bluuebunny/crossref_metadata_2025_split huggingface dataset:
An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak
Authors: Medford Richard J. , Saleh Sameh N. , Sumarsono Andrew , Perl Trish M. , Lehmann Christoph U. 
Published: April 2020

AbstractBackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about Coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment has evolved in the early stages of any outbreak, including the COVID-19 epidemic, has not been described.ObjectiveTo quantify and understand early changes in Twitter activity, content, and sentiment about the COVID-19 epidemic.DesignObservational study.SettingTwitter platform.ParticipantsAll Twitter users who created or sent a message from January 14th to 28th, 2020.MeasurementsWe extracted tweets matching hashtags related to COVID-19 and measured frequency of keywords related to infection prevention practices, vaccination, and racial prejudice. We performed a sentiment analysis to identify emotional valence and predominant emotions. We conducted topic modeling to identify and explore discussion topics over time.ResultsWe evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. The frequency of racially charged tweets closely paralleled the number of newly diagnosed cases of COVID-19. The economic and political impact of the COVID-19 was the most commonly discussed topic, while public health risk and prevention were among the least discussed.ConclusionTweets with negative sentiment and emotion parallel the incidence of cases for the COVID-19 outbreak. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and target public health messages based on user interest and emotion.FundingNone.
Autocatalytic Model for Covid-19 Progression in a Country
Authors: Chernyshev Anatoly 
Published: April 2020

AbstractHerewith we present a computational model for the forecasting of cumulative diagnosed cases of Covid-19 pneumonia within a country. The only explicit parameter of the model is the population density. The implicit parameter is a moving average ambient temperature, currently integrated into the kinetic constants. Other finer details pertaining to the mechanism of the pathogen SARS-CoV-2 spread within a given region are implicitly manifested in the exponent parameters derived from the non-linear fitting of the published data on Covid-19 occurrence. The performance of the model is demonstrated on a few selected countries, and on the Diamond Princess cruising ship outbreak. The model might be used as an aiding tool for the policy makers regarding the decisions on the containment measures and quarantine regime required.
COVID-19 diagnosis prediction in emergency care patients: a machine learning approach
Authors: de Moraes Batista André Filipe , Miraglia João Luiz , Rizzi Donato Thiago Henrique , Porto Chiavegatto Filho Alexandre Dias 
Published: April 2020

AbstractThe coronavirus disease (COVID-19) pandemic has increased the necessity of immediate clinical decisions and effective usage of healthcare resources. Currently, the most validated diagnosis test for COVID-19 (RT-PCR) is in shortage in most developing countries, which may increase infection rates and delay important preventive measures. The objective of this study was to predict the risk of positive COVID-19 diagnosis with machine learning, using as predictors only results from emergency care admission exams. We collected data from 235 adult patients from the Hospital Israelita Albert Einstein in São Paulo, Brazil, from 17 to 30 of March, 2020, of which 102 (43%) received a positive diagnosis of COVID-19 from RT-PCR tests. Five machine learning algorithms (neural networks, random forests, gradient boosting trees, logistic regression and support vector machines) were trained on a random sample of 70% of the patients, and performance was tested on new unseen data (30%). The best predictive performance was obtained by the support vector machines algorithm (AUC: 0.85; Sensitivity: 0.68; Specificity: 0.85; Brier Score: 0.16). The three most important variables for the predictive performance of the algorithm were the number of lymphocytes, leukocytes and eosinophils, respectively. In conclusion, we found that targeted decisions for receiving COVID-19 tests using only routinely-collected data is a promising new area with the use of machine learning algorithms.
Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario
Authors: Hasan Neaz A. , Haque Mohammad Mahfujul 
Published: April 2020

AbstractThe spread of COVID-19 engulfs almost all the countries and territories of the planet, and infections and fatality are increasing rapidly. The first epi-center of its’ massive spread was in Wuhan, Hubei province, China having a temperate weather, but the spread has got an unprecedented momentum in European temperate countries mainly in Italy and Spain (as of March 30, 2020). However, Malaysia and Singapore and the neighboring tropical countries of China got relatively low spread and fatality that created a research interest on whether there are potential impacts of weather condition on COVID-19 spread. Adopting the SIR (Susceptible Infected Removed) deviated model to predict potential cases and death in the coming days from COVID-19 was done using the secondary and official sources of data. This study shows that COVID-19 spread and fatality tend to be high across the world but compared to tropical countries, it is going to be incredibly high in the temperate countries having lower temperature (7-16°C) and humidity (80-90%) in last March. However, some literature predicted that this might not to be true, rather irrespective of weather conditions there might be a continuous spread and death. Moreover, a large number of asymptotic COVID-19 carrier in both temperate and tropical countries may re-outbreak in the coming winter. Therefore, a comprehensive global program with the leadership of WHO for testing of entire population of the world is required, which will be very useful for the individual states to take proper political action, social movement and medical services.
Community responses during early phase of the COVID-19 epidemic: a cross-sectional study
Authors: Delshad Mohammad Hossein , Pourhaji Fatemeh , Pourhaji Fahimeh , Ghanbarizadeh Saeed Reza , Zarmehri Hassan Azhdari , Bazrafshan Edris , Gholian-Aval Mahdi 
Published: April 2020

