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Models of Infection: Person to Person
January/February 2004 (vol. 6 no. 1)
pp. 68-73
| ASCII Text | x | ||
| Dianne P. O'Leary, "Models of Infection: Person to Person," Computing in Science and Engineering, vol. 6, no. 1, pp. 68-73, January/February, 2004. | |||
| BibTex | x | ||
| @article{ 10.1109/MCISE.2004.1255823, author = {Dianne P. O'Leary}, title = {Models of Infection: Person to Person}, journal ={Computing in Science and Engineering}, volume = {6}, number = {1}, issn = {1521-9615}, year = {2004}, pages = {68-73}, doi = {http://doi.ieeecomputersociety.org/10.1109/MCISE.2004.1255823}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - Computing in Science and Engineering TI - Models of Infection: Person to Person IS - 1 SN - 1521-9615 SP68 EP73 EPD - 68-73 A1 - Dianne P. O'Leary, PY - 2004 KW - infection models KW - Markov chains VL - 6 JA - Computing in Science and Engineering ER - | |||
When faced with a spreading infection, public health workers want to predict its path and severity to make decisions about vaccination strategies, quarantine policy, and the use of public health resources. This is true whether the pathogen?s dispersion is natural (for example, the spread of influenza in 1918) or deliberate (for example, the spread of anthrax via terrorism). Effective mathematical models can help us test a public health policy?s potential outcome and arrive at an effective response. In this issue, we focus on a simplified model of the spread of an infection and develop some tools that lend insight into its behavior.
Index Terms:
infection models, Markov chains
Citation:
Dianne P. O'Leary, "Models of Infection: Person to Person," Computing in Science and Engineering, vol. 6, no. 1, pp. 68-73, Jan.-Feb. 2004, doi:10.1109/MCISE.2004.1255823
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