scholarly journals Epidemic Spreading Combined with Age and Region in Complex Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xu Zhang ◽  
Yurong Song ◽  
Haiyan Wang ◽  
Guo-Ping Jiang
Keyword(s):  
Social Networks ◽  
Partial Differential Equations ◽  
Infectious Diseases ◽  
Disease Control ◽  
Numerical Simulations ◽  
Real World ◽  
Epidemic Spreading ◽  
Sis Model ◽  
Proposed Model ◽  
The Usa

In social networks, the age and the region of individuals are the two most important factors in modeling infectious diseases. In this paper, a spatial susceptible-infected-susceptible (SIS) model is proposed to describe epidemic spreading over a network with region and age by establishing several partial differential equations. Numerical simulations are performed, and the simulation of the proposed model agrees well with real influenza-like illness (ILI) in the USA reported by the Centers for Disease Control (CDC). Moreover, the proposed model can be used to predict the infected density of individuals. The results show that our model can be used as a tool to analyze influenza cases in the real world.

Epidemic Spreading

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Social Media ◽  
Infectious Diseases ◽  
Sir Model ◽  
Temporal Networks ◽  
Epidemic Spreading ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Sis Model ◽  
Online Social Media ◽  
Different Types

Epidemic processes are relevant to studying the propagation of infectious diseases, but their current use extends also to the study of propagation of ideas in the society or memes and news in online social media. In most of the relevant applications epidemic spreading does not actually take place on a single network but propagates in a multilayer network where different types of interaction play different roles. This chapter provides a comprehensive view of the effect that multilayer network structures have on epidemic processes. The Susceptible–Infected–Susceptible (SIS) Model and the Susceptible–Infected–Removed (SIR) Model are characterized on multilayer networks. Additionally, it is shown that the multilayer networks framework can also allow us to study interacting Awareness and epidemic spreading, competing networks and epidemics in temporal networks.

scholarly journals Faces of Resistance: Using Real-world Patients and Their Advocates to Teach Medical Students about Antimicrobial Stewardship

2019 ◽  
Author(s):  
Priya Nori ◽  
Kelsie Cowman ◽  
Amanda Jezek ◽  
Joshua D Nosanchuk ◽  
Magdalena Slosar-Cheah ◽  
...  
Keyword(s):  
Medical Students ◽  
Infectious Diseases ◽  
Disease Control ◽  
Antimicrobial Stewardship ◽  
Real World ◽  
Response Rate ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
Patient Stories

Abstract We engaged medical students with antimicrobial stewardship (AS) and resistance (AMR) through patient stories and a panel on AMR advocacy with experts from the Centers for Disease Control and Prevention and the Infectious Diseases Society of America. Students were surveyed on their perceptions about AS and AMR (response rate=139/166, 84%).

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

Permanence and extinction for a nonautonomous schistosomiasis model with saturation incidence rate

2015 ◽  
Vol 08 (02) ◽  
pp. 1550021 ◽  
Author(s):  
Dehui Xie ◽  
Xiangyu Zhang ◽  
Shuixian Yan ◽  
Shujing Gao
Keyword(s):  
Infectious Diseases ◽  
Numerical Simulations ◽  
Incidence Rate ◽  
Real World ◽  
Sufficient Conditions ◽  
Time Behavior ◽  
Long Time Behavior ◽  
The Real ◽  
Long Time

In the real world, quite a few infectious diseases like schistosomiasis spread seasonally. In this paper, a nonautonomous schistosomiasis system is established, in which the saturation incidence rate and the coefficients varying with time are taken into account. The long-time behavior of the model is studied. Under quite weak assumptions, sufficient conditions for the permanence and extinction of infectious population of disease are obtained. Finally, numerical simulations illustrate the validity of our results.

