Living Dead Networks
Eugene Thacker, School of Literature, Communication, and Culture,
Georgia Institute of Technology
Contagion and Transmission
In contemporary popular culture, ideas about contagion are often
tied up with ideas about information transmission. The film 28
Days Later, for instance, opens with a harrowing scene in which
primates undergo medical experiments by being exposed to large doses
of violent media images. Though the link between these images and
the ‘rage virus’ that takes over the British Isles is never explained,
the film abstractly puts forth the idea that there is some relation
between media image and biological virus. The Japanese horror film
Ringu takes this a step further, imaging a videotape, which
causes its viewer to suffer a mysterious death. Rumors about the
videotape begin circulating and the videotape itself becomes a kind
of vector for the contagious and ultimately fatal images. But it
is not only in film that such connections between contagion and
transmission are expressed. The online, multiplayer video game Resident
Evil: Outbreak takes the contagion-transmission link even further
in its existence as a network-based game. The game follows the narrative
of many of the other Resident Evil games, in which a secret
corporate bioweapons program runs amok, releasing its experimental
virus into the unsuspecting population of a nearby city. One of
the aims of the game is not only to contain the spread of the epidemic
(which has the effect of turning its host into a flesh-eating zombie),
but also to exterminate those that are already infected. As an online,
multiplayer game, Resident Evil: Outbreak is actually played
in real-time over the Internet, with other players who take on other
roles or characters in the game world. Thus the biological network
of the epidemic within the diagesis of the game world is layered
onto the informatic network or technical infrastructure, which enables
the game to be played. In addition, players in the game must gather
various bits of information regarding the status of the epidemic
in the infected city (urban regions infected, number of civilians
infected, number of kills), making the game a hybrid of typical
‘FPS’ (first-person shooter) games and a public health computer
simulation.
What each of these examples does is to raise the issue of the
relation between contagion and transmission, or between the assumed
materiality of biology, and the assumed immateriality of information.
We are accustomed to thinking of contagion as a biological or epidemiological
term, which is then metaphorized into non-biological contexts to
speak of computer ‘viruses,’ cultural ‘memes,’ or ‘viral marketing.’
Conversely, information is colloquially thought of as an abstract,
immaterial entity that may exist in different physical media (DVDs,
CDs, or hard drives). Classical information theory, which states
that ‘the semantic aspects of information are irrelevant to the
engineering problem’ (Shannon and Weaver, 1965: 31), still influences
many of the basic assumptions in the construction and maintenance
of information networks today. In short, the concept of contagion
presumes a materiality that can then be abstracted into metaphor,
while the concept of information assumes an immaterial form or pattern
that can then be materialized in a range of physical media.
For this reason, it is no surprise that a great number of horror
films combine epidemics with the figure of the zombie, or the ‘living
dead.’ Zombies always seem to result from the contagion of biological
epidemics, as if the ultimate fear of contagion was not simply death
itself, but a death beyond death, a ‘living death’ in which the
biological is exclusively biological, in which the self is
nothing but a body. But the figure of the zombie has also
gone through many metamorphoses, from the earliest films (e.g. White
Zombie) depicting eroticized Haitian voodoo rituals, to the
American and Italian splatter horror films of the 1970s (the films
of Romero and Fulci), in which zombies are often metaphors for the
‘silent majority.’ However contemporary genre horror (in film, fiction,
games, and comics) adds a twist to the familiar motifs of the zombie
film: the role of information in either transmitting, propagating,
or even producing contagion. In its own language of genre motifs
and campy self-reflexivity, contemporary zombie horror asks an interesting
question: how is our understanding of biological epidemics affected
by our ambient environment of computer and information networks?
That is, how does transmission affect contagion, and vice-versa?
Traditionally, zombie films represent the paradox of the living
dead as the ‘animate corpse’ or the state of being nothing but the
‘bare life’ of a body. The horror of contemporary ‘living dead’
is not just the fear of being reduced to nothing but body, but,
in the ‘network society,’ perhaps the horror of the ‘living dead’
is the fear being reduced to nothing but information – or not
being able to distinguish between contagion and transmission.
In this sense the paradox of the living dead is also the paradox
of ‘vital statistics,’ a sort of living dead network that exceeds
and even supercedes the ‘bare life’ of the organism.
A Note on Method
In a sense, emerging technoscientific fields are more avant-garde
than the most avant-garde cultural theory. Hybrid DNA chips, neural
cells communicating across the Internet, enzyme-based ‘wet’ computers,
in vitro DNA libraries, and computer immune systems are all
examples of this vanguard character of contemporary technoscience.
Indeed, one of the unique characteristics presented by the artifacts
of technoscience is that they seem to demonstrate their contingencies,
their modes of knowledge-production, their performative laboratory
contexts, and their disciplinary and institutional sites. I speak
of such artifacts with a degree of vitalism (and irony) because,
in many cases, they demonstrate something in their performance
that is in excess of the intentions and discourses that enframe
them; they increasingly demand to be considered as fleshy but nonhuman.
They are artifacts that not only perform biological labor and produce
information, but they are artifacts that also intervene in human
decision-making and action.
Such artifacts demand a mode of critical engagement that is as
uncanny as they are, expressing a sense of the most everyday that
is the most unbelievable. Gilles Deleuze suggests one such approach,
in his notion of the ‘diagram.’ In its colloquial sense, a diagram
is a graphical mode of representation that is used to conceptualize
a process or to produce a model (a workflow diagram, a technical
diagram). In this way, a diagram is an analytic tool, a visual artifact
pointing to its referent. But a diagram also brings forth relationships
between entities in a system that are not apparent in the system
itself; it also reveals latent, existing relations, and as such
it may cut across traditional distinctions. It is this abstract
and concrete character of the diagram that Deleuze emphasizes when
he speaks of power relations as being ‘diagrammatic.’ For Deleuze,
‘the diagram is no longer an auditory or visual archive but
a map, a cartography’ (1999: 34). Furthermore, ‘every society has
its diagram(s),’ its unique topology of the discursive and non-discursive,
‘the map of relations between forces that constitute power’ (1999:
35, 36).
A diagrammatic method revolves around the issue of form.
