Vital Systems Security

The recent H1N1 outbreak has been fertile ground for discussion on this site and elsewhere. Borrowing a term that Stephen once brought onto this blog, there is a veritable “smorgasbord” of objects, concepts, strategies and technologies to examine and reflect upon as the world comes to grips with a bona fide pandemic event (I’ll save for Carlo to verify this claim). One such first-order concept which I would like to introduce today is “situation awareness.” The term has bubbled up a great deal lately in discussions around H1N1, especially within the various operational structures and organizations, such as HHS and CDC, taking the lead on the response side of things.

At its core, situation awareness, or SA, basically describes knowing the right information, at the right time, in the right way and in the right amount, in order to make the right decisions to improve or protect health. Although it is ostensibly applicable across all domains of public health, as a concept it is usually associated – or in any event, it eventually ties itself back to – public health emergency preparedness and response. To give an example from H1N1, response has been very much guided by demands for information that go beyond what we might call “traditional” public health surveillance and epidemiological investigation. Certain disease detection and surveillance technologies have been employed in tracking the disease, but the sources of information are disparate and varied, requiring a great deal of filtering and integration to paint a meaningful picture. From aggregate disease reporting to case level data; media and internet search term tracking to hospital bed and patient tracking; virologic surveillance to border surveillance, the array of data being produced across heterogeneous sites that constitute an increasingly global health security apparatus is staggering. Contrast these information needs with the 1976 Swine Flu event, and one begins to see a stark shift in terms of the kinds of techniques brought to bear in developing useful information for response.

Many of us blogging on VSS are familiar with various public health capabilities in the context of preparedness and response. Community mitigation strategies, countermeasure delivery, mass prophylaxis and vaccination – these are just some of the capabilities to which public health is held accountable in carrying out its preparedness and response functions. More to the point, knowledge about the state of readiness and performance of these various capabilities is also very much at the core of situation awareness. Think of knowledge about capabilities as something like the second half of a feedback loop in the production of situation awareness, the first half of which is information gleaned from signals (like flu surveillance, or internet search term use, or BioSense, etc.) in the external environment. In theory, marrying these two produces a picture not just of a health threat in near real-time, it also produces a picture of what can be done about it – what resources can be brought to bear – in like time, suggesting what likely outcomes will be. This, in turn, can suggest future states, with future interventions and future outcomes, etc etc.

For students of public health, this is a far cry from the kind of rationality that has governed public health surveillance historically: namely, a focus on the aggregation of a variety of population-level data to determine risk factors (and interventions) associated with health outcomes. I would like to suggest that public health’s traditional techniques – surveillance, epidemiological investigations, laboratory testing, etc.- are, at least at certain sites and in certain contexts, being marshaled and modified in service to the emergent need (rationality?) for situation awareness. My core argument, which I’d like to test out in this forum, is that situation awareness embodies the demand for a new kind of knowledge, one premised less on the need for ever greater quantities of data, per se, and more on the need for timely, actionable information. Again turning to H1N1, this may account for the seemingly odd development that public health officials are no longer concerned, per se, with the accuracy of aggregate case counts. Generating that information is extremely resource-intensive and, to the point I am trying to make, not all that useful for public health decision-making. At the level of actual practice, this number, the all-important “numerator” in calculating disease incidence, and “denominator” in calculating case fatality ratios – and the very core of traditional epidemiological techniques – has been backgrounded in favor of additional types of information: characteristics in severe cases, transmissibility in specific settings, such as schools and hospitals, utility of community mitigation strategies such as school closures (VERY controversial, for about a thousand reasons), and viral susceptibility to treatment and prophylaxis.

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How has situation awareness emerged? What problems does it seek to address? What are its techniques? Its boundaries? What configuration of practices and knowledge arose to form it? What demands does it place on individuals? On other assemblages and apparatuses? These are complex questions worthy, I think, of further pursuit. Lyle, of course, has done a stellar job of getting at many of these issues in his discussion of BioSense and syndromics, and I should very much like to see continued discussion of, for example, the employment (failure?) of real-time disease detection technologies in the context of H1N1. A broader investigation tracing back the concept may prove fruitful. A cursory look shows situation awareness to have a very extensive history in military applications, notably around operator performance of a variety of technologies, including aircraft and other combat vehicles. (Good) pilots have, in fact, been held as exemplars in the field, having been very heavily scrutinized for how they are able to make sense of, and inject order into, a vast amount of very disparate and very quickly changing information in a high-stress environment – and achieve desired outcomes on top of that. Tracing back contemporary public health situation awareness across its various lineages in aviation, psychology, operations research and the military, could prove a very informative project.

