scholarly journals When will the Covid-19 epidemic fade out?

2020 ◽  
Author(s):  
Ilaria Renna
Keyword(s):  
Infectious Diseases ◽  
Discrete Time ◽  
Epidemic Model ◽  
Small World ◽  
Small World Network ◽  
Sirs Model ◽  
Gaussian Fit

AbstractA discrete-time deterministic epidemic model is proposed with the aim of reproducing the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model under the framework of a small world network formulation. Using this model, we make predictions about the peak of the Covid-19 epidemic in Italy. A Gaussian fit is also performed, to make a similar prediction.

A KICKED EPIDEMIC SIRS MODEL

2012 ◽  
Vol 22 (10) ◽  
pp. 1250251
Author(s):  
F. PALADINI ◽  
I. RENNA ◽  
L. RENNA
Keyword(s):  
Infectious Diseases ◽  
Numerical Simulations ◽  
Population Size ◽  
Discrete Time ◽  
Epidemic Model ◽  
Seasonal Variability ◽  
Sirs Model ◽  
Variable Population ◽  
Variable Population Size

A discrete-time deterministic epidemic model is proposed with the aim of reproducing the behavior observed in the incidence of real infectious diseases, such as oscillations and irregularities. For this purpose, we introduce, in a naïve discrete-time SIRS model, seasonal variability (i) in the loss of immunity and (ii) in the infection probability, modeled by sequences of kicks. Effects of a variable population size (assumed as logistic) are also analyzed. Restrictive assumptions are made on the parameters of the models, in order to guarantee that the transitions are determined by true probabilities, so that comparisons with stochastic discrete-time previsions can be also provided. Numerical simulations show that the characteristics of real infectious diseases can be adequately modeled.

scholarly journals AN EPIDEMIC MODEL ON SMALL-WORLD NETWORKS AND RING VACCINATION

2002 ◽  
Vol 13 (02) ◽  
pp. 189-198 ◽  
Author(s):  
E. AHMED ◽  
A. S. HEGAZI ◽  
A. S. ELGAZZAR
Keyword(s):  
Epidemic Model ◽  
Vaccination Programme ◽  
Foot And Mouth Disease ◽  
Small World ◽  
Analytical Approximation ◽  
Theoretical Explanation ◽  
Small World Networks ◽  
Epidemic Spreading ◽  
Sirs Model ◽  
Ring Vaccination

A modified version of susceptible-infected-recovered-susceptible (SIRS) model for the outbreaks of foot-and-mouth disease (FMD) is introduced. The model is defined on small-world networks, and a ring vaccination programme is included. This model can be a theoretical explanation for the nonlocal interactions in epidemic spreading. Ring vaccination is capable of eradicating FMD provided that the probability of infection is high enough. Also an analytical approximation for this model is studied.

scholarly journals On discrete time epidemic models in Kermack-McKendrick form

2021 ◽  
Author(s):  
Odo Diekmann ◽  
Hans G. Othmer ◽  
Robert Planque ◽  
Martin CJ Bootsma
Keyword(s):  
Infectious Diseases ◽  
Discrete Time ◽  
Epidemic Model ◽  
Continuous Time ◽  
Reproduction Number ◽  
Compartmental Models ◽  
Initial Speed ◽  
Fixed Duration ◽  
Epidemic Spread ◽  
Special Cases

Surprisingly, the discrete-time version of the general 1927 Kermack-McKendrick epidemic model has, to our knowledge, not been formulated in the literature, and we rectify this omission here. The discrete time version is as general and flexible as its continuous-time counterpart, and contains numerous compartmental models as special cases. In contrast to the continuous time version, the discrete time version of the model is very easy to implement computationally, and thus promises to become a powerful tool for exploring control scenarios for specific infectious diseases. To demonstrate the potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and initial speed of epidemic spread, compartmental models systematically predict lower peak sizes than models that use a fixed duration for the latent and infectious periods.

A Surveillance and Spatiotemporal Visualization Model for Infectious Diseases Using Social Network

2019 ◽  
pp. 1027-1046
Author(s):  
Younsi Fatima-Zohra ◽  
Hamdadou Djamila ◽  
Boussaid Omar
Keyword(s):  
Risk Factors ◽  
Infectious Diseases ◽  
Human Population ◽  
Dynamical Behavior ◽  
Small World ◽  
Decision Makers ◽  
Small World Network ◽  
Seir Model ◽  
Visualization System ◽  
Spatiotemporal Visualization

In this paper, the authors propose a surveillance and spatiotemporal visualization system to simulate the infectious diseases spread which enables users to make decisions during a simulated pandemic. This system is based on compartment Susceptible, Exposed, Infected, and Removed (SEIR) model within a Small World network and Geographic Information System. The main advantage of this system is that it allows not only to understand how epidemic spreads in the human population and which risk factors promote this transmission but also to visualize epidemic outbreaks on the region's map. Experiments results reflect significantly the dynamical behavior of the influenza epidemic and the system can provide significant guidelines for decision makers when coping with epidemic diffusion controlling problems.

Epidemic spreading on networks based on stress response

2017 ◽  
Vol 31 (16) ◽  
pp. 1750131 ◽  
Author(s):  
Fuzhong Nian ◽  
Shuanglong Yao
Keyword(s):  
Stress Response ◽  
Infectious Diseases ◽  
Stress Responses ◽  
Epidemic Model ◽  
Small World ◽  
Small World Networks ◽  
Epidemic Spreading ◽  
Spreading Resistance ◽  
Scale Free ◽  
Scale Free Networks

Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual’s stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases.

scholarly journals The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1029 ◽  
Author(s):  
Mengyao Wu ◽  
Wei Dai ◽  
Zhiyuan Lu ◽  
Yu Zhao ◽  
Meiqing Wang
Keyword(s):  
Discrete Time ◽  
Information Entropy ◽  
Epidemic Model ◽  
Risk Evaluation ◽  
Disease Model ◽  
Propagation Mechanism ◽  
Assembly Process ◽  
Sirs Epidemic Model ◽  
Sirs Model ◽  
Optimal Assembly

In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process Δ H b a s e when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach Δ H is used to evaluate the assembly risk by comparing with the Δ H b a s e . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

A Surveillance and Spatiotemporal Visualization Model for Infectious Diseases using Social Network

2015 ◽  
Vol 7 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Younsi Fatima-Zohra ◽  
Hamdadou Djamila ◽  
Boussaid Omar
Keyword(s):  
Infectious Diseases ◽  
Human Population ◽  
Dynamical Behavior ◽  
Small World ◽  
Decision Makers ◽  
Small World Network ◽  
Influenza Epidemic ◽  
Seir Model ◽  
Visualization System ◽  
Spatiotemporal Visualization

In this paper, the authors propose a surveillance and spatiotemporal visualization system to simulate the infectious diseases spread which enables users to make decisions during a simulated pandemic. This system is based on compartment Susceptible, Exposed, Infected, and Removed (SEIR) model within a Small World network and Geographic Information System. The main advantage of this system is that it allows not only to understand how epidemic spreads in the human population and which risk factors promote this transmission but also to visualize epidemic outbreaks on the region's map. Experiments results reflect significantly the dynamical behavior of the influenza epidemic and the system can provide significant guidelines for decision makers when coping with epidemic diffusion controlling problems.

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

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