This archive report was first published on 9 May 2020.
As the world grapples with the Covid-19 pandemic, epidemiologists and data scientists have been working tirelessly to produce and publish grim estimates of how many people will be infected with the coronavirus or die of it.
These estimates are presented in graphs showing 'best-' and 'worst-case' scenarios, with the aim of 'flattening the curve' – keeping Covid-19 cases below the level that the health system can handle.
However, the scale and scope of these numbers, and the frequency at which they change, can make some people distrust them or wish them away.
But what's behind these projections? In crunching the numbers, estimators make assumptions that certain conditions will hold, which are assigned numerical values and fed into a computer software that runs mathematical formulas and spits out results.
This process is known as modelling, and it's merely a way to approximate reality. It's a way of producing an early warning system, without which we would be flying blind.
But the projections change as mitigation measures shift, and the more data available – especially if it's of high quality – the better the forecasts.
Testing improves the accuracy of viral disease forecasting, and a higher number of people tested means better estimates that scientists can generate.
Not everyone agrees with models, predictions, and estimates, but we should not fight the coronavirus with one hand tied to our back nor fly blind into the future.
By building scenarios premised on locally generated data, we can equip decision-makers with much-needed insights for tackling the tidal wave of the coronavirus.
