room Disinfection System
Authors: John Amrita R. , Raju Shine , Cadnum Jennifer L. , Lee Kipum , McClellan Phillip , Akkus Ozan , Miller Sharon K. , Jennings Wayne , Buehler Joy A. , Li Daniel F. , Redmond Sarah N. , Braskie Melissa , Hoyen Claudia K. , Donskey Curtis J. 
Published: April 2020

AbstractBackgroundCritical shortages of personal protective equipment (PPE) especially N95 respirators, during the SARS-CoV-2 pandemic continues to be a source of great concern among health care workers (HCWs). Novel methods of N95 filtering facepiece respirator (FFR) decontamination that can be scaled-up for in-hospital use can help address this concern and keep HCWs safe.MethodsA multidisciplinary pragmatic study was conducted to evaluate the use of an ultrasonic room high-level disinfection system (HLDS) that generates aerosolized peracetic acid (PAA) and hydrogen peroxide for decontamination of large numbers of N95 respirators. A cycle duration that consistently achieved disinfection of N95 respirators (defined as ≤ 6 log10 reductions in bacteriophage MS2 andGeobacillus stearothermophilusspores inoculated onto respirators) was identified. The treated masks were then assessed for changes to their hydrophobicity, material structure, strap elasticity, and filtration efficiency (FE). Assessment of PAA off-gassing from a treated mask was also performed.ResultsThe PAA room HLDS was effective for disinfection of N95 respirators in a 2447 cubic feet room with deploy and dwell times of 16 and 32 minutes respectively, and a total cycle time of 1 hour and 16 minutes. After 5 treatment cycles, no adverse effects were detected on filtration efficiency, structural integrity, or strap elasticity. There was no detectable off-gassing of PAA from the treated masks.ConclusionThe PAA room disinfection system provides a rapidly scalable solution for in-hospital decontamination of large numbers of N95 respirators to meet the needs of HCWs during the SARS-CoV-2 pandemic.
The role of spatial structure in the infection spread models: population density map of England example
Authors: Mashanov Gregory , Mashanova Alla 
Published: April 2020

AbstractIn the current situation of a pandemic caused by COVID-19 developing models accurately predicting the dynamics of the outbreaks in time and space became extremely important.Individual-based models (IBM) simulating the spread of infection in a population have a few advantages compared to classical equation-based approach. First, they use individuals as units, which represent the population, and reflect the local variations happening in real life. Second, the simplicity of modelling the interactions between the individuals, which may not be the case when using differential equations.We propose to use freely available population density maps to simulate the infection spread in the human population on the scale of an individual country or a city. We explore the effect of social distancing and show that it can reduce the outbreak when applied before or during peak time, but it can also inflict the second wave when relaxed after the peak. This can be explained by a large proportion of susceptible individuals, even in the large cities, after the first wave.The model can be adapted to any spatial scale from a single hospital to multiple countries.
Hypertension and Renin–Angiotensin–Aldosterone System Inhibitors in Patients with Covid-19
Authors: Ip Andrew , Parikh Kaushal , Parrillo Joseph E. , Mathura Shivam , Hansen Eric , Sawczuk Ihor S. , Goldberg Stuart L. 
Published: April 2020

ABSTRACTIntroductionCOVID-19 disproportionately affects those with comorbidities and the elderly. Hypertension is the most common pre-existing condition amongst COVID-19 patients. Upregulation of the renin-angiotensin-aldosterone system (RAAS) is common in hypertensive patients and may promote inflammation and ensuing cytokine storm in COVID-19. It is unknown whether RAAS inhibition with ACE1 inhibitors or angiotensin-receptor blockers (ARB) can be harmful or beneficial.MethodsWithin Hackensack Meridian Health network, the largest healthcare provider in New Jersey, we performed a retrospective, multicenter, convenience sampling study of hospitalized COVID-19 patients. Demographics, clinical characteristics, treatments, and outcomes were manually abstracted. Fishers exact tests, and logistic regression were performed.ResultsAmong 3017 hospitalized COVID-19 patients, 1584 (52.5%) carried a diagnosis of hypertension. In the discharged or deceased cohort, the overall mortality was significantly increased at 35% vs 13% among COVID-19 patients with hypertension. However, when adjusted for age, the effect of hypertension on mortality was greatly diminished, with a reduction in odds-ratio by over half; and completely disappeared when adjusted for other major covariates. The mortality rates were lower for hypertensive patients prescribed ACE1 (27%, p=0.001) or ARBs (33%, p=0.12) compared to other anti-hypertensive agents (39%) in the unadjusted analyses. RAAS inhibitor therapy appeared protective compared to other anti-hypertensive agents (p=0.001).ConclusionsWhile our results are limited by the retrospective nature of our study and by potential confounders, our data argue against a harmful effect of RAAS inhibition and support the HFSA/AHA/ACC joint statement recommending continuing ACE1 and ARB therapy in hypertensive COVID-19 patients.
Early detection of in-patient deterioration: one prediction model does not fit all
Authors: Blackwell Jacob N. , Keim-Malpass Jessica , Clark Matthew T. , Kowalski Rebecca L. , Najjar Salim N. , Bourque Jamieson M. , Lake Douglas E. , Moorman J. Randall 
Published: April 2020