In the work of Michel Foucault, Deleuze identifies a constant interplay
between a form that organizes ‘matters’ (e.g. the prison, the hospital,
the school) and a form that canalizes ‘functions’ (punishing, curing,
educating). Now, neither of these aspects of form can be reduced
to the other (for instance, ‘curing’ cannot be reduced to the hospital).
But, asks Deleuze, is there a common term that stitches or weaves
them together? For Deleuze, the diagram is this topological relation
within the forms of power relations, an ‘immanent cause that is
coextensive with the whole social field’ (1999: 37). Foucault is
therefore less a ‘new historian’ and more a ‘new cartographer,’
drawing out points, relations, and topologies.
Deleuze points to three characteristics of the diagram, characteristics
that will guide this essay. First, each diagram abstracts a ‘spatio-temporal
multiplicity,’ existing in a way that occupies topologies
of all sorts (geographic, economic, biological topologies). Deleuze
gives the example of Foucault’s history of madness, in the shift
from the ‘leprosy’ diagram of the Middle Ages (which functions by
excluding and dividing) to the ‘plague’ diagram of the early modern
era (which functions by including and regulating). A second feature
of the diagram is that it is ‘continually churning up matter and
functions in a way likely to create change’ (1999: 35). Diagrams
are always about to undergo a phase change, as when Foucault describes
hospital reforms in pre-Revolutionary France as a combination of
sovereign (state-mandated) and disciplinary (surveillance) diagrams.
Finally, Deleuze states that the diagram ‘produces a new kind of
reality’ by drawing out ‘unexpected conjugations or improbable continuums’
that constitute a particular object of study (1999: 35).
The diagram provides a cross-section, a transversal (similar to
the transverse cross-sections used on frozen cadavers in digital
anatomy). Diagrams cut across separate organs and organ systems,
they cut across institutions, governments, social classes, technical
paradigms, and cultural forms. The resultant view is very different
from the anthropomorphic body politic, though still familiar, if
only in a dizzying way. Given that Deleuze is often referenced as
the philosopher of becoming, we may be inclined to think of a diagram
as that which reveals the ‘becoming’ of the event. But I would argue
instead that a diagram is more like a demonstration, a technical
‘demo’ of something that is already in effect. A diagrammatic method
would therefore draw out the ‘demo’ function of each particular
context. At its most extreme, a diagrammatic approach is simply
a crafted series of juxtapositions. The diagram appears to simply
present information, a montage of data and flesh, an artifactual
dérive.
Information Security / Mathematical Epidemiology
Let us begin with the separate fields of information security
and mathematical epidemiology. The cultural con-fusion between contagion
and transmission mentioned above has its analogue in these two related
fields. In information security, biological tropes are used to understand
computer ‘viruses’ and design ‘computer immune systems.’ In mathematical
epidemiology, mathematical, statistical, and probabilistic methods
are used to study the dynamics between populations and disease,
which is now being extended in the use of computers to simulate
and forecast epidemic outbreaks.
However, it is not the case that we begin with two separate fields
(biology and informatics) which are then fused together via contemporary
technoscience, and neither is it the case that a primary unity is
subsequently bifurcated into the material (biology) and immaterial
(information) domains. Instead what we see is a continual process
of differentiations, transdifferentiations, and connections of terms
that are at once ontological and thoroughly pragmatic – that is,
a diagram.
In the case of information security, biological tropes began
being applied to accidental or intentionally caused glitches in
computer systems in the mid-1960s, with the first intentionally
designed computer viruses (e.g. ‘Darwin’ and ‘Cookie Monster’).
Many of these vague uses of biologically-inspired terms were crystallized
in the work Fred Cohen, whose writings on computer viruses were
published in the 1980s, just as personal computing and civilian
Internet technologies were gaining momentum. The language of computer
‘viruses’ and ‘infected’ computer systems continue to characterize
more recent descriptions of Trojan horses, Internet worms, and ‘5th
generation polymorphic’ viruses. Currently, information security
has expanded its approach to include ‘adware,’ ‘spyware,’ and even
‘spam’ email. Generally speaking, information security concerns
itself primarily with ensuring the ongoing systemic integrity of
a given computer system or network. This, of course, involves a
number of procedures, from identifying what a ‘system’ or ‘network’
is (e.g., an individual computer or a local network of computers),
to devising techniques for preventing intrusion and infection (e.g.
‘firewalls’ and ‘anti-virus’ software). Not surprisingly, the rhetoric
of war often accompanies the biologically inspired concepts of information
security, which has had the effect of making information security
for the average user an everyday battle.
However, the basic premise of information security is that specific
types of computer behaviors can be understood through the lens of
biology. If, as the analogy goes, a piece of software can infiltrate
and infect a computer system just as a virus can infiltrate and
infect a biological system, then it follows that the best way to
prevent such attacks is to construct an ‘immune system’ for the
computer. As one research article states, ‘improvements [in computer
security] can be achieved by designing computer systems that have
some of the important properties illustrated by natural immune systems’
(Forrest et al., 1996: 1). Furthermore, just as immunology is predicated
on the ‘self-nonself’ distinction, ‘the problem of protecting computer
systems from malicious intrusions can similarly be viewed as the
problem of distinguishing self from nonself’ (Forrest et al., 1996:
3). In addition, designing such computer immune systems require
not just the micro-view of immunology, but also a knowledge of the
macro-view of epidemiology, or how infectious agents spread throughout
a population. The research on ‘computer epidemiology’ makes just
this argument. For instance, Kephart et al. (1993) that a focus
on the modes of distribution of computer viruses, including their
birth rates, death rates, incident, and threshold, can offer a more
effective, global view of how computer viruses affect not just single
machines, but entire networks of machines.
Most recently, this view has influenced the emerging field of
‘network science,’ whose scope is not limited to the biological
or informational domains, but proposes a synoptic view of networks
as both ubiquitous and universal. Albert-László Barabási’s work
on ‘scale-free’ networks (in which few nodes are highly connected,
a many nodes are minimally-connected) has suggested that traditional
methods of tracking down computer viruses are determined to fail
in complex networks (Barabási, 2002: 123-42). Instead, Barabási
suggests that an approaches that ‘discriminate between the nodes,
curing mostly the highly connected nodes, can restore a finite epidemic
threshold and potentially eradicate a virus’ (Barabási and Dezsno,
2002: 1). In other words, the points of a network that are the most
connected are also the most vulnerable to attack or infection. Countering
the spread of a computer virus or worm will depend not on targeting
individual pieces of software, but on managing the traffic at the
most busy nodes or hubs within a network.
