Microbial Threats to Health Emergence, Detection, and Response (2003) / Chapter Skim
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Appendix E: Computational Modeling and Simulation of Epidemic Infectious Diseases
Appendix E: Computational Modeling and Simulation of Epidemic Infectious Diseases
Pages 335-342
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From page 335... ...
Bloomberg School of Public Health Johns Hopkins University I simply wish that, in a matter which so closely concerns the well-being of mankind, no decision shall be made without all the knowledge which a little analysis and calculation can provide. Daniel Bernoulli, on smallpox inoculation, 1766 HISTORICAL FOUNDATIONS Mathematics and statistics have been essential to the theory and practice of infectious disease control since 1766, when Bernoulli analyzed life expectancies and death rates in his evaluation of variolation as a public health too!
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From page 336... ...
Such simulations could serve as dry "laboratories" for a new science of experimental epidemiology in which new population-level interventions could be designed, evaluated, and iteratively refined on simulated epidemics, with tangible benefits for real-worId epidemic prevention and control efforts. Successful development of this new science will require interdisciplinary collaborations between epidemiologists and other computationally oriented academic disciplines (Levin et al., 1997~.
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From page 337... ...
Of special interest is demonstration of the power of wavelet analysis to decompose measles epidemic harmonics to reveal recurrent spatial spreading patterns not evident in the undecomposed epidemic data. Such preliminary successes with decompositional techniques suggest they will make it possible to analyze and explain the dynamics of many infectious diseases (Grenfell et al., 2001; Strebe!
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From page 338... ...
The rapid rise in freely available computational power should permit the development of a wide variety of infectious disease agent-based simulations (Swarm Development Group website)
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From page 339... ...
Furthermore, the crucial role of occasional long-distance internodal connections in shortening global mean path lengths (the "small world" phenomenon) and accelerating epidemic spread has come to be appreciated (Watts, 1999~.
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From page 340... ...
Genetic algorithms are now widely employed by computer programmers to solve practical computationally intensive problems, such as protein folding, but only a few studies have appeared in which evolving code strings are used to simulate microbial evolution and adaptation. Preliminary studies suggest that the rules governing code string evolution may be independent of the stuff from which the evolving code strings are made, and that experiments on digital microbes with code string evolution and epidemiology "in silicon" may be a productive way to understand and solve problems that are difficult to study in nature (Ray, 1995; Wilke et al., Adami et al., 2000; Radman et al., 1999~.
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From page 341... ...
Particularly in dealing with a hypothetical threat such as smallpox, models and simulations can allow the testing of intervention strategies in silicon that simply cannot be tested in advance, and could never be tested in a real-worId bioterrorism emergency. NEW NATIONAL INITIATIVE IN COMPUTATIONAL EPIDEMIOLOGY The Center for Discrete Mathematics and Theoretic Computer Science, created by the National Science Foundation, recently established a five-year special focus on computational and mathematical epidemiology (Center for Discrete Mathematics and Theoretic Computer Science webpage, 2001~.
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From page 342... ...
Travelling waves and spatial hierarchies in measles epidemics. Nature 414: 716-723 (2001)
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