Network and equation-based models in epidemiology

Edoh, Kossi; MacCarthy, Elijah

doi:10.1142/s1793524518500468

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Thus, the factors that determine the weight of an edge are of great interest to us. Among these factors are the following: the duration of contacts [ 6 ] and number of contacts between nodes on the edge; age and gender of nodes; pathogen strength; and geographic area [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

High-Dimensional Contact Network Epidemiology

Ackerman,

Martin,

Tanisha

et al. 2023

Epidemiologia

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Contact network models are recent alternatives to equation-based models in epidemiology. In this paper, the spread of disease is modeled on contact networks using bond percolation. The weight of the edges in the contact graphs is determined as a function of several variables in which case the weight is the product of the probabilities of independent events involving each of the variables. In the first experiment, the weight of the edges is computed from a single variable involving the number of passengers on flights between two cities within the United States, and in the second experiment, the weight of the edges is computed as a function of several variables using data from 2012 Kenyan household contact networks. In addition, the paper explored the dynamics and adaptive nature of contact networks. The results from the contact network model outperform the equation-based model in estimating the spread of the 1918 Influenza virus.

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Section: Methodsmentioning

confidence: 99%

High-Dimensional Contact Network Epidemiology

Ackerman,

Martin,

Tanisha

et al. 2023

Epidemiologia

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“…Agent-based modelling often used by statistical physicists (Epstein, 1999;Samanidou, 2007;Chakraborti, 2011) has supplied a huge boost to analyse large systems in economy, social science and epidemiology. There have been a lot of possible topological settings considered in epidemiological contexts: square lattices (Sadedin 2003,;Dybiec 2009), completely connected networks with possible direct virus transmission between any pair of agents (Kephart, 1991;Trpevski, 2011), Markovian networks with correlated connectivity of neighbouring nodes (Boguna, 2002), small-world networks with high clustering (Trpevski, 2011;Edoh, 2018), Erdos-Renyi networks with high homogeneity of interactions (Trpevski, 2011;Reppas, 2012;Edoh, 2018) and scale-free networks with a power-law distribution of node connectivity (Pastor-Sattoras, 2001; Moreno, 2002). Setting the modelled dynamics in a scale-free network allows us to apply a variety of factors within a simple structure.…”

Section: Introductionmentioning

confidence: 99%

How to plug out of the networks in jeopardy of ASF, Covid-19, social media or markets toxicity

Buda¹,

Kuźmicz²

2021

EMET

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Aim: In our research, we examine universal properties of the global network whose structure represents a real-world network that might be later extended to social media, commodity market or countries under the infl uence of diseases like Covid-19 or ASF.Methods: We propose quasi-epidemiological agent-based model of virus spread on a network. Firstly, we consider countries represented by subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges. The global network of countries is a complete, directed, weighted network of thesesubnetworks connected by their capitals and divided into cultural and geographical proximity. Viruses with a defi ned strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of products or messages or diseases.Results and conclusion: We analyse dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. These phenomena have been also shown fromindividual users points of view because the node removal from the network might have impact on its nearest neighbours differently. The selective participation in global network is proposed here to avoid side effects when the global network has been fully connected and no longer divided into clusters.

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“…Since 1760, when Daniel Bernoulli developed the first disease model of smallpox, numerous mathematical models have been utilized to study disease transmission dynamics, and to predict, assess, and control infectious diseases [9][10][11][12].The substance of mathematical modeling lies in formulating a set of mathematical equations that mimic reality [13]. Mathematical models have been evolved from small sets of ordinary differential equations to sophisticated compartmental models with several equations (see [14][15][16] for a review).…”

mentioning

confidence: 99%

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

AlQadi

Bani-Yaghoub

2022

PLoS ONE

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Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection.

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Network and equation-based models in epidemiology

Cited by 9 publications

References 21 publications

High-Dimensional Contact Network Epidemiology

High-Dimensional Contact Network Epidemiology

How to plug out of the networks in jeopardy of ASF, Covid-19, social media or markets toxicity

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

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