Heterogeneous network epidemics: real-time growth, variance and extinction of infection
Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability...
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| Format: | Article |
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Springer Verlag
2017
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| Online Access: | https://eprints.nottingham.ac.uk/40467/ |
| _version_ | 1848796064355713024 |
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| author | Ball, Frank House, Thomas |
| author_facet | Ball, Frank House, Thomas |
| author_sort | Ball, Frank |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multitype branching process. We find closed-form analytic expressions for the mean and variance of the number of infectious individuals as a function of time and the degree of the initially infected individual(s), and write down a system of differential equations for the probability of extinction by time t that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution—in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of infectious individuals, meaning that in the large population limit they would be asymptotically exact even for one initial infectious individual. |
| first_indexed | 2025-11-14T19:42:02Z |
| format | Article |
| id | nottingham-40467 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:42:02Z |
| publishDate | 2017 |
| publisher | Springer Verlag |
| recordtype | eprints |
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| spelling | nottingham-404672020-05-04T18:30:22Z https://eprints.nottingham.ac.uk/40467/ Heterogeneous network epidemics: real-time growth, variance and extinction of infection Ball, Frank House, Thomas Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multitype branching process. We find closed-form analytic expressions for the mean and variance of the number of infectious individuals as a function of time and the degree of the initially infected individual(s), and write down a system of differential equations for the probability of extinction by time t that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution—in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of infectious individuals, meaning that in the large population limit they would be asymptotically exact even for one initial infectious individual. Springer Verlag 2017-01-17 Article PeerReviewed Ball, Frank and House, Thomas (2017) Heterogeneous network epidemics: real-time growth, variance and extinction of infection. Journal of Mathematical Biology . pp. 1-43. ISSN 1432-1416 SIR epdidemic configuration model branching process http://link.springer.com/article/10.1007%2Fs00285-016-1092-3 doi:10.1007/s00285-016-1092-3 doi:10.1007/s00285-016-1092-3 |
| spellingShingle | SIR epdidemic configuration model branching process Ball, Frank House, Thomas Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title | Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title_full | Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title_fullStr | Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title_full_unstemmed | Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title_short | Heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| title_sort | heterogeneous network epidemics: real-time growth, variance and extinction of infection |
| topic | SIR epdidemic configuration model branching process |
| url | https://eprints.nottingham.ac.uk/40467/ https://eprints.nottingham.ac.uk/40467/ https://eprints.nottingham.ac.uk/40467/ |