Predicting epidemics

Some ideas from a meeting on Complex Networks, 2-6 July, Sardegna, Italy

With the threat of a global outbreak of the H5N1 virus hanging over our collective heads, it's natural to wonder about our science of prediction. We can send satellites into the remotest regions of the solar system, and predict some quantities of fundamental physics to one part in 10 billion. Boeing aircraft designs its new aircraft with computational simulations so accurate that test flights are no longer necessary; in fact, as one of their executives told me last year, Boeing only does flight tests to reassure an uneasy public! Given the power of today's science, shouldn't we be able to predict the likely outcome of a new viral outbreak?

One possible response is that we shouldn't hope to be so ambitious, because the spread of disease depends not only on biological factors -- the nature of the virus, for example -- but on what individual people do, on who meets with whom and where people travel. It's deeply entangled with free will and human psychology and all the unpredictability of human behavior, and so we shouldn't be surprised if the fate of an epidemic is a matter if chance and guesswork. But is the situation really so hopeless? Increasingly, network scientists don't think so. It's may just be a matter of bringing the right data to bear, and paying attention to the surprising architecture of real-world networks -- such as the network of international air travel.

The fact is that people aren't so unpredictable, especially at the collective level, and technology is making it possible to map out human interactions with more detail than ever before. Using mobile phones, for example, researchers have been able to build up detailed pictures of the social links between people in various communities. A couple years ago, researchers at Los Alamos National Lab used information gathered this way (and from more traditional surveys) to build a computational model that could mimic the evolution of an epidemic within a city by following the second-by-second movements of millions of individuals on their daily paths. This is the social equivalent of Boeing's flight simulations. With this computational tool, you can do experiments to test the consequences of various interventions. What happens if you close the schools, perhaps, or try to reduce the movement of people by public transport? One thing the Los Alamos group found was that the timeliness of the response is absolutely crucial -- measures save many more lives if they're implemented very early on in the course of the epidemic.

Yesterday morning, physicist Alessandro Vespignani spoke about recent work with Vittoria Collizza and other members of his group at the University of Indiana, which has been aiming to bring data on international air travel into such models, which is probably the most important factor for epidemic spread at the global level. They've used a massive data set for something like 3,100 airports worldwide, and 20,000 regular flight paths (you can see some animations of such data here), which reveal the larger-scale human flows around the world. Using this data to then model the spread of a disease -- introduced at one point, say, in Vietnam.

This modeling effort is the most ambitious yet to try to bring the data we have to bear on understanding what we're likely to face with an influenza pandemic. The first surprise that emerges from it is that trying to control the epidemic by reducing the flow of people -- taking the obvious step of restricting traffic through all airports, for example -- is remarkably ineffective. Even reducing the number of people passing through airports by as much as 50% has virtually no effect on the ultimate spread of an epidemic. To have much influence, the models suggest, authorities would have to reduce airport traffic by as much as 90% everywhere -- which from an social and economic point of view is probably a non-starter.

This may be a negative lesson, but at least it helps authorities know what NOT to waste their efforts on. A more positive message that emerges from this work is that the cooperative sharing of antiviral drugs between countries may well be the best way to the stem the spread of such a disease. (Unfortunately, I have to wonder, how likely is that?)

One other interesting point to emerge from this recent work (discussed more in this paper) is that the outcome of an epidemic may well be more predictable than one might expect. They've run their simulations over many times, seeding an epidemic with the same initial conditions. Although the simulations include lots of probablistic events (it depends on the virus passing between people, after all, which are chancy events), the overall outcome remains roughly the same. The reason, they suggest, is that the global air transport is dominated by channels going between major airports. These seems to act as preferred pathways or conduits along which the virus tends to travel -- and obviously represent good targets for, say, monitoring people for infection (if feasible).

This work obviously has huge implications for our collective well being. But it also makes the point that understanding social processes, especially at the largest collective level, isn't really hampered at all by the mysteries of human psychology. In many ways, we're akin to particles following fairly simple rules, and careful science can learn how to follow and hopefully influence those movements in an intelligent way.