We are thrilled to announce the launch of a new academic journal, Philosophy of Medicine. The journal’s website is live for submissions at http://philmed.pitt.edu.
Philosophy of Medicine is an open-access journal that publishes exceptional original philosophical research and perspectives on all aspects of medicine, including medical research and practices. Through its public-facing section The Examination Room, it also publishes content for the wider public, including health professionals and health scientists.
The mission of Philosophy of Medicine is to serve as the flagship journal for the field by advancing research in philosophy of medicine, by engaging widely with medicine, health sciences and the public, and by providing open-access content for all.
The journal is led by Alex Broadbent as inaugural Editor-in-Chief and Jonathan Fuller as Deputy Editor in Chief (see the full editorial team here: https://philmed.pitt.edu/philmed/about/editorialTeam). It is published by the University of Pittsburgh Library System through Open Journal Systems (OJS) with generous financial support from the Center for Philosophy of Science at the University of Pittsburgh and the Faculty of Humanities at the University of Johannesburg.
If I guess the time, and get it right, do I know the time? No, says common sense, and nearly all theoretical and formal epistemology. If I guess that it will rain tomorrow, am I any better off? Presumably not. Yet we assess predictions almost entirely by whether they are right.
I do think Swedish predictive work was broadly accurate, compared to, for example, the models produced by Imperial College London. But more importantly, I think their stance was rational. They did what was right given the evidence. That isn’t the same as being right in the sense of landing on the truth. But there’s nothing either epistemically or morally significant about the latter. The former, however, is both. Sweden behaved more reasonably than any other country, or perhaps at least as reasonably as the most reasonable, given that there was room for reasonable disagreement.
The stance on Sweden is another version of the intellectual intolerance of the age. And it ignores the evidence. Sweden has done well: not perfectly, but no country has, that I can think of. Whether it comes out tops long-term is up in the air. But there is good reason to think it will – at least as good as the reasons to think it won’t.
South Africa is now ranked 5th in the world for COVID-19 active cases, 9th for cumulative cases, and 23rd for cumulative deaths.
The nation’s leadership was initially widely praised for reacting decisively and early by implementing stringent lockdown regulations. These have been successively eased since they became unsustainable.
The president has recently announced new regulations. Some, like the ban on alcohol sales, are designed to alleviate the burden on the healthcare system. These make sense. But those regulations designed to slow transmission do not. They are variations on familiar themes: curfews, continued restrictions on social and economic activities, regulations on taxi operation, and similar.
Regulation is entirely the wrong approach. Lockdown failed in South Africa, despite its huge cost. The emphasis should never have been on imposing restrictions. It should have been on asking people in different parts of the fantastically complex mosaic of South African society to participate in coming up with solutions.
People know their own way of life, and can identify solutions that work for them. Even if there are none, we all deserve a say in how to balance the risks we face. There is no avoiding the coming storm, but the country can prepare for it by settling on a strategy informed by realism – about what has and hasn’t worked, and about what is feasible in South Africa.
What hasn’t worked
It is obvious that lockdown failed to avert the current situation, since we are in it. It is less widely appreciated that there were no changes in the trajectory of COVID-19 either during the locking down or in the unlocking phases. The infection rate, viewed on a logarithmic scale (because the linear scale makes changes harder to spot), is roughly a straight line from about 28 March onwards. That was Day 2 of lockdown – far too soon for an effect. This means that the reproduction number has remained approximately the same for over three months. (Deaths look similar, with a time lag.) This is obscured on a linear scale, because it is hard to spot changes in a curve. But when viewed with a logarithmic y-axis, it is obvious that the line is approximately straight. Lockdown didn’t make a difference, and nor did unlocking, as Figure 1 shows.
South Africa’s current predicament is a continued, steady growth in incidence rate. This on the back of the huge socioeconomic impact of lockdown:
enormous psychological pressure felt by nearly everyone in the country.
Given these consequences, the last thing South African lawmakers should be considering is a further lockdown.
So is the country out of options?
Ask the people
The road not taken was a considered mitigation strategy, instead of a copycat approach – one that persists as the country unlocks in step with the rest of the world.
The approach, advocated unsuccessfully by some both before and nearer the time of locking down, is to identify context-specific measures that result in reduced infection rates while permitting as much normal activity to proceed as possible.