ABSTRACTCommunity responses are important for outbreak management during the early phase when preventive interventions are the major options. Therefore, this study aims to examine the behavioral responses of the community during the early phase of the COVID-19 epidemic in the Razavi Khorasan Province of Iran. A cross-sectional online survey was proceeded after confirmed COVID-19 in Iran. The population of the study was 500 residents of Razavi Khorasan areas were randomly surveyed. The research tool was demographic and risk perception questionnaire and Anxiety was assessed using the 7-item GAD Scale. The data analyzed using the SPSS statistical version (V.20). The means of age participants was 31.9±11.9. The mean GAD-7 scores were 6.4±5.2 and 92.4% had moderate or severe anxiety (GAD-7 score ≥10). Many respondents reported their health status were very good or good (62.2 %; 311/500). About a quarter of them had respiratory symptoms in the past 14 days and experienced 20% of them travelled outside the Razavi Khorasan Province in the previous. Risk perception toward COVID-19 in the community of the Razavi Khorasan Province was moderate. Most participants are alert to disease progression. This study suggested timely behavioral assessment of the community is beneficial and effective to inform next intervention, and risk communication strategies in epidemic disease.
COVID-19 higher induced mortality in Chinese regions with lower air quality
Authors: Pansini Riccardo , Fornacca Davide 
Published: April 2020

AbstractCOVID-19 has spread in all continents in a span of just over three months, escalating into a pandemic that poses several humanitarian as well as scientific challenges. We here investigated the geographical expansion of the infection and correlate it with the annual indexes of air quality observed from the Sentinel-5 satellite orbiting around China, Italy and the U.S.A. Controlling for population size, we find more viral infections in those areas afflicted by Carbon Monoxide (CO) and Nitrogen Dioxide (NO2). Higher mortality was also correlated with poor air quality, namely with high PM2.5, CO and NO2 values. In Italy, the correspondence between poor air quality and SARS-CoV-2 appearance and induced mortality was the starkest. Similar to smoking, people living in polluted areas are more vulnerable to SARS-CoV-2 infections and induced mortality. This further suggests the detrimental impact climate change will have on the trajectory of future epidemics.SignificanceWe found a significant correlation between levels of air quality and COVID-19 spread and mortality in China, Italy and the United States. Despite the infection being still ongoing at a global level, these correlations are relatively robust not being influenced by varying population densities. Living in an area with low air quality seems to be a risk factor for becoming infected and dying from this new form of coronavirus.
Neural basis of common-pool resources exploitation
Authors: Martinez-Saito Mario , Andraszewicz Sandra , Klucharev Vasily , Rieskamp Jörg 
Published: April 2020

AbstractWhy do people often exhaust unregulated common (shared) natural resources but manage to preserve similar private resources? To answer this question, in the present work we combine a neurobiological, economic, and cognitive modeling approach. Using functional magnetic resonance imaging on 50 participants, we show that sharp depletion of common and private resources is associated with deactivation of the ventral striatum, a brain region involved in the valuation of outcomes. Across individuals, when facing a common resource, ventral striatal activity is anti-correlated with resource preservation (less harvesting), whereas with private resources the opposite pattern is observed. This indicates that neural value signals distinctly modulate behavior in response to the depletion of common versus private resources. Computational modeling suggest that over-harvesting of common resources is facilitated by the modulatory effect of social comparison on value signals. In sum, the results provide an explanation of people’s tendency to over-exploit unregulated common natural resources.
Load adaptation of endocytic actin networks
Authors: Kaplan Charlotte , Kenny Sam J. , Chen Xuyan , Schöneberg Johannes , Sitarska Ewa , Diz-Muñoz Alba , Akamatsu Matthew , Xu Ke , Drubin David G. 
Published: April 2020

