Influenza Pandemic Guide For Geeks

I’ve read and seen quite a lot recently about the apparently inevitable ‘flu pandemic that is going to hit the world. In a way, this seems like a remote possibility; then again, it’s a very real threat that should probably be taken seriously. (I’m going to get my annual ‘flu jab next week and also get more ventolin inhalers: my mild asthma is the reason that I’m eligible for a ‘flu jab in the first place)

However, I’m not going to talk about the serious nature of a real ‘flu pandemic, but about the mechanics of viral epidemics themselves. In particular, I’m using online computer network analogies to describe the behaviour of real-world viruses. This is an intentionally tongue-in-cheek description. Conventionally, the analogies have worked the other way: people use descriptions of real-world epidemiology to help explain and understand online computer worms and viruses.

When a virus outbreak kicks off, online or offline, the most at-risk subjects (subjects: a neutral term used for both computers systems and for people) will be infected first. In the online world, this means systems live on the internet, possibly with security holes or unpatched applications, let’s say an unpatched Windows PC. Those subjects protected from infection are those with good security and up-to-date anti-virus systems. In the real world, the equivalent of ‘good security’ is probably good hygiene and ‘staying away from others known to be infected’, or possibly ‘staying away from everyone‘. Anti-virus software’s real-world counterpart is actually not really very different: your immune system, including vaccinations against known diseases. That’s exactly what anti-virus software is: a set of instructions telling the computing how to identify various malicious pieces of software. The human immune system uses similar techniques: your anitbodies will attack invading viruses, based on ‘knowledge’ of their appearance and structure.

Some viruses are more deadly than others. A virus which is good at spreading is generally not a virus which is fatal to its host. For example, if a virus which infects a Windows PC immediately trashes the hardware or the BIOS, then this arm of the virus epidemic stops. The destroyed PC cannot infect any more systems: it didn’t survive long enough for the virus to be propogated. In the real world, the same is true. The main reason that we haven’t seen a large-scale outbreak of something like Ebola is because it usually kills those infected very quickly. These epidemics burn themselves out very fast.

A more ‘successful’ virus is one which will spread to many hosts. In order to maximize its spread, ideally the virus should not harm the host in any way – in fact, its presence should not be detectable. In reality, this is fairly unlikely, although humans do harbour (and pass on) various viruses and bacteria which are largely harmless. However, restricting the discussion to viruses which cause harm as well as propogate, the viruses which spread most successfully are those which do so before symptoms of harm appear and which spread easily from one host to another. On a computer, a virus may be set to initiate its ‘damage the host’ routines after a set period of time since infection, or perhaps on a specific date. Typically, real world viruses often only begin to cause noticeable symptoms after a few days: in extreme cases, such as with HIV, it takes many years. The noticeable measure of spread typically relates to this ‘window of opportunity’ of undetected propogation between the host becoming infected and the host developing symptoms. The more hosts that an infected host can pass the virus on to during this window, the more rapid the progress of the epidemic. In an extreme case, if large pockets of a population become infected before symptoms appear in any host, then this will result in a very serious outbreak indeed.

Epidemics come in ‘waves’. The reasons for this are not completely understood, but typically the first wave of an epidemic is followed by a lull, when there are many fewer new infections, at least for a while. For example, a network worm may infect may susceptible Windows PCs, with millions of new infections each hour. But it can’t keep up that rate of infection: before long the population of uninfected, at-risk PCs is getting quite small. The virus has infected all the most infectible machines and there aren’t any easily-available new hosts to infect. In the real world, this is seen too: the first groups of people to be infected are those which are most at-risk. Typically, this means people who mix with many others, or who are intentionally mixing with individuals who are already ill, i.e. travellers by train or air, those in the medical profession. A small number of people carrying an infectious agent in such a group may spread it to a large number of others very fast. However, soon, so many of this group will be infected that there are fewer of them uninfected and thus the rate of virus spread drops.

Secondary waves of infection may occur, perhaps when those infected in the first wave pass it on to their families or other contacts. In the online world, this is perhaps like exposed systems on the network passing the virus on to secondary systems on their own networks, even though those secondary systems aren’t exposed to the outside world directly.

So, there you go. The Influenza Pandemic Explained For the Slashdot Generation. 🙂