These are all examples of the way in which biology influences
computer science – or, to be more specific, the ways in which concepts
and models from immunology and epidemiology influence information
security. But the reverse also occurs, and in this regard, epidemiology
is an important hinge between computer science and biology. While
recent information security research has incorporated the metaphors
and concepts of epidemiology, the much lengthier history of epidemiology
shows a close relation to mathematical and informatic modes of understanding
disease at the macro-level. In 17th century London, the
weekly mortality tables compiled by parish clerks provided the basis
for the demographic studies of John Graunt, whose mathematical analyses
reveal trends in infant mortality and fatal diseases in select urban
areas. [1] Another
statician, William Petty, characterized such studies as ‘political
arithmetic’ or ‘political anatomy.’ [2]
This mathematical view of death and disease
as the macro-level are, as Michel Foucault points out, intimately
tied to the intersections of politics and medicine of the time.
The controversies surrounding the English Poor Law, the medical
reforms of the Hôtel Dieu in Paris, and the development of
a system of ‘medical police’ in Prussia, are all profoundly connected
to the growing interest in a quantitative, mathematical view of
disease at the macro-level. [3]
This ‘statistical enthusiasm,’ as historian
Ian Hacking calls it, was not only concerned with charting the spread
or patterns of a population’s health, but it was also centrally
concerned with the articulation of specific categories into which
disease and population types could be set. ‘Enumeration demands
kinds of things or people to count’ (Hacking, 1982: 280).
Epidemiology, in its historical context, was not just a matter
of counting, however. It required an ‘open field’ of observation,
and an analytical sensibility that could encompass the indeterminate.
An epidemic disease was not an autonomous entity that could be enclosed
in a box, or categorized in a table; its totality lay precisely
in its continual or recurring nature. Throughout the 18th century,
epidemiology came to be opposed to the classificatory science of
nosology, and it was this time-based, distributed view that led
to the recognition of the network effects of disease: ‘The analysis
of an epidemic does not involve the recognition of the general form
of the disease, by placing it in the abstract space of nosology,
but the rediscovery, beneath the general signs, of the particular
process, which varies according to circumstances from one epidemic
to another, and which weaves from the cause to the morbid form a
web common to all the sick’ (Foucault, 1973: 24). In this sense,
the concurrent observations of John Snow and William Budd, both
studying the effects of cholera in the 19th century,
can be seen as demonstrations of this point. [4]
In particular, Snow’s famous epidemiological maps of south London
reveal a concept that is central to network thinking: the layering,
in one space, of different types of networks (e.g. networks of infection,
networks of water pumps and sewage channels, and the overall socio-economic
topology that described the particular Broad Street neighborhood).
What we can highlight in epidemiology is a two-fold network consciousness:
an awareness of ‘epidemics’ as discrete entities displaying network
properties, and, inseparable from this, an awareness of the need
for network-based techniques for analyzing, mapping, and securing
against epidemics. Influenced by the mathematical epidemiology of
Norman Bailey, contemporary network science has taken up many of
the lessons of epidemiology – as well as information security. As
Duncan Watts notes, ‘viruses, both human and computer, essentially
perform a version of what we have been calling a broadcast search
throughout a network,’ a mode of propagation in which ‘the more
contagious a virus is, and the longer it can keep the host in an
infectious state, the more efficient it is at searching’ (Watts,
2003: 166). Thus, understanding the characteristics that define
an epidemic is a first step towards devising strategies for counteracting
it. For this reason, it is no surprise that surveillance, or the
gathering of information, is a central part of public health and
epidemiology. ‘The old simple schema of confinement and enclosure
– thick walls, a heavy gate that prevents entering or leaving –
began to be replaced by the calculation of openings, of filled and
empty spaces, passages and transparencies’ (Foucault, 1979: 172).
It is this shift towards contagion and/or transmission that we are
witnessing today.
Pathogenic Information vs. Informed Pathogens
So we have two separate fields, each of which integrates informatics
and materiality differently through a network paradigm –
this last part is crucial. If information security tells us that
certain kinds of computer behavior can be understood through the
lens of epidemiology, then it is equally important to note that
modern epidemiology tells us that infectious disease can be understood
through the lens of mathematics, statistics, and informatics. In
one the basic idea is that we can understand particular types of
computer behavior through the lens of biology, while in the other
the basic idea is that we can understand infectious disease through
the paradigm of informatics.
This uneven, twofold integration results, however, in two opposing
ontological positions. Recall our opening discussion regarding contagion
and transmission. While the view of contagion presumes a condition
of biological materiality, that can then be abstracted into metaphor
(computer ‘virus’), when contagion is considered within epidemiology,
it also implicitly links contagion with material and biological
processes of rate of infection, logistic growth, and epidemic thresholds,
encapsulated in the often-referenced SIR (susceptible-infected-removed)
model. [5] But
these material and biological processes are, in epidemiology, also
informational processes that reflect the specific topology of an
infectious disease. Mathematical epidemiology, despite its abstruse
qualities, must, by definition refer to a real biological-material
condition (if for no other reason than this material condition provides
the basis for data abstraction).
But the same conundrum also holds for the view of transmission,
and the field of information security. While the view from classical
information theory assumes an immaterial core that can then be instantiated
in a range of material, physical media (the assumption behind simple
file conversions), transmission is also never separate from its
materiality. Indeed, there is a ‘materiality of informatics,’ in
that the classical separation of ‘message’ from ‘channel’ is only
a heuristic means of assessing the accuracy of information transmission.
The reality is that information is never separate from its channel,
just as the message is never separate from its medium. Not only
does the supposedly immaterial quality of information always require
a material substrate (radio towers, fiber optic cable, WiFi transmissions),
and not only does information ‘matter’ in its social effects, but
transmission is inseparable from its materiality.
Therefore, while the relationship between contagion and transmission
is not an exactly symmetrical one, we can derive two distinct positions.
While information security views information as being immaterial,
epidemiology is predicated on the assumption that information is
material. In the former position what we see is pathogenic information
– that is, information in the classical, technical sense that has
become ‘viral’ – while the latter position what we see are informed
pathogens – that is, biological epidemics that, through epidemiology,
become information-dense entities. From this, we can say that information
security, as a field, deals with pathogenic information, while mathematical
epidemiology deals with informed pathogens.