How does one devise a context-specific mitigation strategy? One doesn’t. Instead, one asks the people who actually live in that context.
Some months ago, I was involved in making a documentary about the effects of lockdown in low-income settings. Interviews were conducted with people living in poverty in both urban and rural settings in Uganda, Malawi, Zambia and India. The common thread in these interviews was their frustration at not being heard.
Most of them feared starvation more than COVID-19. Something else was apparent too. Several people had their own ideas about how to deal with the threat.
In particular, the leadership of a Malawian village came up with a solution to protect older people by locating them in one part of the village. Malawi never locked down but, with a very poor population, half of whom are 17 or under, it is really not clear why it should. Had Malawi’s then-leaders consulted, they might never have suffered the ignominy of having their obviously inappropriate lockdown regulations thrown out by a court.
The road not taken, then, is consultation. It sounds watery, but it’s not. Humans are problem-solvers: that’s our special skill. But we have to know what the problem is, and what tools are available in the context. So long as the people who understand the problem don’t talk to the people who know the context, the chances of solutions are small.
It’s not too late. South Africa’s best bet now is to provide communities with accurate information about how COVID-19 spreads and whom it threatens, exactly as happened in the interaction in Malawi, and then ask them what they want to do about it.
Different steps for different circumstances
Nobody wants to catch coronavirus, and people will take reasonable steps to avoid it. But in this most unequal of countries, those steps will be quite different for different people.
For an office worker living in the suburbs on an uninterrupted salary, working from home and having food delivered and avoiding public places makes sense.
Waste-pickers, hawkers, restaurateurs, taxi-drivers, hairdressers, and domestic workers all live differently. They are all exposed to different risks. They are also faced with different imperatives against which to balance those risks.
By consulting communities, government would also begin the process of rebuilding trust, which was squandered in the attempt to enforce a strategy that was obviously impossible here.
“Suppression” of the virus, as defined in the influential report from Imperial College London, is the reduction of the reproduction number below one, achieved by a 60% reduction in social contact.
South Africa’s initial response to COVID-19 was confident, but wrong. Now it has stalled. But the country is not out of options. The trick is for the chattering classes to stop telling each other what the solution is, and instead ask some of those who haven’t been heard. The leaders have had their chance. It’s only fair that the people have a go.
Soon I’ll have an opinion piece out arguing several of these points. In particular, regulation is just the wrong idea in the first place: people need to be consulted. And that’s not a watery option, it’s the way to get effective solutions that are context-specific.
We wrote this letter a couple of months ago in response to an editorial in the Lancet suggesting that opposing lockdowns was neoliberal. I continue to be surprised by how the world hasn’t noticed that, in fact, extreme measures to combat COVID-19 shift the burden from the wealthy to the poor, who suffer more from the measures than from the disease. It’s a disease that primarily affects the old, and thus primarily the wealthy. This is true even if people who are of the same age fare worse if they are lower down the socioeconomic scale. That is unsurprising, extremely so; what is surprising, and what outweighs that effect massively, is that this disease is so much more dangerous for demographics that are dominated by the wealthy of the world. I still feel that has not been grasped in the global north. So, I’m very pleased to have this letter out. Maybe it will change the perspective just a little towards a more global one.
When President Cyril Ramaphosa announced the decision to implement an initial 21-day national lockdown in response to the threat posed by the COVID-19 pandemic, he referred to “modelling” on which the decision was based. A media report a few days later based on leaked information claimed that the government had been told that “a slow and inadequate response by government to the outbreak could result in anywhere between 87,900 and 351,000 deaths”. These estimates, the report said, were based on, respectively, population infection rates of 10% to 40%.
In late April, the chair of the health minister’s advisory committee sub-committee on public health referred to the early models used by the government as “back-of-the-envelope calculations”, saying they were “flawed and illogical and made wild assumptions”.
These assertions have been impossible to fully assess. This is because no official information on the modelling has ever been released – despite its apparently critical role.
A briefing by the chair of the health minister’s advisory committee in mid-April sketched some basic details of what the government’s health advisors believed about the likely peak and timing of the epidemic. But no details were given on expected infections, hospital admissions or deaths.
A spokesperson for the presidency said that government was withholding such numbers “to avoid panic”.
