Eight challenges for network epidemic models
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 epi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015-03-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436514000334 |
| _version_ | 1818936644197154816 |
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| author | Lorenzo Pellis Frank Ball Shweta Bansal Ken Eames Thomas House Valerie Isham Pieter Trapman |
| author_facet | Lorenzo Pellis Frank Ball Shweta Bansal Ken Eames Thomas House Valerie Isham Pieter Trapman |
| author_sort | Lorenzo Pellis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T05:39:20Z |
| format | Article |
| id | doaj.art-72e0a81702bd461ab44a8d73b2fd78dc |
| institution | Directory Open Access Journal |
| issn | 1755-4365 1878-0067 |
| language | English |
| last_indexed | 2024-12-20T05:39:20Z |
| publishDate | 2015-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-72e0a81702bd461ab44a8d73b2fd78dc2022-12-21T19:51:30ZengElsevierEpidemics1755-43651878-00672015-03-0110C586210.1016/j.epidem.2014.07.003Eight challenges for network epidemic modelsLorenzo Pellis0Frank Ball1Shweta Bansal2Ken Eames3Thomas House4Valerie Isham5Pieter Trapman6Warwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UKSchool of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UKDepartment of Biology, Georgetown University, Washington, DC 20057, USACentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UKWarwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UKDepartment of Statistical Science, University College London, London WC1E 6BT, UKDepartment of Mathematics, Stockholm University, Stockholm 106 91, SwedenNetworks 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.http://www.sciencedirect.com/science/article/pii/S1755436514000334Infectious disease modelsTransmission dynamicsContact networksRandom graphsDynamic networksControl measures |
| spellingShingle | Lorenzo Pellis Frank Ball Shweta Bansal Ken Eames Thomas House Valerie Isham Pieter Trapman Eight challenges for network epidemic models Epidemics Infectious disease models Transmission dynamics Contact networks Random graphs Dynamic networks Control measures |
| title | Eight challenges for network epidemic models |
| title_full | Eight challenges for network epidemic models |
| title_fullStr | Eight challenges for network epidemic models |
| title_full_unstemmed | Eight challenges for network epidemic models |
| title_short | Eight challenges for network epidemic models |
| title_sort | eight challenges for network epidemic models |
| topic | Infectious disease models Transmission dynamics Contact networks Random graphs Dynamic networks Control measures |
| url | http://www.sciencedirect.com/science/article/pii/S1755436514000334 |
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