Both, however, are united in their use of the ‘network paradigm’
to comprehend their objects of study. In both cases, the ‘network’
serves as the model through which the apparently disparate phenomena
of infectious disease and computer processes can be analyzed. However,
while the view of pathogenic information (information security,
computer ‘viruses’) assumes information as immaterial, the view
of informed pathogens (mathematical epidemiology) presumes a material
aspect to information. The question we can now ask, is what sorts
of networks result when these apparently opposing views of contagion
and transmission are layered on top of each other?
DSN, not DNS
In the past five years or so – and especially in the time since
9/11 – there have been a number of efforts to develop disease alert
and response systems that would make use of information networks.
The US CDC (Centers for Disease Control and Prevention) began a
number of such projects in the 1990s, with acronyms such as LRN
(Laboratory Response Network) and NEDSS (National Electronic Disease
Surveillance System). [6] The
impetus behind such programs was the alarming number of new and
emerging infectious diseases being tracked nation-wide by the CDC,
and internationally by the WHO (World Health Organization). In addition,
the 1980s and 1990s saw a number of instances of biological sabotage
(often by religious cults), both within the US and in other countries
such as Japan. [7] Such
events, combined with evidence suggesting a Soviet offensive bioweapons
program in 1979, collectively made ‘biodefense’ an increasing concern
of both public health and national security within the US. [8]
It became evident that an information network like the Internet
could be a crucial tool in enabling health officials to foresee
potential outbreaks before they have a wide-spread effect on a population.
In recent years, two events in particular have given the need
for such programs greater urgency. One is the 2001 anthrax attacks
that occurred within the US, in which several letters containing
a weaponised strain of anthrax in powdered form were sent through
the US postal system to media and government offices in New York
and Washington, DC. While the anthrax in the letters did not cause
a nation-wide or state-wide epidemic, it did cause what one journalist
called ‘mass disruption,’ triggering a state of public alarm through
the elaborate media coverage given to the events. Undoubtedly the
anthrax attacks were but one important factor behind the 2002 Bioterrorism
Act, which, among other things, restricted the access to and research
on approximately fifty ‘select biological agents’ – even within
legitimate, university-based biology labs receiving government funding.
The other event that has made the need for alert and response systems
more urgent was the 2003 SARS epidemic. While SARS barely deserves
the title of ‘epidemic’ in comparison to AIDS and tuberculosis worldwide,
the condensed time span in which it spread from China to Canada
made it a perfect case study for next-generation alert and response
systems. In fact, it was, in part, thanks to the WHO’s ‘Global Outbreak
Alert and Response Network’ that the spread of SARS was limited
to the cities through which it traveled. [9]
Coordinating among hospitals and clinics in
infected areas in Beijing, Singapore, Toronto, Hong Kong, and elsewhere,
and making use of a central server to upload and download patient
data, the WHO was able to issue travel advisories and suggest countermeasures
to the spread of SARS. In a sense, the WHO’s network provided a
proof-of-concept that information networks could be effectively
used in countering epidemic outbreaks.
This idea – the use of information networks to monitor, prevent,
and counter-act epidemics – is called ‘biosurveillance’ by the US
government. The systems that are used are variously referred to
as ‘syndromic surveillance systems’ or ‘disease surveillance networks.’
For the sake of brevity, and following upon the penchant for acronyms
in government agencies, we can broadly refer to them all as disease
surveillance networks or simply ‘DSN’ (not to be confused with ‘DNS,’
or the ‘domain name system’ that hierarchically stratifies Internet
server addresses). In the wake of 9/11, the US Department of Homeland
Security and Department of Health and Human Services has been especially
active in promoting the need for a sophisticated, nation-wide DSN.
Since the late 1990s, prototype DSNs have been active in multiple
cities nationwide. [10] In
early 2003, the Homeland Security ‘BioWatch’ program was tested
in a number of American cities, with the cooperation of state and
local governments. [11]
The BioWatch system routinely took air samples to test for the presence
of biological agents, and was connected to a network, through which
it would send the data to be processed. This program became the
forerunner of the US Biosurveillance program, which received a record-setting
$274 million for the development of DSNs alone. The program aims
to ‘enhance on-going surveillance programs in areas such as human
health, hospital preparedness, state and local preparedness, vaccine
research and procurement, animal health, food and agriculture safety
and environmental monitoring, and integrate those efforts into one
comprehensive system’ (US Department of Homeland Security, 2004).
Proposals and projects surrounding DSNs are, as of this writing,
growing at an exponential rate, and include projects underway both
from the private sector as well as government-funded, university-based
research.
Networks Fighting Networks
The existence and development of DSNs is noteworthy for a number
of reasons. Chief among these is the way in which the DSNs integrates
– or appears to integrate – the contrary views of contagion and
transmission mentioned above (the view of ‘pathogenic information’
versus ‘informed pathogens,’ or information security versus mathematical
epidemiology). The DSNs bring together the views of contagion and
transmission into a single ‘artifactual’ system. On one view, ‘information’
is assumed to be immaterial (in that it is a unit of quantitative
abstraction), but it operates through a biological process (in that
the computer ‘virus’ has as its aim the infection of hosts and replication
of itself). In other words, in the view of information security,
biological process is abstracted from biological materiality, and
is seen to inhabit the so-called immaterial domains of data and
light.
This is countered by the other view, in which ‘information’ has
to be material in order for an analysis to be accurate, or for a
model to be effective. If there is no correspondence between an
epidemic model and the actual epidemic, then epidemiology and public
health have no ground on which to stand. Thus, even mathematical
epidemic models are always forced to begin from empirical data.
Yet, at the same time, there is ambiguity in this materiality of
information. For, in the case of epidemiology, biological or medical
information is understood both as a product of knowledge-based systems
(e.g. medical records and disease statistics), and as a real ‘thing’
that spreads throughout a population (e.g. mutations in the RNA
‘code’ of a virus that enables it to evade medical therapies). In
other words, in epidemiology – more specifically in its mathematical
guises – informational processes are extracted from the particular
media through which and across which information flows. These views
intersect in the DSNs. Between the genetic code of a virus, the
rate of epidemic growth, its demographic distribution, and the role
of medical records, health insurance policies, and sales of pharmaceutical
vaccines, there is an ambiguous continuum of informational processes
that is informatic and yet thoroughly material.
