Pellis, L;
Ball, F;
Bansal, S;
Eames, K;
House, T;
Isham, V;
Trapman, P;
(2015)
Eight challenges for network epidemic models.
Epidemics
, 10
pp. 58-62.
10.1016/j.epidem.2014.07.003.
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Abstract
Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity) have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.
Type: | Article |
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Title: | Eight challenges for network epidemic models |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.epidem.2014.07.003 |
Publisher version: | http://dx.doi.org/10.1016/j.epidem.2014.07.003 |
Language: | English |
Additional information: | © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/). |
Keywords: | Infectious disease models; Transmission dynamics; Contact networks; Random graphs; Dynamic networks; Control measures |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1457126 |
1. | China | 4 |
2. | United States | 1 |
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