AbstractClathrin-mediated endocytosis (CME) robustness under elevated membrane tension is maintained by actin assembly-mediated force generation. However, whether more actin assembles at endocytic sites in response to increased load, as has been observed in lamellipodia, has not previously been investigated. Here actin network ultrastructure at CME sites was examined under low and high membrane tension. Actin and N-WASP spatial organization indicate that actin polymerization initiates at the base of clathrin-coated pits and that the network then grows away from the plasma membrane. Actin network height at individual CME sites was not coupled to coat shape, raising the possibility that local differences in mechanical load feedback on assembly. By manipulating membrane tension and Arp2/3 complex activity we tested the hypothesis that actin assembly at CME sites increases in response to elevated load. Indeed, in response to elevated membrane tension, actin grew higher, resulting in greater coverage of the clathrin coat, and CME slowed. When membrane tension was elevated and the Arp2/3 complex was inhibited, shallow clathrin-coated pits accumulated, indicating that this adaptive mechanism is especially crucial for coat curvature generation. We propose that actin assembly increases in response to increased load to ensure CME robustness over a range of plasma membrane tensions.
Optical Aberration Correction via Phase Diversity and Deep Learning
Authors: Krishnan Anitha Priya , Belthangady Chinmay , Nyby Clara , Lange Merlin , Yang Bin , Royer Loic A. 
Published: April 2020

AbstractIn modern microscopy imaging systems, optical components are carefully designed to obtain diffraction-limited resolution. However, live imaging of large biological samples rarely attains this limit because of sample induced refractive index inhomogeneities that create unknown temporally variant optical aberrations. Importantly, these aberrations are also spatially variant, thus making it challenging to correct over wide fields of view. Here, we present a framework for deep-learning based wide-field optical aberration sensing and correction. Our model consists of two modules which take in a set of three phase-diverse images and (i) estimate the wavefront phase in terms of its constituent Zernike polynomial coefficients and (ii) perform blind-deconvolution to yield an aberration-free image. First, we demonstrate our framework on simulations that incorporate optical aberrations, spatial variance, and realistic modelling of sensor noise. We find that our blind deconvolution achieves a 2-fold improvement in frequency support compared to input images, and our phase-estimation achieves a coefficient of determination (r2) of at least 80% when estimating astigmatism, spherical aberration and coma. Second, we show that our results mostly hold for strongly varying spatially-variant aberrations with a 30% resolution improvement. Third, we demonstrate practical usability for light-sheet microscopy: we show a 46% increase in frequency support even in imaging regions affected by detection and illumination scattering.
Comparing Metapopulation Dynamics of Infectious Diseases under Different Models of Human Movement
Authors: Citron Daniel T. , Guerra Carlos A. , Dolgert Andrew J. , Wu Sean L. , Henry John M. , Sánchez C Héctor M. , Smith David L. 
Published: April 2020

Newly available data sets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one’s choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the SIR model; the SIS model; and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model’s results, finding that in all cases there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions ofR0while the other produces nonsensical results.Significance StatementNewly available large-scale datasets of human population movement represent an opportunity to model how diseases spread between different locations. Combining infectious disease models with mechanistic models of host movement enables studies of how movement drives disease transmission and importation. Here we explore in what ways modeled epidemiological outcomes may be sensitive to the modeler’s choice of movement model structure. We use three different mathematical models of disease transmission to show how a model’s epidemiological predictions can change dramatically depending on the chosen host movement model. We find these different outcomes are robust to using the same data sources to parameterize each candidate model, which we illustrate using an example of real-world malaria transmission and importation in Bioko Island, Equatorial Guinea.
Explaining the “Bomb-Like” Dynamics of COVID-19 with Modeling and the Implications for Policy
Authors: Lin Gary , Strauss Alexandra T. , Pinz Maxwell , Martinez Diego A. , Tseng Katie K. , Schueller Emily , Gatalo Oliver , Yang Yupeng , Levin Simon A. , Klein Eili Y. ,   For the CDC MInD-Healthcare Program
Published: April 2020

AbstractUsing a Bayesian approach to epidemiological compartmental modeling, we demonstrate the “bomb-like” behavior of exponential growth in COVID-19 cases can be explained by transmission of asymptomatic and mild cases that are typically unreported at the beginning of pandemic events due to lower prevalence of testing. We studied the exponential phase of the pandemic in Italy, Spain, and South Korea, and found theR0to be 2.56 (95% CrI, 2.41-2.71), 3.23 (95% CrI, 3.06-3.4), and 2.36 (95% CrI, 2.22-2.5) if we use Bayesian priors that assume a large portion of cases are not detected. Weaker priors regarding the detection rate resulted inR0values of 9.22 (95% CrI, 9.01-9.43), 9.14 (95% CrI, 8.99-9.29), and 8.06 (95% CrI, 7.82-8.3) and assumes nearly 90% of infected patients are identified. Given the mounting evidence that potentially large fractions of the population are asymptomatic, the weaker priors that generate the highR0values to fit the data required assumptions about the epidemiology of COVID-19 that do not fit with the biology, particularly regarding the timeframe that people remain infectious. Our results suggest that models of transmission assuming a relatively lowerR0value that do not consider a large number of asymptomatic cases can result in misunderstanding of the underlying dynamics, leading to poor policy decisions and outcomes.
Estimating number of cases and spread of Coronavirus disease 2019 (COVID-19) in the United Kingdom using critical care admissions, February to March 2020
Authors: Jit Mark , Jombart Thibaut , Nightingale Emily S , Endo Akira , Abbott Sam ,   LSHTM Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Edmunds W John 
Published: April 2020