One way of understanding this ambiguity with regards to DSNs is
to return to the concept of the network. What is perhaps most striking
about DSNs and the very idea of biosurveillance, is the way in which
it positions one type of network (a computer network) against another
type of network (a biological network). For many epidemiologists,
the 2003 SARS epidemic has become a case study in this regard. The
WHO’s outbreak network – which included network servers and software,
as well as conferencing technologies – intentionally positioned
itself as a network against the spread of the SARS coronavirus.
During the outbreak, a Newsweek article (28 April 2003) summarized
this view: ‘a 32-year-old Singaporean physician had attended a conference
in New York and was on his way home—and he was exhibiting suspicious
respiratory symptoms. Reports of cases in Canada and Singapore had
recently made their way to Geneva; the predawn call made the situation
all the more urgent. WHO officials tracked the man to a Singapore
Airlines flight, due in Frankfurt at 9:30 that morning. By the time
the plane touched down, quarantine specialists in goggles and jumpsuits
were waiting to take the doctor and his two travel companions to
an isolation ward.’ Such ‘preparedness and response’ actions involve
not just technology, but also negotiations among WHO officials with
governments and hospitals, from Toronto to Beijing. All these processes
of information exchange and communications constituted part of the
WHO’s counter-epidemic network.
The resultant effect is that of a real-time battle between networks:
one, a biological network operating through infection, but abetted
by the modern technologies of transportation; the other, an information
network operating through communication, and facilitated by the
rapid exchange of medical data between institutions. This is a situation
of what we can call networks fighting networks, in which
one type of network is positioned against another, and the opposing
topologies made to confront each other’s respective strengths, robustness,
and flexibilities. In their analyses of new modes of social organization
and conflict, John Arquilla and David Ronfeldt (2001) have pointed
to the importance of the network paradigm. What they call ‘netwar’
reflects the contemporary integration of information technologies
and network-based modes of political action, culminating in a Janus-faced
dichotomy between pro-democracy activism on the one hand, and international
terrorism on the other. As they state, ‘governments that want to
defend against netwar may have to adopt organizational designs and
strategies like those of their adversaries. This does not mean mirroring
the adversary, but rather learning to draw on the same design principles
that he has already learned about the rise of network forms in the
information age’ (Arquilla and Ronfeldt, 2001: 15). The take-home
message is that network forms of organization are highly resistant
to top-down, centralized attempts to control and restrain them.
Instead, the authors suggest that ‘it may take networks to fight
networks’ (Arquilla and Ronfeldt, 2001: 327). In this case, biosurveillance
and DSNs can be seen as initial attempts by governments to re-frame
public health within the context of information technologies and
national security. [12]
However there are a number of significant differences between
what Arquilla and Ronfeldt call ‘netwar’ and the example of biosurveillance
and DSNs. The majority of case studies that are considered under
the rubric of ‘netwar’ – case studies which range from the Zapatista
resistance, to the anti-WTO protests in Seattle and Geneva, to al-Qaeda
– imply human action and decision-making as a core part of the network’s
organization. In fact, one limit of the netwar approach is that
it does not push the analysis far enough, to consider the uncanny,
‘nonhuman’ characteristics of such networks. In a sense, the interest
in the study of network forms of organization is exactly in their
decentralized, or even distributed mode of existing – and for this
reason research in biological self-organization often provide a
reference point for netwar analysis (e.g. in studies of crowd behavior,
flocking, or swarming). Yet, as many studies make clear, the result
of netwar analysis is, ultimately, to gain a better instrumental
knowledge of the ‘how’ and ‘why’ of network forms of organization
(that many netwar studies have come out of the RAND think-tank environment
is indicative in this regard). In other words, approaches to studying
networks seem to be caught between the views of control and
emergence with respect to networks as dynamic, living entities.
On the one hand, networks are intrinsically of interest because
the basic principles of their functioning (e.g. local actions, global
patterns) reveal a mode of living organization that is not and cannot
be dependent on a top-down, ‘centralized’ mindset. Yet, for all
the idealistic, neoliberal visions of ‘open networks’ or ‘webs without
spiders,’ there is always an instrumental interest that underlies
the study of networks, either to better build them, to make them
more secure, or to deploy them in confronting other network adversaries
or threats. At the same time that there is an interest in better
controlling and managing networks, there is also an interest in
their uncontrollable and unmanageable character.
Indeterminate Control
The challenges put forth in this tension between ‘control’ and
‘emergence’ are not just technical problems, but are challenges
that raise ontological as well as political questions. From the
network perspective, case studies like the 2003 SARS epidemic look
very much like a centralized information network counter-acting
a decentralized biological network. The WHO’s outbreak response
network coordinated the exchange of data through network servers
and conference calls, and health advisories could then radiate from
this central node. By contrast, SARS infection was maximized by
moving through the highly-connected nodes of airports and hotels.
The strategy of DSNs, then, is to canalize transmission in order
to fight the decentralization of contagion. If an epidemic is ‘successful’
at its goals of replication and spread, then it gradually becomes
a distributed network, in which any node of the network may infect
any other node. [13]
Health officials warned in late 2003 that the SARS virus may very
well make occasional re-appearances during the cold and flu season,
implying that new and emerging infectious diseases are less one-off
events, and more of an ongoing milieu. By definition, if
a network topology is decentralized or distributed, it is highly
unlikely that the network can be totally shut down or quarantined:
there will always be a tangential link, a stray node (a ‘line of
flight’?) that will ensure the minimal possibility of the network’s
survival. This logic was, during the Cold War, built into the design
of the ARPAnet, and, if we accept the findings of network science,
it is also built into the dynamics of epidemics as well. While the
idea of totally distributed networks and ‘open networks’ have become
slogans for the peer-to-peer and open source cultures, the hybrid
quality of DSNs and biosurveillance (at once material and immaterial,
contagion and transmission) reveal the frustratingly oppressive
aspects of decentralization. Furthermore, the network organization
of epidemics are, as we’ve noted, much more than a matter of biological
infection; epidemic networks of infection are densely layered with
networks of transportation, communication, political negotiation,
and the economics of health care.
