The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
Mathematical modeling of infectious disease dynamics
Open accessAbstract
Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host-pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning their implementation and practice and for each category we give an annotated list of representative works.
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Cites
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Cited by
Carter, A., Msemburi, W., Sim, S. Y., Gaythorpe, K. A. M., Lambach, P., Lindstrand, A., & Hutubessy, R. (2024) · Vaccine · doi:10.1016/j.vaccine.2023.07.033 · Cited by 121
Awine, T., Malm, K., Bart-Plange, C., & Silal, S. P. (2017) · Global Health Action · doi:10.1080/16549716.2017.1381471 · Cited by 102
Metadata
- Authors
- Siettos, C. I., & Russo, L.
- Venue
- Virulence
- Publisher
- Informa UK Limited
- Published
- 2013-05-15
- Volume
- 4
- Issue
- 4
- Pages
- 295-306
- ISSN
- 2150-5594 (ISSN-L)
- 2150-5608
- OpenAlex
- W2058963141
- Cited by
- 466
- References
- 91
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
