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Models have become the tool of choice for telling the story of the pandemic. Problem is, in journalism they’ve landed in that murky spot where demand for future certainty meets desire for catastrophising clickbait. 

They’ve fed the drift from diagnosis to prognosis. It’s a jerk back to ancient Rome, where the high priests of journalism read the entrails of the moment not so much to explain what was happening, but to predict the future.

Now that facts about “what’s happened” are ubiquitous, journalists are seeking the scoop of being the first to predict what’s going to happen — and the more awful it looks, the more likely it is to go, well, viral. 

Right now there’s plenty of data to draw on. Everyone with a bit of confidence in their numeracy seems to have some data analysis. Spend any time online and you’ll know modelling has replaced Candy Crush as the online activity of choice, as people are punching in the daily publicly available COVID-19 data to understand where we’re at.

It’s been social media as a social good, providing real-time understanding of COVID and vaccination trends, with a public Twitter discussion about the appropriate application of statistical and epidemiological tools such as exponential growth, Reff, peaks and lags. Programmers have built easy-to-use mapping of exposure sites and vaccine availability from public and crowd-sourced data.

The media have climbed on board, including Casey Briggs at the ABC and the data team at Guardian Australia. People in Sydney are starting to make October dinner plans off the back of Briggs’ daily “best guesstimate” of when the state’s adults will be 70% double-vaxed.

In the UK, Financial Times’ John Burn-Murdoch has from the beginning popularised statistical tools like log graphs to simplify telling the comparative country-by-country COVID story.

Then there are the big models that make news in their own right: the now famous (or, depending on your priors, notorious) institutes like Burnet and Grattan in Melbourne and Kirby in New South Wales, and state and federal health departments. These are serious models by serious people, with serious real-world effects. 

As everyone who has spent five minutes listening to any premier or prime ministerial press conference knows, it’s the predictions in the Doherty Institute modelling that has determined that sacred text of Australia’s reopening — the national plan. (Although, like lots of sacred texts, it seems open to multiple readings.)

But here’s the thing: “All models are wrong,” as scientists (social and otherwise) have recognised since British statistician George Box acknowledged this bitter truth back in the 1970s. Of course, he also added: “Some are useful.” It’s the nuanced take on Mark Twain’s more dismissive “lies, damned lies and statistics”.

Models that explain the world as it is right now are not so much wrong as they are partial — simplified. In churning real-time data through historically determined ways in which the world sorta kinda works, they provide a useful tool for painting the present. 

That makes them useful, too, in providing context and depth to the core journalistic task of writing the first draft of history, of telling “what just happened”. 

But models are inherently less right — potentially more wrong — in predicting what happens next. As each future input becomes fuzzier, with outcomes and interactions more variable, they take on an uncertainty that should lead journalists to tread with caution.

It’s not the modellers’ fault. Models are rendered complex by that hardest of inputs to wrangle — changing human behaviour under rapidly changing circumstances. Particularly, as in this pandemic, when the models themselves can act to create — or undermine — their own reality. 

Proponents of nudge theory use predictive models to shape behaviour (“every cigarette is doing you damage”). On the other hand, modelling that suggests the need for continued hard lockdowns can, depending on the circumstances, encourage or undermine the community compliance on which the lockdowns depend. 

Governments, of course, love modelling. They use its predictive power to underpin the 21st century political marketing strategy of choice — “there is no alternative” — whether it’s models of China to justify nuclear submarines or ICU beds to drive vaccination.

Journalists need to take models for what they are: not definitive statements about what will be (or gotcha claims about what went wrong) but as another politically contestable input into what is being decided right now.