I’ve got an opinion out in the Sunday Independent 31 May: ‘We were set up to lock down’ People who say “It was right to lock down as a precaution but things have changed and now we should unlock” are wrong and should admit it or we won’t do better next time #epitwitter

This was published in 31 May in the Sunday Independent (South Africa) but for some reason they have not made this available online. So:

  1. Here is an image of what was published (presumably fine to share because it was in print only) We were set up to lock down (The Sunday Independent)
  2. Below is the text I submitted. They did not run the final text past me and there are some irritating editorial bungles that make the published text less readable (and sometimes ungrammatical). So, the one below is probably a better read.

We were set up to lock down

There’s a standard line. South Africa’s decision to lockdown when we did was sensible. Little was known about COVID-19 and its potential impact here. Since then, the situation has changed. We know more about how the pandemic is likely to unfold and who the disease affects, and we have made preparations to deal with the likely impact. The economy continues to deteriorate each day we stay locked down, and with it, people’s livelihoods. It is now time to unlock; in fact, unlocking is overdue. Decisive steps should now be taken to restore the economy, education, health services, and other pillars of the nation to their “new normal” function.

This familiar story is wrong. The evidence available at the time we locked down supported doing something more moderate. Lockdown was not the right response for South Africa to the threat COVID-19 posed in South Africa. Its potential benefits for a population the majority of whom is under 27, and can expect to be dead by their mid-sixties, did not outweigh the certain costs to the one in four living in poverty, and the many more who would join them on losing their livelihoods. Besides, it was obvious that, for most of the population, lockdown was impossible, due to overcrowding, shared sanitation, and the necessity of travel to receive social grants.

Contrary to what’s said, the evidence hasn’t changed. The relevant characteristics of COVID-19 were apparent by the end of March, when the decision to lock down was taken. Much of it is cited in an opinion piece published on the same day lockdown was announced, 23 March, a piece arguing that a one-size-fits-all approach could not be applied to achieving social distancing. The piece was written by a colleague and myself, unaware that that same day the country would move in exactly the opposite direction to the one we advised. We wrote several further pieces, and by 8 April I was sure that lockdown was wrong for Africa, including but not limited to South Africa, and published an opinion to that effect. The next day lockdown, was extended.

What has changed? Is it the evidence, or is it intellectual fashion?

It’s possible that those of us making anti-lockdown arguments two months ago are like stopped clocks that inevitably tell the right time when it comes. But the salient evidence was there all along. The dominance of age as a predictive (who knows whether causal, or how) risk factor for serious, critical and fatal COVID-19. One credible infection fatality estimate published in March based on data from China was 0.66%, with a marked age gradient. A credible systematic review concluding that school closures were not supported by evidence was published in early April. Perhaps the major uncertainty concerned HIV as a potential vulnerability of the South African population. But it was known early that treated HIV status was not correlated with COVID-19 risk, and in early April early results emerged that this might be true even for untreated HIV. Those same results are being relied on in current opinions, in some cases by people who dismissed them at the time.

If that’s correct, and many will deny it, then how could so many academics, politicians, analysts and commentators have got it wrong? And what stops them seeing it now?

Obviously there are social costs to admitting error, and perhaps psychological ones too. Certainly we’re better at spotting each other’s mistakes than our own. But I think there was something else in play, which continues to confuse us. We felt we were presented with two options, and chose one of them as a precaution. This was not the reality, but a product of the modelling approaches that informed policy and perception alike at the time, and that still play worryingly prominent roles in the policy approach.

These models had and have three misleading features.

First, they did not and do not estimate the health burden of COVID-19. This is because they model the effects of reduction in social contact without properly modelling the effects of the actual measures taken to achieve that reduction. A free decision to stay home is represented in the same way as being chained to the bed, or indeed being shot dead on the spot. These have different consequences for mortality, none of which show up in the models. Perhaps this doesn’t matter in the developed world, where economic downturn means poverty but not starvation. But it’s crucial in the developing world, where recession often means death.

