21st April 2020
Devi Sridhar and
Maimuna S Majumder
Edinburgh University and
Harvard Medical School
Over-reliance on modelling leads to missteps and blind spots in our response
The coronavirus pandemic has revealed much about public policy, including the extent to which politicians and their advisers rely on modelling to help predict the future of virus spread and decide what actions are best to take.1 This is true of many countries such as the US, UK, France, and Germany as well as Hong Kong, Singapore, and China. Although better than relying on intuition or flying completely blind into a crisis, over-reliance on modelling might have led to several missteps.2 For example, some early covid-19 models did not consider the possible effects of mass “test, trace, and isolate” strategies or potential staff shortages on transmission dynamics. Including these may have led to earlier focus on testing capacity and appropriate protective equipment for frontline workers.
This is not any fault of the modellers themselves; scientists often are cautious about the uncertainty around their predictions, the shaky nature of the data they are inputting, and the assumptions underpinning their analyses. However, when governments want quick answers and a crystal ball, they take modelled projections as certainty and lose sight of other crucial information sources. The models themselves are constructed using advanced statistics and mathematics. They are a technical tool to present different scenarios, but deciding which model to follow and what factors to include is a political choice.
Unfortunately, data to aid this decision making process can be sparse in the event of a truly novel pandemic. Because covid-19 has been around for only a few months, we don’t have data from previous outbreaks to inform our response today. Instead, we must make do with data collected in real time—data mired in insufficiencies because of testing, documentation, and reporting practices that vary over time and between countries.
With the true number of infections and deaths as yet unknown and no benchmark from the past, we must rely on models that can accommodate these data insufficiencies while generating important epidemiological insights. These include estimates of the death rate of covid-19 and how fast it may spread, as well as how different these estimates may be across different populations.