In DSNs, the tension between ‘control’ and ‘emergence’ points
to the ‘nonhuman’ character of networks. DSNs are nonhuman networks,
not because the human element is removed from them and replaced
by computers, but precisely because human action and decision-making
form constituent parts of the network. This point is worth pausing
on. Despite the technophilic quality of many biosurveillance projects,
their most interesting network properties come not from the ‘automated
detection systems,’ but from the ways in which a multiplicity of
human agencies produces a intentional yet indeterminate aggregate
effect. While much time and money is spent on computer systems to
model and forecast epidemic spread, such systems are always ‘best
guesses.’ The same is implied in the human involvement – autonomous
and conscious – in the epidemics that biosurveillance aims to prevent.
As we’ve noted, the layered quality of networks (infection, transportation,
communication) gives each particular epidemic incident a singularity
that frustrates any sort of reductive, quantitative modeling. In
short, for biosurveillance the challenge for the network management
of an epidemic is how to articulate control within emergence.
The nearly paradoxical question posed by biosurveillance with regards
to epidemics is this: is it possible to construct a network for
articulating intention within indeterminacy?
Let us rephrase the situation of biosurveillance in plain terms,
to make the political issues at stake clearer. An epidemic is underway.
An agency – the CDC for example – must develop and deploy a strategy
for containing the epidemic. Because epidemics are understood to
be network forms of organization, any attempts to contain and eradicate
the epidemic must similarly use a network approach. Thus, one network
– that of the CDC’s NEDSS – must counteract another network – that
of the disease. We thus have an instance of ‘networks fighting networks.’
However the two networks are not simply mirror images of each other.
The CDC’s network is a centralized network that makes use of information
technologies, while the epidemic is a more decentralized combination
of biological, technological (e.g. air travel), and other types
of networks. To prevent the latter network from becoming more diffuse,
the former network becomes more canalized, or rather, more selective.
Thus, the main challenge put forth to the first network (the CDC)
is how to intervene in, perturb, and shape the topology of the second
network (the disease). Meeting this challenge means, then, deciding
on the exceptional instances in which intervention and action is
warranted. Intervention itself is not so much the issue; rather,
it is the decision on intervention that is at stake.
Network Exception
We can summarize this even further: the challenge of biosurveillance
is the challenge of establishing sovereignty within a network.
As a political and juridical term, the concept of sovereignty is
already defined by paradox. As Giorgio Agamben (1998) notes, the
defining feature of modern sovereignty is not that it is the power
to execute the law, but that it is the capacity to claim the exception
to the rule. ‘I, the sovereign, who am outside the law, declare
that there is nothing outside the law’ (Agamben, 1998: 15). Agamben’s
dense and thought-provoking analysis suggests that, when sovereignty
establishes itself in this way (as the exception to the rule), it
necessarily produces its other in the figure of ‘homo sacer,’
or the ‘bare life’ that is outside both the political and social
orders (‘life that can be killed and yet not sacrificed’). Sovereignty’s
own injunction is to be at once outside and inside the juridical-political
order, at once legitimized through law, and yet capable of deciding
when the law should be suspended. What is captured in this no-man’s-land
is ‘bare life,’ life that is outside of the political order, and
yet, by being abandoned in this way, is also inscribed within it.
The radicalism of Agamben’s proposal is that this logic is common
to both the totalitarianism of National Socialist medicine, as well
as to the discourse of ‘human rights’ that emerged in the post-war
era. [14] Both
the claim to protect the population from hereditary disease, and
the claim that human beings, by the fact of being alive, have inalienable
rights, draw upon the ‘zone of indistinction’ between sovereignty
and ‘bare life.’ Whenever ‘bare life’ or ‘life itself’ is at stake,
the population or body politic is also at stake, legitimizing emergency
measures, or the declaration of a ‘state of exception.’ In this
way, sovereignty makes itself known at the very point at which ‘bare
life’ comes under threat, in the state of emergency or state of
exception. As both Agamben and Foucault note, this sovereign decision
on ‘life itself’ is also often a decision on ‘death itself.’ When
the state of exception is in effect, then the defense of the ‘life
itself’ of the population depends on a range of exceptional measures
or actions taken, actions which often have ambivalent effects.
No other ‘state of exception’ is quite as exceptional as an epidemic
– except perhaps war. In fact, the most powerful state of exception
is one that is not recognized as such. The sovereign exception obtains
its most intense level of legitimation in an environment in which
the exception is the rule – that is, a situation in which ‘exception’
is directly correlated to a ‘threat’ that is, by definition, indeterminate.
In this regard nothing is more exceptional than the inability
to distinguish between epidemic and war, between emerging infectious
disease and bioterrorism. Although, wars have the benefit of
being waged by individual and collective human agents, humans fighting
humans. Epidemics ignite public fears with great ease, in part because
the ‘enemy’ is often undetected, and therefore potentially everywhere.
But more than this, it is the alien, nonhuman character of epidemics
that incite public anxiety – there is no intentionality, no rationale,
no aim except to carry out iterations of what we understand to be
simple rules (infect, replicate, infect, replicate…). The exceptions
of epidemics and war implode in biological warfare, bioterrorism,
and in the way that US policy enframes the public health response
to infectious disease. In the US, the rubric ‘biodefense’ – which
is increasingly taking on epidemic proportions itself – has come
to incorporate within itself what was, at least on an institutional
level, the non-defense concerns of public health. A recent White
House press release states that ‘the [US] President believes that,
by bringing researchers, medical experts, and the biomedical industry
together in a new and focused way, our Nation can achieve the same
kind of treatment breakthroughs for bioterrorism and other threats
that have significantly reduced the threat of heart disease, cancer,
and many other serious illnesses’ (White House, 2003).
The ‘biopolitical’ analyses of sovereignty by Agamben and Foucault
become more complicated with biosurveillance and DSNs. This is because
of the way in which biosurveillance ambiguously integrates the informatic
and biological views of epidemics, producing an implosion between
the immaterial and material, model and object, concept and entity.