Second, and relatedly, contextual differences were obliterated by the use of using a simple percentage scale to measure the reduction in social distancing. This meant that, for instance, a 60% reduction in social distancing was represented as the same thing in Geneva and Johannesburg. Whereas, of course, that is an outcome one takes by implementing policy decisions, which would usually be informed by the local context.

Third, the different scenarios modelled were then given different names, re-introducing a qualitative difference between them that was simply absent in the input. Qualitative differences were thus obliterated in the inputs – perfectly reasonably, from a modelling perspective – then introduced in the output. Where before we had (say) a 40% reduction in distancing, we have “mitigation”. And instead of (say) a 60% reduction, we have “suppression”. These began life as arbitrary points on a continuous scale, as the modellers would have been the first to admit. But with different names, they became treated as qualitatively different strategies. Moreover, the leading models at the time predicted hugely greater benefits from suppression compared to mitigation.

Thus, almost magically, the huge range of possible measures, varying between context depending on context and policy priorities, became transformed into a choice between lockdown and no-lockdown. Lockdown was exemplified already in China and Europe as a set of specific restrictions, and not as an abstract percentage reduction in social contact.

All context, all nuance, all qualitative factors were lost, washed out in a modelling exercise that was insensitive to contextual differences when formulating its inputs, and unwise in giving qualitatively different labels to its outputs.

Against this background, precautionary thinking naturally overtakes cost-benefit thinking. Proportionality gave way to precaution. The anti-COVID measure has a clear form: restricting on economic activities and confining people to their homes. It is so much more effective than any other measure that it presents us with a binary choice; other measures are pathetically ineffective by comparison, because in the process of de-quantifying the effectiveness of suppression over mitigation, regional differences have been lost. The choice is between action and inaction, and the cost of doing nothing appears huge: just look at the footage from Italy. Yes, it will be painful, but it’s better than the alternative.

But the precautionary approach was never necessary. There was always a range of possible actions, the costs of lockdown were always obvious, and the most significant determinants of the risk profile of the South African population were known.

Now, European countries have passed their peak, and we are again ignoring our own context. Our curve remains exactly the same as it was the day we went into lockdown (a straight line on a logarithmic scale, which is the relevant scale here – for both cases and deaths). Lockdown made no difference, if those graphs are to be believed; and it’s hard to know what other data to look at. The decision to unlock is, as Glenda Gray pointed out, not backed by any scientific case. Yet it’s the right one, not because the evidence changed, but because it was right all along. Lockdown was always wrong for Africa, including South Africa.

Benjamin Smart in the Independent: ‘Parents shouldn’t fear COVID-19’ https://www.iol.co.za/sundayindependent/dispatch/parents-shouldnt-fear-covid-19-48455400 @bthsmart


This is from last week but I don’t recall sharing it. A concise account of why people should not worry about school reopening. It is written for SA but applies also to the U.K. where timing is similar, as are the fears, including among people who consider themselves educated.

Interview with UJ FM: https://www.facebook.com/UJFMRadio/videos/197495267961770 @ujfm in preparation for the webinar at 1pm today: Data and Delusion after COVID-19 https://universityofjohannesburg.us/4ir/covid-19-webinar-3/

Very enjoyable interview with Bolela Polisa at UJ FM, discussing some of the issues we might encounter in this afternoon’s webinar on Data and Delusion after COVID-19, as well as why Glenda Gray is right and what it’s like to wear a mask if you have a beard.

IFK Panel 27 May: Data and Delusion after Covid 19 – Shakir Mohammed (Google Deepmind), Chris Harley (UJ Engineering), Olaf Dammann (Tufts Public Health and Community Medicine) https://universityofjohannesburg.us/4ir/covid-19-webinar-3/ #epitwitter @mediauj

Please join us for a panel discussion on Data and delusion after Covid 19, Wednesday 27 May @ 1pm South Africa, W Europe |  12 noon UK | 7am US East Coast | 7pm Beijing China. Please “arrive” (log in) 15 minutes beforehand to ensure time for you to be admitted prior to the event as we admit participants individually for security reasons. We start sharp on the hour. To join you first need to register.


