Many are fleeing, everyone is fearful, you are neither – splendid, magnificent! For what is more foolish than to fear what you cannot avoid by any strategy, and what you aggravate by fearing? What is more useless than to flee what will always confront you wherever you may flee?

—Petrarch, Letters of Old Age.

What, then, can we conclude from this brief and superficial look at epidemic modelling on networks? I would like to think that there are several broad take-away messages.

The most important message by far is that – despite using advanced mathematics, detailed sets of parameters, and extensive computer simulation – modelling remains an inexact science. It’s important to qualify that word “inexact”: while models and simulations can generate results in extraordinary volume and with great precision, the interpretation of those results inevitably involves judgement calls. Many details remain unknown, and in many cases unknowable, perhaps because they cannot be properly measured, or perhaps because they change so fast that measurement is quickly outdated by events. Whatever the reason, no model in itself tells us anything; rather, they provide evidence to guide our thinking.

A corollary to this is that the policy responses to an epidemic can only partially be driven by, determined by, or justified by, the results of modelling. Policy remains an essentially political activity, and while it may be “driven by” or “informed by” science, there will always be other factors needing to be included that may skew a final decision away from what a scientist may view as “correct”. Many real-world problems are wicked, impossible to solve because of inherent contradictions and the compromises they imply, but mandating an immediate response nonetheless.

In many ways this makes modelling more important, not less. A model provides only a limited view onto any problem. But the fact that it can provide a view onto any problem means that we can explore problems we haven’t yet faced, explore techniques we couldn’t yet deploy in reality, and so forth. It is at least important to understand things that can’t happen as it is to understand those that can, if only to cut down the space of possibilities that need further consideration.

The second take-away message is the scientific underpinnings of many policies with which we’re familiar – so much so that they sometimes feel almost part of the world’s folklore. Vaccination, quarantine, physical distancing, herd immunity, and so forth are all susceptible to exploration and variation. And the science can expose commonalities that are not initially obvious: that the provision of protective equipment behaves like vaccination, for example, in the way it can be used to reduce the dangers of super-spreading. This can lead to alternative approaches.

The third message concerns countermeasures. We saw when we discussed adaptive countermeasures that variations in the efficacy with which the processes were carried out made a huge difference to the results. In the real world, of course, one may be stuck with ineffective processes: an imprecise test, a limited number of testers-and-tracers, and so forth. This may defeat even a well-thought-through strategy.

The implication of this is that to impose any set of countermeasures is to conduct an experiment – and the same is true of any attempt to unwind a countermeasure, such as for example when coming our of a physical distancing lockdown. It’s possible that the strategy will fail, and that measures will need to be re-imposed. This may be difficult for people to take, especially if they’ve not been warned of the possibility beforehand.

The final message is the most important for me as an academic: the democracy of science. People sometimes feel that science is something alien, requiring endless qualifications, state or corporate sponsorship, and access to techniques and tools that are out of reach of the amateur. Nothing could be further from the truth.

Science, as practiced by real scientists, is largely just an exerciseb scientific method – that has evolved over the years to help stop us misleading ourselves. The framework isn’t a barrier to entry into science; rather, it’s a guide to help identify simple truth within a complex reality.

The quotation from Rovelli with which we opened this book highlights that the conclusions drawn by science are always tentative and open to question, refutation, and overthrow. It’s working within this framework that makes a practice into science, not the letters after the practitioner’s name. And while we hope that well-qualified people are right sufficiently often to be trusted, that’s an authority that has to be earned and justified by a willingness to accept correction as part of the process of truth-finding.

If this book shows anything, I hope it’s that computational science is within the reach of everyone. It’s not the preserve of academics, although academic scientists have developed many of the ideas and tools; it doesn’t need supercomputers, although they’re often useful; and no-one should be afraid of posing questions: any question, sincerely asked, is worth asking, and worth the cost of working towards an answer.