An individual-based modeling framework for infectious disease spreading in clustered complex networks

2020 ◽  
Vol 83 ◽  
pp. 1-12 ◽  
Author(s):  
Qingchu Wu ◽  
Tarik Hadzibeganovic
Keyword(s):  
Infectious Disease ◽  
Complex Networks ◽  
Modeling Framework ◽  
Disease Spreading ◽  
Individual Based Modeling

scholarly journals Bounding the Size and Probability of Epidemics on Networks

2008 ◽  
Vol 45 (2) ◽  
pp. 498-512 ◽  
Author(s):  
Joel C. Miller
Keyword(s):  
Infectious Disease ◽  
Lower Bounds ◽  
Upper Bounds ◽  
Marginal Probability ◽  
Transmission Properties ◽  
Disease Spreading ◽  
Special Case

We consider an infectious disease spreading along the edges of a network which may have significant clustering. The individuals in the population have heterogeneous infectiousness and/or susceptibility. We define the out-transmissibility of a node to be the marginal probability that it would infect a randomly chosen neighbor given its infectiousness and the distribution of susceptibility. For a given distribution of out-transmissibility, we find the conditions which give the upper (or lower) bounds on the size and probability of an epidemic, under weak assumptions on the transmission properties, but very general assumptions on the network. We find similar bounds for a given distribution of in-transmissibility (the marginal probability of being infected by a neighbor). We also find conditions giving global upper bounds on the size and probability. The distributions leading to these bounds are network independent. In the special case of networks with high girth (locally tree-like), we are able to prove stronger results. In general, the probability and size of epidemics are maximal when the population is homogeneous and minimal when the variance of in- or out-transmissibility is maximal.

Modeling Large-Scale Epidemics

2017 ◽  
Author(s):  
Sanjay Basu
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Markov Model ◽  
Simulation Modeling ◽  
Large Scale ◽  
Basic Feature ◽  
Risk Of Infection ◽  
Critical Aspect ◽  
Modeling Framework ◽  
Disease Epidemics

Previous chapters ignored a critical aspect of modeling some major diseases: the infectious nature of many diseases. For infectious diseases, the risk of getting the disease is related to how many people are infectious at a given time: the more infectious people in the area, the higher the risk of infection among susceptible people. In a typical Markov model, we can’t account for this basic feature of infectious diseases because the risk of moving from one state (healthy) to another state (diseased) is assumed to be constant. In this chapter, the author introduces a simulation modeling framework that has been used for decades to simulate infectious disease epidemics.

scholarly journals Controlling distant contacts to reduce disease spreading on disordered complex networks

2020 ◽  
Vol 545 ◽  
pp. 123709 ◽  
Author(s):  
Ignacio A. Perez ◽  
Paul A. Trunfio ◽  
Cristian E. La Rocca ◽  
Lidia A. Braunstein
Keyword(s):  
Complex Networks ◽  
Disease Spreading

scholarly journals COVID-19 and Thrombosis Predisposition

2020 ◽  
Vol 5 (3) ◽  
pp. 86-90
Author(s):  
Renata Gerculy ◽  
Camelia Libenciuc ◽  
Nora Rat ◽  
Monica Chitu ◽  
Imre Benedek
Keyword(s):  
Infectious Disease ◽  
Current Knowledge ◽  
The Novel ◽  
Coagulation Disorders ◽  
Healthy Individuals ◽  
The World ◽  
Life Threatening ◽  
Thrombotic Complications ◽  
Disease Spreading ◽  
Novel Coronavirus

AbstractThe novel coronavirus disease first appeared in Wuhan (China) is an infectious disease spreading throughout the world, causing life-threatening conditions in vulnerable or even healthy individuals. The great impact of this virus on healthcare urges physicians to investigate all aspects of the disease in order to overcome its complications. A particularly investigated aspect of the SARS-CoV-2 infection is represented by the coagulation disorders among infected and critically ill patients. Several studies observed modified blood coagulation parameters such as D-dimers, fibrinogen, and coagulation times. Moreover, the severe thrombotic complications, mainly pulmonary embolism, could be responsible for the high mortality and poorer outcomes of COVID-19 infected patients. The aim of this article is to present the current knowledge related to thrombosis predisposition in patients infected with the new coronavirus.

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