The Guardian published an article today under the headline, Britain had a head start on Covid-19, but our leaders squandered it, written by Professor Devi Sridhar, chair of global public health at the University of Edinburgh. It's worth quoting from Professor Sridhar's article [emphasis added]:
"On 17 March, Imperial College released a study noting that it had revised the model the government had been using, and stating that suppressing the virus was in fact the best way to avoid a vast number of people dying. The earlier model did not include the ICU data shared in the Lancet on 24 January. Instead, it was similar, but much later information from Italy, that changed their recommendation."
In other words, the MRC Centre's model was calibrated to less representative data than was available at the time.
What are the implications for policy and outcomes? Professor Sridhar offers some thoughts on that as well [again, emphasis added]:
"We had a choice early on in the UK’s trajectory to go down the South Korean path of mass testing, isolating carriers of the virus (50% of whom are asymptomatic), tracing all contacts to ensure they isolate as well, and at the same time taking soft measures to delay the spread. Instead, we watched and waited, and whether it was academic navel-gazing, political infighting, a sense of British exceptionalism, or a deliberate choice to minimise economic disruption over saving lives, we have ended up in a position where we are now closer to the Italy scenario than anticipated, and are faced with taking more and more drastic measures."
Modeling can be difficult, particularly when parameter values need to be estimated with limited data. Over time, I imagine a number of interesting articles will be written about the modeling efforts at Imperial and their effect on policy. Until then, it's useful to appreciate the wide range of forecasting errors we face when trying to model the transmission of a new virus.
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