AbstractAn exponential growth model was fitted to critical care admissions from multiple surveillance databases to determine likely COVID-19 case numbers and growth in the United Kingdom from 16 February – 23 March 2020, after which a national lockdown occurred. We estimate that on 23 March, there were 102,000 (median; 95% credible interval 54,000 - 155,000) new cases and 320 (211 - 412) new critical care reports, with 464,000 (266,000 – 628,000) cumulative cases since 16 February.
A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York
Authors: Bliznashki Svetoslav 
Published: April 2020

AbstractWe use Bayesian Estimation for the logistic growth model in order to estimate the spread of the coronavirus epidemic in the state of New York. Models weighting all data points equally as well as models with normal error structure prove inadequate to model the process accurately. On the other hand, a model with larger weights for more recent data points and with t-distributed errors seems reasonably capable of making at least short term predictions.
On predicting the novel COVID-19 human infections by using Infectious Disease modelling method in the Indian State of Tamil Nadu during 2020
Authors: Ayubali Arsath Abbasali , Satheesh Sara Roshini 
Published: April 2020

AbstractSince the introduction of the novel Corona Virus (The COVID-19) to the Chinese city Wuhan in the Hubei province during the late December 2019, the effectiveness of the deadly disease, its human infection, spreading severity and the mortality rate of the infection has been an issue of debate. The outbreak of the virus along the time has become a massive threat to the global public health security and has been declared as a pandemic. Accounting the radical number of increases in the infected cases and the death due to COVID-19 infections around the globe, there is a need to predict the infections among the people by making proper optimization and using various Infectious Disease modelling (IDM) methods, in order to challenge the outcome. In comparison with previous diseases like SARS and Ebola viruses, the new corona virus (COVID-19) infections are infectious during the incubation period. In addition to that, naturally produced droplets from humans (e.g. droplets produced by breathing, talking, sneezing, coughing) and Person-to-person contact transmission are reported to be the foremost ways of transmission of novel corona virus. By considering the above two factors, a modified SEIR (Susceptibility-Exposure-Infection-Recovery) method have been used for predicting the spread of the infections in the state of Tamil Nadu which is located in the southern part of India. Further, we have utilized the current surveillance data from Health and Family Welfare Department – Government of Tamil Nadu to accurately predict the spreading trend of the infection on a state level.
Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model
Authors: Ghosh Pramit , Basheer Salah , Paul Sandip , Chakrabarti Partha , Sarkar Jit 
Published: April 2020

ABSTRACTObjectiveIn absence of any vaccine, the Corona Virus Disease 2019 (COVID-19) pandemic is being contained through a non-pharmaceutical measure termed Social Distancing (SD). However, whether SD alone is enough to flatten the epidemic curve is debatable. Using a Stochastic Computational Simulation Model, we investigated the impact of increasing SD, hospital beds and COVID-19 detection rates in preventing COVID-19 cases and fatalities.Research Design and MethodsThe Stochastic Simulation Model was built using the EpiModel package in R. As a proof of concept study, we ran the simulation on Kasaragod, the most affected district in Kerala. We added 3 compartments to the SEIR model to obtain a SEIQHRF (Susceptible-Exposed-Infectious-Quarantined-Hospitalised-Recovered-Fatal) model.ResultsImplementing SD only delayed the appearance of peak prevalence of COVID-19 cases. Doubling of hospital beds couldn’t reduce the fatal cases probably due to its overwhelming number compared to the hospital beds. Increasing detection rates could significantly flatten the curve and reduce the peak prevalence of cases (increasing detection rate by 5 times could reduce case number to half).ConclusionsAn effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with SD measures and increase in hospital beds.HIGHLIGHTSIncreased Detection of COVID-19 cases must accompany Social Distancing and Health Capacity Planning to reduce the burden of cases and fatalities.Interruptive Social Distancing is an effective alternative to continuous Social Distancing.Given the overwhelming burden of COVID-19 fatalities, there is immediate need of co-ordination with the Private Healthcare Sector.COVID-19 cases will be peaking after May, 2020 giving us time for Healthcare Capacity Building in the government and private sector both.