But the situation regarding sovereignty is also more complicated
because of the network properties of DSNs and the epidemics they
are designed to combat. In a sense, biosurveillance and DSNs are
emblematic of the challenge facing many network forms of organization
today – the challenge of the role of sovereignty within networks
(or what Negri refers to as the ‘political problem of the decision’
in the multitude). To posit the need for network strategies to fight
network threats is one thing, but it is quite another to place such
strategies within governmental and institutional structures that
are anything but distributed. The overarching goal of the DSNs becomes
suddenly ensnared in the multiple agencies and interests involved
in the network. This problem can already be witnessed in current
US biodefense policies and practices. While no one will deny that
bioterrorism does present a significant threat today, the DSNs that
have been deployed and that are currently in development have raised
a whole host of ethical and political issues: the confidentiality
regarding a patient’s medical records, the impact of biosurveillance
on public health care systems (most notably health insurance), the
question of mandated or voluntary reporting of medical data by physicians,
and finally the concern of designing secure information networks
dedicated to DSNs – this last issue being particularly interesting,
since it posits a scenario in which a computer ‘virus’ may disable
the capacity to stop a biological virus. [15]
When networks fight networks, the characteristic political response
has been to rely upon the structure of sovereignty to intervene
in and define the topology of the networks. The collection of information
by Homeland Security officials is predicated on the sovereign ‘state
of exception,’ and this same logic is being carried over into the
information networks that underlie the various DSNs that are part
of the US biosurveillance endeavor. We have, with DSNs, not just
the use of new tools for the same old job, but rather the construction
of exceptional topologies, in the sense of an ongoing ‘state
of exception,’ preparedness, and readiness for a threat that is,
by definition, immanent to the network itself. As an NEDSS fact
sheet notes, ‘the long-term vision of NEDSS is that of complementary
electronic information systems that automatically gather health
data from a variety of sources on a real-time basis; facilitate
the monitoring of the health of communities; assist in the ongoing
analysis of trends and detection of emerging public health problems;
and provide information for setting public health policy’ (NEDSS,
2000). The WHO’s response to SARS is another exceptional topology,
a hybrid of computers, communications, hospitals, health advisories,
and what the US calls ‘medical countermeasures’ such as quarantine
and travel restriction.
Again, in order to grasp what is at stake ontologically, it is
important to resist a simple moral understanding of DSNs,
as if the mere fact of surveillance in itself is a ‘bad’ thing,
a sign of the further ‘medicalisation’ of society. The demonstrated
success of the WHO’s network makes such condemnations difficult.
And yet, without a doubt, biosurveillance programs such as those
in the US are in the process of casting the ‘medical gaze’ further
than it has ever been cast before. This is why biosurveillance has
to be regarded as a topological problem as well as a political problem.
DSNs are caught between the recognition of the need to fight networks
with networks, and the insistent need to establish sovereignty within
the network. For this reason, we may see the situation of ‘networks
fighting networks’ become the rule rather than the exception. In
the condition of a normative state of exception, they may remain
continually operative, but relatively invisible in terms of its
effects. Until, of course, a threat is identified, at which time
the network topology may undergo a sudden, even violent contraction
(bioterror alerts, seizure of materials, detention of individuals,
Haz-Mat inspections).
Political Vitalism
This sovereignty of ‘exceptional topologies’ – the mode of sovereignty
specific to networks – is currently having a number of concrete
effects in shaping US biosurveillance and biodefense policies. One
is that there is no longer any strict division between ‘naturally-occurring’
infectious diseases and what the CDC calls ‘intentional epidemics’
(bioterrorism). In a sense, biosurveillance has surpassed even the
most avant-garde cultural theory, disregarding the traditional divisions
between nature and culture. If their causes are different, from
the point of view of ‘security,’ their effects are the same. (And
indeed one of the fearful aspects of bioterrorism is the unknown
and indeterminate impact of an artificially-induced, or worse, genetically-engineered
epidemic.) If epidemics and bioterrorism are, from the biopolitical
perspective of ‘security,’ the same, then it follows that medical
practice and health care systems will increasingly be called upon
to participate in the concerns of national security and defense.
This is not unique to biosurveillance programs today, however. The
history of epidemiology, statistics, and demography reveals a long-standing,
implicit collaboration between medicine and government (of which
the idea of ‘public health’ is but one result). [16]
Furthermore, military research programs in
the US and other Western nations have, at least since World War
II (and, arguably, after the first biological sabotage programs
in World War I), made biology and medicine part of defense. [17]
What is unique about contemporary biosurveillance
is the unofficial and vague incorporation of medicine into national
security. Such vagueness comes out in the concerns over the degree
to which physicians, nurses, and health practitioners may in the
future become obligated by law to report specific types of medical
information.
A blurring of distinctions, then, is one effect of the ambiguousness
regarding control versus emergence in biosurveillance. The ‘complex’
and ‘emergent’ properties of networks, be they biological or otherwise,
serves as the rationale for a technically-sophisticated surveillance
system that has, as its long-term goal, the total integration with
federal and local healthcare infrastructures. Yet this immanence
of biosurveillance has a flip-side, which is the language of ‘threat’,
’security,’ and ‘defense,’ a language of networks fighting networks
that necessitates exceptional measures to intervene in and shape
networks. On the one hand, the DSNs will be invisible and immanent,
part and parcel of medical practice and public health. On the other
hand, that same DSNs may, in times of crisis or a state of emergency,
become suddenly contracted and highly centralized. What this masks,
of course, is the way in which the DSNs are always in a continual
state of emergency. ‘Preparedness’ simply becomes actualized in
‘emergency,’ both of which are predicated on the sovereign exception
acting within a network.
The challenge to epidemiology and public health, then, is to confront
the paradoxical claim that ‘networks are needed to fight networks.’
In other words, the study of epidemics, and the application in biosurveillance
and in DSNs, presents us with a situation in which the need for
control is also, in some way, the need for an absence of control
(‘emergence,’ ‘self-organization,’ and so forth). An approach that
concentrates on eradicating the ‘disease itself’ through vaccination
will only ever follow the epidemic. Thus, the search for the ‘disease
itself’ will only result in finding the disease everywhere in general,
but nowhere in particular. And yet, any attempt to design preventive
systems inevitably implies the design of preemptive systems,
and the acceptance of the ambiguous politics associated with the
doctrine of preemption. In this regard, Agamben’s (1998) comment
that ‘biopolitics necessarily turns into thanatopolitics’ takes
on a new meaning.
