Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing

Stroud, Phillip D.; Sydoriak, Stephen J.; Riese, Jane M.; Smith, James P.; Mniszewski, Susan M.; Romero, Phillip

doi:10.1016/j.mbs.2006.01.007

Cited by 57 publications

(77 citation statements)

References 22 publications

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“…In 2006, Stroud [101] simulated epidemic dynamics of avian-related influenza in synthetic populations representing three US metropolitan areas (Los Angeles, Chicago, and Portland). They found that the number of new infections per day per infectious person scaled as a power (greater than one) of the fraction of the population that is susceptible.…”

Section: Other Examples Of Power Law In Sciencesmentioning

confidence: 99%

A review of power laws in real life phenomena

Pinto

Lopes

Machado³

2012

Communications in Nonlinear Science and Numerical Simulation

131

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a b s t r a c t Power law distributions, also known as heavy tail distributions, model distinct real life phenomena in the areas of biology, demography, computer science, economics, informa-tion theory, language, and astronomy, amongst others. In this paper, it is presented a review of the literature having in mind applications and possible explanations for the use of power laws in real phenomena. We also unravel some controversies around power laws.

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Section: Other Examples Of Power Law In Sciencesmentioning

confidence: 99%

A review of power laws in real life phenomena

Pinto

Lopes

Machado³

2012

Communications in Nonlinear Science and Numerical Simulation

131

View full text Add to dashboard Cite

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“…A simple phenomenological extension of the SIR model to the non-homogeneous mixing case was proposed in [23] with…”

Section: Epidemic Outbreak Modelmentioning

confidence: 99%

“…It was suggested [23] and also validated by numerical simulations that with the new parameter, the modified epidemiological model can capture the inhomogeneity of social interactions. The model reduces to the conventional SIR model for a particular value of the "inhomogeneity" parameter (details below).…”

Section: Introductionmentioning

confidence: 97%

“…Such a capability would allow c Commonwealth of Australia making any countermeasures (quarantine, vaccination, medical treatment) much more effective and less costly [25]. Conventional approach to early detection and prediction of an epidemic is often based on massive scale agent-based simulation programs, like EpiSim [23], [24].…”

Section: Introductionmentioning

confidence: 99%

“…A simple way to overcome the first issue was proposed in the recent paper [23] (for further references see also [14]), where a new "inhomogeneity" parameter was heuristically introduced into the SIR epidemiological model. It was suggested [23] and also validated by numerical simulations that with the new parameter, the modified epidemiological model can capture the inhomogeneity of social interactions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting an epidemic based on syndromic surveillance

Skvortsov

Ristić

Woodruff

2010

2010 13th International Conference on Information Fusion

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-Early detection and prediction of the size and the peak time of an epidemic outbreak (malicious or natural) is of crucial importance for a timely medical response (quarantine, vaccination, etc). A conventional approach to this problem is based on large scale agent-based computer simulations. This paper proposes an alternative framework formulated in the context of stochastic nonlinear filtering. The framework is based on the stochastic SIR epidemiological model of infection dynamics, with syndromic (often non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study etc.). The unknown parameters of the SIR epidemic model are estimated via the sequential Monte Carlo method, with the prediction based on the dynamic model. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.

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Applications Involving Non-standard Measurements

Ristić¹

2013

Particle Filters for Random Set Models

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Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing

Cited by 57 publications

References 22 publications

A review of power laws in real life phenomena

A review of power laws in real life phenomena

Predicting an epidemic based on syndromic surveillance

Applications Involving Non-standard Measurements

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