Biology Reference
In-Depth Information
computational epidemic forecast infrastructures able to
provide reliable, detailed and quantitatively accurate
predictions of global epidemic spread.
REFERENCES
[1] Anderson RM, May RM. Spatial, temporal and genetic heteroge-
neity in host populations and the design of immunization programs.
IMA J Math Appl Med Biol 1984;1:233
e
66.
[2] May RM, Anderson RM. Spatial heterogeneity and the design of
immunization programs. Math Biosci 1984;72:83
e
111.
[3] Chatterjee S, Durrett R. Contact processes on random graphs with
power law degree distributions have critical value 0. Ann Probab
2009;37:2332
e
56.
[4] Bolker BM, Grenfell BT. Chaos and biological complexity in
measles dynamics. Proc R Soc Lond B 1993;251:75
e
81.
[5] Bolker BM, Grenfell BT. Space persistence and dynamics of
measles epidemics. Phil Trans R Soc Lond 1995;348:309
20.
[6] Lloyd AL, May RM. Spatial heterogeneity in epidemic models.
J Theor Biol 1996;179:1
e
11.
[7] Grenfell BT, Bolker BM. Cities and villages: infection hierarchies
in a measles meta-population. Ecol Lett 1998;1:63
e
70.
[8] Keeling MJ, Rohani P. Estimating spatial coupling in epidemio-
logical systems: a mechanistic approach. Ecol Lett 2002;5:20.
[9] Ferguson NM, et al. Planning for smallpox outbreaks. Nature
2003;425:681.
[10] Rvachev LA, Longini JIM. A mathematical model for the global
spread of influenza. Math Biosc 1985;75:3.
[11] Keeling MJ, Rohani P. Estimating spatial coupling in epidemio-
logical systems: a mechanistic approach. Ecol Lett 1995;5:20
e
9.
[12] Hufnagel L, Brockmann D, Geisel T. Forecast and control of
epidemics in a globalized world. Proc Natl Acad Sci USA 2004;
101:15124.
[13] Longini JIM, et al. Containing pandemic influenza at the source.
Science 2005;309:1083.
[14] Ferguson NM, et al. Strategies for containing an emerging influ-
enza pandemic in Southeast Asia. Nature 2005;437:209.
[15] Colizza V, Barrat A, Barthelemy M, Vespignani A. The role of the
airline transportation network in the prediction and predictability of
global epidemics. Proc Natl Acad Sci 2006;103:2015.
[16] Keeling MJ, et al. Foot and mouth epidemic: stochastic dispersal in
a heterogeneous landscape. Science 2001;294:813
e
7.
[17] Colizza V, Barrat A, Barthelemy M, Vespignani A. The modeling
of global epidemics: stochastic dynamics and predictability. Bull
Math Biol 2006;68(8):1893
e
FIGURE 27.7
Each node represents a Twitter user that employed the
#bahrain hashtag. Blue and orange edges represent information
exchanged between users through the use of retweets and replies,
respectively. The two clusters observed correspond to two communities, an
English- and an Arabic- speaking one. Retweets are used predominantly to
communicate within a single cluster, whereas mentions are employed to
send information from one community to the other.
and dynamics, and predict their behavior, but also to control
their dynamics. Also in this case, although control theory
offers a large set of mathematical tools for steering engi-
neered and natural systems, we have yet to understand how
the network heterogeneities influence our ability to control
the network dynamics and how network evolution affects
controllability
[82]
.
Taking into account the complexity of real systems in
epidemic modeling has proved to be unavoidable, and the
corresponding approaches have already produced a wealth
of interesting results. While this has stimulated the recent
focus on large-scale computational approach to epidemic
modeling it is clear that many basic theoretical questions
are still open. How does the complex nature of the real
world affect our predictive capabilities in the realm of
computational epidemiology? What are the fundamental
limits in epidemic evolution predictability with computa-
tional modeling? How do they depend on the level of
accuracy of our description and knowledge on the state of
the system? Tackling such questions necessitates exploiting
several techniques and approaches. Complex systems and
networks analysis, mathematical biology, statistics, non-
equilibrium statistical physics and computer science all
play an important role in the development of a modern
computational epidemiology approach. While such an
integrated approach might still be in its first steps, it seems
now possible to ambitiously imagine the creation of
921.
[18] Eubank S, et al. Modelling disease outbreaks in realistic urban
social networks. Nature 2004;429:180.
[19] Keeling MJ, Rohani P. Modeling Infectious Disease in Humans and
Animals. Princeton University Press; 2008.
[20] Goffman W, Newill VA. Generalization of epidemic theory: an
application to the transmission of ideas. Nature 1964;204:225.
[21] Castellano C, Pastor-Satorras R. Thresholds for epidemic spreading
in networks. Phys Rev Lett 2010;105:218701.
[22] Peiris JSM, Yuen KY, Stohr K. The severe acute respiratory
syndrome. N Engl J Med 2003;349:2431
e
41.
[23] Lloyd AL, May RM. How viruses spread among computers and
people. Science 2001;292:1316.
[24] Eagle N, Sandy Pentland A. Reality mining: sensing complex
social systems. Pers Ubiquitos Comput 2006;10:255
e
68.
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