AbstractObjectivesEarly detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score (NEWS) will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models.DesignIndividual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; logistic regression modeling for the outcome of ICU transfer for a specific clinical reason.SettingCardiac medical-surgical ward; tertiary care academic hospital.Patients8111 adult patients, 457 of whom were transferred to an ICU for clinical deterioration.InterventionsNone.Measurements and main resultsWe calculated the contributing relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate ROC areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons – respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy – had distinct signatures of illness. Statistical models trained to target specific reasons for ICU transfer performed better than one model targeting combined events, and both performed better than the untrained NEWS score.Conclusions and relevanceA single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.
A Novel Protein Drug, Novaferon, as the Potential Antiviral Drug for COVID-19
Authors: Zheng Fang , Zhou Yanwen , Zhou Zhiguo , Ye Fei , Huang Baoying , Huang Yaxiong , Ma Jing , Zuo Qi , Tan Xin , Xie Jun , Niu Peihua , Wang Wenlong , Xu Yun , Peng Feng , Zhou Ning , Cai Chunlin , Tang Wei , Xiao Xinqiang , Li Yi , Zhou Zhiguang , Jiang Yongfang , Xie Yuanlin , Tan Wenjie , Gong Guozhong 
Published: April 2020

AbstractBackgroundNovaferon, a novel protein drug approved for the treatment of chronic hepatitis B in China, exhibits potent antiviral activities. We aimed to determine the anti-SARS-CoV-2 effects of Novaferon in vitro, and conducted a randomized, open-label, parallel group study to explore the antiviral effects of Novaferon for COVID-19.MethodsIn laboratory, the inhibition of Novaferon on viral replication in cells infected with SARS-CoV-2, and on SARS-CoV-2 entry into healthy cells was determined. Antiviral effects of Novaferon were evaluated in COVID-19 patients with treatment of Novaferon, Novaferon plus Lopinavir/Ritonavir, or Lopinavir/Ritonavir. The primary endpoint was the SARS-CoV-2 clearance rates on day 6 of treatment, and the secondary endpoint was the time to the SARS-CoV-2 clearance in COVID-19 patientsResultsNovaferon inhibited the viral replication in infected cells (EC50=1.02 ng/ml), and protected healthy cells from SARS-CoV-2 infection (EC50=0.1 ng/ml). Results from the 89 enrolled COVID-19 patients showed that both Novaferon and Novaferon plus Lopinavir/Ritonavir groups had significantly higher SARS-CoV-2 clearance rates on day 6 than the Lopinavir/Ritonavir group (50.0% vs.24.1%, p = 0.0400, and 60.0% vs.24.1%, p = 0.0053). Median time to SARS-CoV-2 clearance were 6 days, 6 days, and 9 days for three groups respectively, suggesting a 3-dayreduction of time to SARS-CoV-2 clearance in both Novaferon and Novaferon plus Lopinavir/Ritonavir groups compared with Lopinavir/Ritonavir group.ConclusionsNovaferon exhibited anti-SARS-CoV-2 effects in vitro and in COVID-19 patients. These data justified the further evaluation of Novaferon.
COVID-19 in India: State-wise Analysis and Prediction
Authors: Ghosh Palash , Ghosh Rik , Chakraborty Bibhas 
Published: April 2020

AbstractCoronavirus disease 2019 (COVID-19), a highly infectious disease, was first detected in Wuhan, China, in December 2019. The disease has spread to 212 countries and territories around the world and infected (confirmed) more than three million people. In India, the disease was first detected on 30 January 2020 in Kerala in a student who returned from Wuhan. The total (cumulative) number of confirmed infected people is more than 37000 till now across India (3 May 2020). Most of the research and newspaper articles focus on the number of infected people in the entire country. However, given the size and diversity of India, it may be a good idea to look at the spread of the disease in each state separately, along with the entire country. For example, currently, Maharashtra has more than 10000 confirmed cumulative infected cases, whereas West Bengal has less than 800 confirmed infected cases (1 May 2020). The approaches to address the pandemic in the two states must be different due to limited resources. In this article, we will focus the infected people in each state (restricting to only those states with enough data for prediction) and build three growth models to predict infected people for that state in the next 30 days. The impact of preventive measures on daily infected-rate is discussed for each state.Highlights of the AnalysisData considered for analysis: up to 1 May 2020.One model can mislead us. Here, we consider the exponential, the logistic and the SIS models along with daily infection-rate (DIR). We interpret the results jointly from all models rather than individually.We expect DIR to be zero or negative to conclude that COVID-19 is not spreading in a state. Even a small positive DIR (say 0.01) indicates virus is spreading in the community. The virus can potentially increase the DIR anytime.Severe: The states without a decreasing trend in DIR and near exponential growth in active infected cases are Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal.Moderate: The states with an almost decreasing trend in DIR and non-increasing growth in active infected cases are Tamil Nadu, Rajasthan, Punjab and Bihar.Controlled: The states with a decreasing trend in DIR and decreasing growth in active infected cases in the last few days are Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana.States with non-decreasing DIR need to do much more in terms of the preventive measures immediately to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR become zero or negative for consecutive 14 days to be able to declare the end of the pandemic.
Sequential informed pooling approach to detect SARS-CoV2 infection
Authors: Millioni Renato , Mortarino Cinzia 
Published: April 2020