  • Dr. Shakir Mohammed is a Senior Researcher at Google DeepMind in London, United Kingdom (UK).
  • Professor Charis Harley is an academic based in the Faculty of Engineering and the Built Environment at the University of Johannesburg (UJ), South Africa.
  • Professor Olaf Dammann is Vice-Chair of Public Health at Tufts University in Boston, United States (US), Professor of Perinatal Neuroepidemiology at Hannover Medical School, Germany, and Adjunct Professor in the Department of Neuromedicine and Movement Science at the University of Science and Technology in Trondheim, Norway.

Facilitated by Professor Alex Broadbent, Director of the Institute for the Future of Knowledge at the University of Johannesburg

Please register if you wish to watch this live. A recording will also be posted afterwards.

This is the third in a series of webinars on Reimagining the World After COVID-19, organised by the Institute for the Future of Knowledge in collaboration with the UJ Library and Information Centre on the initiative of the Vice Chancellor’s Office at the University of Johannesburg.

Data and delusion after COVID-19

An epidemic has a single centre from which disease spreads: an epicenter. A pandemic is what happens when the disease no longer spreads from a single centre but circulates and spreads throughout the population. The COVID-19 pandemic has been accompanied by a pandemic of data. Data is offered, analysed, re-packaged and criticized by mighty international organisations and by tiny local outfits. Even private individuals with no prior expertise or interest in data, disease, or statistics spend hours poring over graphs and critiquing case fatality estimates.

Yet this proliferation of data and analysis has not yielded effective predictions. Instead, it has demonstrated how ill-equipped we are to deal with this new, non-hierarchical, distributed information context. Leading scientists have proved dramatically wrong. Or perhaps not – it depends who you ask. The unfolding pattern of spread still surprises us at every turn – except those who predicted it all along. Nothing is more common than the common cold, and coronavirus variants are one of its causes: yet we seem unable make reliable predictions about COVID-19.

This webinar explores a range of issues relating to data and trust in science in the aftermath of COVID-19. What went wrong with the modelling approach to prediction – if, indeed, anything did go wrong? How should policy and scientific research interact, and how should policy makers make use of data? Can people without domain-specific knowledge use data to predict better than the experts in that domain? If not, then can data analysts themselves make predictions merely by studying patterns in data? Turning to the generation of data, how does the individual interest in privacy weight against the public interest in private information, notably location, which can be very useful in the context of a pandemic?

Our improved data processing abilities did not help us as much as we might have imagined in this situation. Machine learning, in particular, thrives on spotting complex patterns in noisy datasets, and doing it fast; yet is has been conspicuously absent from the efforts to predict the course of this pandemic.

Register here



Just out, Better the drug you know: commentary on Daughton 2020, Natural Experiment Concept to Accelerate the Re-purposing of Existing Therapeutics for Covid-19 https://www.sciencedirect.com/science/article/pii/S2590113320300110

Broadbent A. 2020. Better the drug you know: commentary on Daughton 2020, Natural Experiment Concept to Accelerate the Re-purposing of Existing Therapeutics for Covid-19. Global Epidemiology 2(10027):1-2. https://doi.org/10.1016/j.gloepi.2020.100027

This is a (positive) commentary on what I thought was a really useful idea for accelerating research into anti-COVID drugs, which I shared previously and which you can (and should) read here:

Daughton CG. 2020. Natural experiment concept to accelerate the re-purposing of existingtherapeutics for Covid-19. Global Epidemiology 2(100026):1–6.66. https://doi.org/10.1016/j.gloepi.2020.100026

And the author Christian Daughton posted a reply to my commentary here:

Daughton CG. 2020. Response to: Broadbent 2020, Better the drug you know: Commentary on “Daughton 2020, Natural experiment concept to accelerate the re-purposing of existing therapeutics for Covid-19”. Global Epidemiology 2(100028):1-2. https://doi.org/10.1016/j.gloepi.2020.100028