DSNs such as those of the WHO, the CDC’s NEDSS, and Homeland Security’s
BioWatch system, are all examples of attempts to use networks to
fight networks. In many cases, as we’ve seen, the strategy is to
deploy a centralized information network to counteract the decentralized
(or ‘becoming-distributed’) network of an epidemic. However, what
often goes unrecognized is that the effectiveness of the WHO’s network
may not be due to the technical existence and deployment of information
technologies, but to the degree to which the WHO’s health advisories
were carried out at local levels – that is, ‘downstream’ from the
central node, at the sparsely-connected peripheries of the network.
In many cities, including Singapore (where health ‘kits’ were made
available to civilians), the transmission of knowledge about the
contagion was key to preventing further epidemic spread. This depended
not upon WHO or state officials, but, ultimately, on the more ‘horizontal’
interactions between local agencies (clinics, physicians, nurses,
educators, volunteers). Again, the point is not to seek to idealize
the inherently liberal principles of decentralized or distributed
networks, but to notice the following: the situation of ‘networks
fighting networks’ puts forth a challenge to us to rethink traditional
notions of ‘control’, ‘decision,’ and ‘action,’ or what these terms
may mean in a given network-based context.
Can we imagine a situation in which both networks are decentralized,
or even distributed? Would this be a desirable thing, or would it
signal a greater fatality for our intention to manage and control
an epidemic? It is not difficult to imagine a range of possible
scenarios based on the current political climate. One is a scenario
in which a real-time DSN is established on its own dedicated Internet,
on which it runs automatically, without human intervention. This
is, in a sense, the biomedical equivalent to the computerized command-and-control
weapons systems of the Cold War. Despite the science fictional overtones,
the automation of DSNs occupies a significant portion of the research,
and at least one automated system – the RODS or Real-time Outbreak
and Disease Surveillance system – was implemented at the 2002 Olympic
Games in Utah. [18] Another
imaginary scenario comes, interestingly enough, from computer science.
In 2003, when the ‘Blaster’ virus made its way through the Internet,
an attempt was made to design a software ‘vaccine’ to Blaster, or
a ‘good virus.’ [19]
Dubbed ‘Naachi,’ this ‘good virus’ would travel through the Internet,
checking computers to see if they were vulnerable to the particular
type of attack that the Blaster virus used. If a computer was found
to be vulnerable, Naachi would automatically download a patch from
the Microsoft website (Blaster only infected PCs running Windows).
All this network activity would be happening in the background,
with the computer user only half-aware of what was taking place.
Unfortunately, due to excessive Internet traffic to and from the
Microsoft website, Naachi caused more damage than it prevented,
clogging several commercial airline and Navy computer systems. But
it is not difficult to imagine the ‘good virus’ example carried
over into biodefense. The prospect is harrowing: from a strictly
network perspective, wouldn’t the best network counter-offensive
be a benign virus, one that would inhabit the very air we breathe,
vaccinating us against a potential threat that we did not know existed?
And, if the best way to fight networks is with networks, then wouldn’t
this necessitate a de-emphasis on human-centered action, and an
increased emphasis on the ‘vital’ properties of the network in itself?
In such an instance, would it still be possible to distinguish contagion
from transmission?
Author's Biography
Eugene Thacker is Assistant Professor in the School of Literature,
Communication, and Culture at Georgia Tech. He has written extensively
on the relationships between biology, informatics, and politics,
and is the author of two books: Biomedia (University of Minnesota,
2004) and The Global Genome (MIT, 2005).
Notes
[1] See Rosen, 1993, pp. 251-63.
[back]
[2] See Porter, 1997, 236-37. [back]
[3] See Foucault, 2000, pp. 134-56. [back]
[4] See Porter, 1997, pp. 412-14. Also see Snow’s
pamphlet On the Mode of Communication in Cholera, published
during the 1849 outbreak in London. [back]
[5] The SIR model measures the probability that
a disease will become epidemic for a particular population. Individuals
within a population are characterized a ‘susceptible’ (vulnerable
to infection), ‘infected’ (capable of infecting others), or ‘recovered’
(either through acquired immunity, medical intervention, or possibly
death). The threshold of epidemicity is when the overall transition
from ‘susceptible’ to ‘infected’ is greater than the transition
from ‘infected’ to ‘removed.’ For a description of the SIR model
in epidemiology, see Watts, 2003, pp. 168-74. [back]
[6] Information about these and other CDC-based
surveillance projects can be obtained online at http://www.cdc.gov.
[back]
[7] See Miller et al., 2002, pp. 15-33, 151-54,
160-63. [back]
[8] See Alibek and Handelman, 1999, and Miller
et al., 2002, pp. 135-37. [back]
[9] For more on the WHO’s ‘Global Outbreak Alert
and Response Network’ go to http://www.who.int.
[back]
[10] See the 2000 CDC report "Preventing
Emerging Infectious Diseases: A Strategy for the 21st Century,"
available online at: http://www.cdc.gov/ncidod/emergplan/.
[back]
[11] For an example, see Hoffman et al., 2003.
[back]
[12] However networks do not always fight other
networks; in many cases the networks can be ‘layered’ on top of
each other to produce an intensification, or a ‘network affect.’
In the case of the 2001 anthrax attacks in the US, for instance,
a minimally-effective biological network was abetted by two ‘layers’
of information networks: that of the postal system, and that of
the mass media. Through this network layering, the actual infection
of a small number of individuals had the impact of an epidemic (indeed
the threat posed by the anthrax attacks were the primary motive
behind the US Bioterrorism Act). In this case, qualitatively different
information networks were able to amplify the limited effect of
a biological agent. In other words, the layering of different types
of networks enabled an overall network amplification to occur. [back]
[13] Thus, the most ‘successful’ epidemic is
one that is virtual with respect to any actual node
on the network. [back]
[14] See Agamben, 1998, pp. 126-35. [back]
[15] On these and other issues, see the special
issue of JAMIA (Journal of the American Medical Informatics
Association), 9.2 (2002), on “The Role of Informatics in Preparedness
for Bioterrorism and Disaster.” [back]
[16] See Porter, 1997, pp. 397-427. [back]
[17] See Harris and Paxman, 1982, and Miller
et al. 2002, pp. 38-41. [back]
[18] See Gesteland et al., 2003. [back]
[19] For a brief summary, see the article “Attack
of the World Wide Worms,” Time (1 September 2003): 48-50.
[back]
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