A lot could go wrong here
COVID-19 is a serious threat. There is no doubt about that. We need to understand how serious it is to figure out the best way to deal with it.
At the moment, we are enacting extremely severe measures in an effort to do something. However, we have very little evidence- based data on how to guide our next steps. We really don’t know where we are, where we are heading, whether our measures are effective, or if we need to modify them. There is a possibility that many of our aggressive measures could be doing more harm than good, especially if they are to be maintained in the long term. There will be major consequences in terms of lives lost, major disruptions to the economy, to the society, and to our civilization.
At this juncture we need to act swiftly. At the same time, we need to act equally swiftly to collect unbiased data that will tell us how many people are infected, the chances that someone who is infected will have a serious outcome and die, how the epidemic is evolving in different settings and places around the world, and what difference we are making with the measures that we’re taking. This information can make a huge difference and there is a lot that can go wrong if we don’t have the right data.
This has been an acute situation. At the same time, collecting reliable data should not take time and should not halt our decision- making process. Getting information on representative samples of the population is very easy. It has been done in Iceland, where they have a cohort covering most of the national population looking at samples that have been provided. They see that they have an infection rate of 1.0 per cent, and up until now only two people have died. So, out of the 3,500 infected people in Iceland there have been two deaths, which corresponds to an infection fatality rate lower than the common flu. Of course, some people may be infected later, but nevertheless, these estimates would be very different compared with the original claims of case fatality rates of 3.4 per cent that were circulated.
At the same time, we have other pieces of evidence that the number of people who are infected is much larger compared with the number of cases we have documented. In most places, with few exceptions around the world, we are just testing people who have substantial symptoms who have come to seek health care or even to be hospitalized. These are just the tip of the iceberg.
The Iceland experience and other data from Rome and Italy where entire city populations were tested shows that the vast majority of people are either completely asymptomatic or mildly symptomatic in ways that you would not be able to differentiate from the common cold or common flu. This information makes a huge difference while we are proceeding with aggressive measures of social distancing and lockdowns that may have tremendous repercussions, especially in the long term.
The solution to the problem would be very different if we had proper data. It could be that we need to continue with lockdowns, but it’s very likely that we would quickly need to abandon blind lockdowns and focus instead on protecting the lives of those who are susceptible, such as the elderly and those with severe underlying diseases. At the same time, we would be able allow people who are very low risk or have already been infected to return to normal life and not destroy our planet and our civilization.
So if we have evidence, we can act swiftly, we can modify our strategy, we can optimize it, and we can also be better prepared for the next time, whenever that next time is. We will know how to get it right and we will have responded in an appropriate way.
THE SOLUTION WOULD BE DIFFERENT IF WE HAD PROPER DATA.
National Post John P. A. Ioannidis, MD, DSC is Professor of Medicine, Professor of Epidemiology and Population Health, and
Professor ( by courtesy) of Biomedical Data Science at the School of Medicine, Professor ( by courtesy) of Statistics at the School of Humanities and Sciences, and co- Director of the Meta- Research Innovation Center at Stanford ( METRICS)
at Stanford University.