Limor Samimian-Darash has a new article in American Ethnologist entitled “A pre-event configuration for biological threats: preparedness and the constitution of biosecurity events.”  The abstract is after the jump…

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I am meeting with the Beijing Haidian District CDC on Thursday Beijing time. As part of the meeting they have asked me to give a talk discussing H1N1 and US/WHO policies. I thought it would be useful to take advantage of our distributed network which has done a lot of thinking about this: what is the current word on H1N1 (swine-origin) influenza?

“The problem, WHO officials have said, is that after years of preparing for the threat of a pandemic strain of the deadly H5N1 avian influenza virus, many governments and people think of a pandemic as a deadly worldwide plague.”

Apparently, the WHO is now “very close” to declaring a pandemic. See below for an interesting CIDRAP piece.

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In a recent Newsweek article on the swine flu pandemic, Laurie Garrett ties together two seemingly disparate problems: the global meat industry and “viral sovereignty.” First, she analyzes the swine flu outbreak through the lens of disease ecology, whose vision of pathogenic globalization was a central element in the “emerging disease” framework as initially articulated in the late 1980s.  In this vision, new diseases are constantly emerging due to the conjuncture of natural ecological processes and massive human transformations of the environment:

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Over the last few weeks – both before and after they were officially released – a great deal of critical attention has been turned to the “stress tests” that were organized by the Treasury Department to determine the health of major U.S. banks. A major criticism of these tests has been that the “adverse scenario” used in them is actually not that adverse. It makes assumptions about economic growth, employment, rates of mortgage default, and other factors that hardly constitute a “worst case scenario.” Consequently, argue these critics, the “stress tests” do not really “stress” the banks at all. 

In light of this criticism, a discussion has emerged about what the point of these stress tests really are, and I think that this secondary discussion is quite interesting for us in understanding how these stress tests relate to some of the phenomena that we have been thinking and writing about. A major argument that has been made by defenders of these stress tests – and it is an argument that I find quite convincing – is that in fact the “adverse scenario” is in no way shape or form intended to present a “worst case” scenario. We know what would happen in a worst case scenario: the government would take these banks over and guarantee their deposits. Instead, the stress tests are supposed to present a “moderately bad” scenario, one in which it is possible that a system of regulation based on mandated reserve ratios or capital requirements would allow banks to weather the storm without further government intervention.

The point I would make here is this: In some important ways, the stress tests resemble catastrophe modeling on a technical level. That is, they collect a tremendous amount of data about the banking system, and then run simulations to understand how it would respond to certain adverse conditions. But on the level of political technology it works differently. In areas such as civil defense, pandemic preparedness, or catastrophe insurance, the point was really to understand how a system would respond if the worst imaginable thing actually came to pass. In the case of the stress tests, the question is whether the system could make it through a reasonably adverse economic scenario without external intervention.

Taking this point one step further, it seems that what is emerging is perhaps a two-tiered system for financial system security. On the one hand, there is continued acknowledgement that in the absolutely worst cases – the “unthinkable” economic emergencies – the remedy will be government intervention to save the banking system. On the other hand, mechanisms like the stress test are intended to ensure that the banking system could survive on its own under moderately adverse economic conditions. In the future, the stress test might, of course, serve another role as well, as was suggested yesterday in the New York Times Breaking Views blog: It could be institutionalized as the basis for a more flexible kind of financial regulation, one that did not set hard and fast rules about capital requirements or reserve ratios, but that used much more intensive exchange of data to assess banks financial position, and to try to prevent the kinds of risk exposures that led to the current crisis.

How does this map onto our thinking about emerging concepts like systemic risk regulation as an example of vital systems security?

Gary Becker and Richard Posner on the economics of pandemic preparedness. Can’t say it is massively enlightening. But the Posner in particular has an interesting update of Foucault’s cost-benefit analysis of vaccination (in this case related to vaccine developement and other preparedness measures) to the modern global pandemic.

People are actively attempting to infect themselves with the “mild” strain of H1N1, hoping to give themselves immunity in case more severe strains appear in the future. Resonates with the DIY Bio folks. Also, another example of the power of the 1918 pandemic analogy: the rationalization is that the 1918 flu had a mild spring wave, and two severe fall and winter waves. My sense is that there is no reason to assume this strain will behave similarly.

“What things are contemporary? Consider a late model car. It is a disparate aggregate of scientific and technical solutions dating from different periods. One can date it component by component: this part was invented at the turn of the century, another, ten years ago, and Carnot’s cycle is almost two hundred years old.” – Michel Serres (1995)

“Rabadan suggests the way to think of this flu is like a homemade car with parts from different vehicles. The parts have all been in several different vehicles before. Sometimes the combination of parts is a dud and the car doesn’t move. And sometimes you get a race car. A pandemic is a race car.” – Associated Press (5 May 2009)