An effective community-based link prediction model for improving accuracy in social networks

2021 ◽  
pp. 1-17
Author(s):  
M. Mohamed Iqbal ◽  
K. Latha
Keyword(s):  
Social Networks ◽  
Prediction Model ◽  
Real World ◽  
Link Prediction ◽  
Centrality Measures ◽  
Community Based ◽  
Proposed Model ◽  
Similarity Indices ◽  
Community Information ◽  
The Given

Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds.

scholarly journals Fake News Detection

2021 ◽  
pp. 140-146
Author(s):  
Lakesh Jat ◽  
Mansi Mohite ◽  
Radhika Choudhari ◽  
Pooja Shelke
Keyword(s):  
Social Networks ◽  
Network Model ◽  
Real World ◽  
Online Social Networks ◽  
Fake News ◽  
Estimation Model ◽  
Proposed Model ◽  
Latent Features ◽  
News Credibility ◽  
Text Information

This research paper aims to examine the principles, procedures and algorithms for finding fake news, creators and topics on online social networks and to evaluate the relevant performance. This paper deals with the unknown features of fake news and the challenges identified by the various relationships between news articles, creators and subjects. This paper introduces a novel automated fake news credibility estimation model called “fake news detection”. Based on a set of explicit and latent features extracted from text information, “Fake News Detection” builds an in-depth network model to simultaneously learn news articles, producers, and topic presentations. Extensive experiments have been carried out on real-world fake news datasets to compare "fake news detection" with many "sophisticated models", and experimental results have shown the effectiveness of the proposed model.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

Fr201 PERSISTENCE OF ABNORMAL ENDOSCOPY AND BIOPSY FINDINGS IN PATIENTS WITH EOSINOPHILIC ESOPHAGITIS IN A REAL-WORLD SETTING IN THE USA

2021 ◽  
Vol 160 (6) ◽  
pp. S-263-S-264
Author(s):  
Olulade Ayodele ◽  
Rohan C. Parikh ◽  
Elizabeth Esterberg ◽  
Mayank Ajmera ◽  
Bridgett Goodwin ◽  
...  
Keyword(s):  
Eosinophilic Esophagitis ◽  
Real World ◽  
Real World Setting ◽  
The Usa

Pesticides in Honey Bee Colonies: establishing a baseline for real world exposure over seven years in the USA

2021 ◽  
pp. 116566
Author(s):  
Kirsten S. Traynor ◽  
Simone Tosi ◽  
Karen Rennich ◽  
Nathalie Steinhauer ◽  
Eva Forsgren ◽  
...  
Keyword(s):  
Honey Bee ◽  
Real World ◽  
The Usa ◽  
Bee Colonies

scholarly journals Rapid implementation of Inpatient eConsult Programme addresses new challenges for patient care during COVID-19 pandemic

2021 ◽  
pp. bmjinnov-2020-000557
Author(s):  
Sharon Rikin ◽  
Eric J Epstein ◽  
Inessa Gendlina
Keyword(s):  
Palliative Care ◽  
Infectious Diseases ◽  
Patient Care ◽  
Personal Protective Equipment ◽  
Protective Equipment ◽  
Qualitative Feedback ◽  
The Usa ◽  
New Challenges ◽  
Science Framework ◽  
Context Specific

IntroductionAt the early epicentre of the COVID-19 crisis in the USA, our institution saw a surge in the demand for inpatient consultations for areas impacted by COVID-19 (eg, infectious diseases, nephrology, palliative care) and shortages in personal protective equipment (PPE). We aimed to provide timely specialist input for consult requests during the COVID-19 pandemic by implementing an Inpatient eConsult Programme.MethodsWe used the reach, effectiveness, adoption, implementation and maintenance implementation science framework and run chart analysis to evaluate the reach, adoption and maintenance of the Inpatient eConsult Programme compared with traditional in-person consults. We solicited qualitative feedback from frontline physicians and specialists for programme improvements.ResultsDuring the study period, there were 46 available in-person consult orders and 21 new eConsult orders. At the peak of utilisation, 42% of all consult requests were eConsults, and by the end of the study period, utilisation fell to 20%. Qualitative feedback revealed subspecialties best suited for eConsults (infectious diseases, nephrology, haematology, endocrinology) and influenced improvements to the ordering workflow, documentation, billing and education regarding use.DiscussionWhen offered inpatient eConsult requests as an alternative to in-person consults in the context of a surge in patients with COVID-19, frontline physicians used eConsult requests and decreased use of in-person consults. As the demand for consults decreased and PPE shortages were no longer a major concern, eConsult utilisation decreased, revealing a preference for in-person consultations when possible.ConclusionsLessons learnt can be used to develop and implement inpatient eConsults to meet context-specific challenges at other institutions.

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