AbstractThe alarming spread of the pandemic coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is requiring several measures to reduce the risk of contagion. Every successful strategy in controlling SARS-CoV2 infection depends on the timely viral diagnosis which should include asymptomatic carriers. Consequently, strategies to increase the throughput for clinical laboratories to conduct large-scale diagnostic testing are urgently needed. Here we support the hypothesis that standard diagnostic protocol for SARS-CoV-2 virus could be conveniently applied to pooled samples obtained from different subjects. We suggest that a two-step sequential pooling procedure could identify positive subjects, ensuring at the same time significant benefits of costs and time. Simulation data are used to assess the efficiency, in terms of number of required tests, both for random assignment of the subjects to the pools and for situations when epidemiological and clinical data are used to create an “informed” version of the pooling. Different scenarios are examined in the simulations to measure the effect of different pool sizes and different values for the virus frequency. Our results allow to customize the pooling strategy according to the specific characteristics of the cohort to be tested.
Extensions of the SEIR Model for the Analysis of Tailored Social Distancing and Tracing Approaches to Cope with COVID-19
Authors: Grimm Veronika , Mengel Friederike , Schmidt Martin 
Published: April 2020

AbstractIn the context of the COVID-19 pandemic, governments worldwide face the challenge of designing tailored measures of epidemic control to provide reliable health protection while allowing societal and economic activity. In this paper, we propose an extension of the epidemiological SEIR model to enable a detailed analysis of commonly discussed tailored measures of epidemic control—among them group-specific protection and the use of tracing apps. We introduce groups into the SEIR model that may differ both in their underlying parameters as well as in their behavioral response to public health interventions. Moreover, we allow for different infectiousness parameters within and across groups, different asymptomatic, hospitalization, and lethality rates, as well as different take-up rates of tracing apps. We then examine predictions from these models for a variety of scenarios. Our results visualize the sharp trade-offs between different goals of epidemic control, namely a low death toll, avoiding overload of the health system, and a short duration of the epidemic. We show that a combination of tailored mechanisms, e.g., the protection of vulnerable groups together with a “trace & isolate” approach, can be effective in preventing a high death toll. Protection of vulnerable groups without further measures requires unrealistically strict isolation. A key insight is that high compliance is critical for the effectiveness of a “trace & isolate” approach. Our model allows to analyze the interplay of group-specific social distancing and tracing also beyond our case study in scenarios with a large number of groups reflecting, e.g., sectoral, regional, or age differentiation and group-specific behavioral responses.
Countries are Clustered but Number of Tests is not Vital to Predict Global COVID-19 Confirmed Cases: A Machine Learning Approach
Authors: Rahaman Khan Md Hasinur , Hossain Ahmed 
Published: April 2020

AbstractCOVID-19 disease is a global pandemic and it appears as pandemic for each and every nation and territory in the earth.This paper focusses on analysing the global COVID-19 data by popular machine learning techniques to know which covariates are importantly associated with the cumulative number of confirmed cases, whether the countries are clustered with respect to the covariates considered, whether the variation in the covariates are explained by any latent factor. Regression tree, cluster analysis and principal component analysis are implemented to global COVID-19 data of 133 countries obtained from the worldometer website as reported as on April 17, 2020. Our results suggest that there are four major clusters among the countries. First cluster consists of 8 countries where cumulative infected cases and deaths are highest. It is also revealed that there are two principal components. The countries which play vital role to explain the 60% variation of the total variations by the first component characterized by all variables except the rate variables include USA, Spain, Italy, France, Germany, UK, and Iran. Remaining countries contribute to explaining 20% variation of the total variations by the second component characterized by only three rate variables. We also found that the number of tests by the country variable among other variables country, number of active cases, number of deaths, number of recovered patients, number of serious cases, and number of new cases is an unimportant variable to predict cumulative number of confirmed cases. Hence, the number of tests might play vital role to individual country level who are in the primary level of virus spread but not to the global level.