Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing
| Autor: | James P. Smith, Susan M. Mniszewski, Phillip Romero, Phillip D. Stroud, Stephen J. Sydoriak, Jane M. Riese |
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| Rok vydání: | 2005 |
| Předmět: |
Statistics and Probability
Urban Population Population Population Dynamics Expected value Power law General Biochemistry Genetics and Molecular Biology Disease Outbreaks Statistics Influenza Human Linear scale Range (statistics) Humans Computer Simulation Statistical physics education Scaling Mathematics Chicago education.field_of_study Models Statistical General Immunology and Microbiology Applied Mathematics Incidence Contrast (statistics) General Medicine Orthomyxoviridae Los Angeles Modeling and Simulation General Agricultural and Biological Sciences Epidemic model Epidemiologic Methods |
| Zdroj: | Mathematical biosciences. 203(2) |
| ISSN: | 0025-5564 |
| Popis: | The expected number of new infections per day per infectious person during an epidemic has been found to exhibit power-law scaling with respect to the susceptible fraction of the population. This is in contrast to the linear scaling assumed in traditional epidemiologic modeling. Based on simulated epidemic dynamics in synthetic populations representing Los Angeles, Chicago, and Portland, we find city-dependent scaling exponents in the range of 1.7-2.06. This scaling arises from variations in the strength, duration, and number of contacts per person. Implementation of power-law scaling of the new infection rate is quite simple for SIR, SEIR, and histogram-based epidemic models. Treatment of the effects of the social contact structure through this power-law formulation leads to significantly lower predictions of final epidemic size than the traditional linear formulation. |
| Databáze: | OpenAIRE |
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