Finally, towards the end of May the health minister hosted an engagement between journalists and some of the modellers government was relying on. It then started releasing details of the models and projections.
The predictions of these models for an “optimistic scenario” are that the vast majority of the population will be infected, there will be a peak of 8 million infections in mid-August and in total there will be 40,000 deaths.
To understand the significance of these – and the previous numbers – it is useful to consider more broadly what models are and how they are being used in the current context.
What models are and how they are used
A theoretical model – whether in epidemiology, economics or even physics – is a simplified representation of how the modeller thinks the world works.
To produce estimates or forecasts of how things might play out in the real world, such models need to make assumptions about the strength of relationships between different variables. Those assumptions reflect some combination of the modeller’s beliefs, knowledge and available evidence.
To put it differently: modelling is sophisticated guesswork. Where models have been successfully used across different contexts and time periods we can have more confidence in their accuracy and reliability.
But models, especially outside sciences like physics, are almost always wrong to some degree. What matters for decision-making is that they are “right enough”. In the current situation, the difference between predicting 35,000 and 40,000 deaths probably won’t change policy decisions, but 5,000 or 500,000 instead of 40,000 might.
In the case of South Africa’s COVID-19 response, available information indicates that epidemiological models have played two main roles.
First, they have provided predictions of the possible scale of death and illness relative to health system capacity, as well as how this is expected to play out over time.
Second, they have been used to assess the success and effects of the government’s intervention strategies.
There are reasons to believe that there have been significant failures in both cases, in the modelling itself and especially in the way that it has been used.
In recent weeks, the government and its advisors have been keen to emphasise the uncertainty of the modelling predictions. From a methodological point of view, that is the responsible stance. But it’s too little too late.
Modelling COVID-19 is challenging in general, but there are at least four additional reasons to be cautious about our COVID-19 models.
Reasons for caution
First, certain key characteristics of SARS-CoV-2 remain unknown and the subject of debate among medical experts.
Second, unlike some countries, South Africa does not have detailed data on the dynamics of social interactions and the models presented so far do not use household survey data as a proxy. Nuanced questions therefore aren’t addressed. For example, most cases early on in the epidemic appeared to have been relatively wealthy travellers. But there was no way to model the consequences of domestic workers being exposed by their employers and thereby infecting others in their (poorer) communities. So the structure of South Africa’s models is high level and does not account for country-specific factors.
Third, the values for the parameters of the models (representing the strength of relationships between different factors) are being taken from evidence in other countries. They may not actually be the same in South Africa.
Finally, the unsystematic nature of aspects of the government’s approach to testing, such as through its community screening programme, makes it much harder to infer the effects of its interventions.
There is little reason to believe that government had anything other than good intentions. Nevertheless, the consequences of its lack of sophistication in using evidence and expertise may burden an entire generation of South Africans.
A major problem linked to the combination of excessive confidence and secrecy is that the government’s strategy was never clear: although it referred to “flattening the curve” it never stated what its specific objectives were. In the terms of the most influential modelling-based advice during the pandemic, was its strategy “suppression” or “mitigation”?
The government and its advisors have made much of the fact that the lockdown probably delayed the peak of the epidemic. But there is no evidence so far that this was worth the cost – since most of the population is expected to be infected anyway.
One key claim is that the lockdown bought the country time to prepare the health system.
The Imperial model defined the primary objective of “flattening the curve” as reducing ICU admissions below the number of critical care beds. On that dimension, the government’s own modellers predict a peak of 20,000 critical cases in the optimistic scenario and only about 4,000 ICU beds with little increase from the pre-lockdown numbers. By this definition, it has failed dismally.
There appears to have been more success with securing supplies of personal protective equipment, quarantine locations, overflow beds and some ventilators. But there is also little evidence that many of those small gains could not have been achieved without such a costly lockdown.
Given this, it is concerning that many academics and commentators have praised the success of government’s strategy. This has included the Academy of Sciences, which has asserted that “strong, science-based governmental leadership has saved many lives, for which South Africa can be thankful”.
This is entirely unsubstantiated.
First, the full toll of the epidemic will be experienced over time and so it is possible to have fewer deaths at the outset due to a policy intervention being exceeded by a larger number of deaths later because of the limitations of that same policy intervention.
Second, the only way to substantiate such claims would be to use models of different scenarios. But we’ve seen that the early models were not credible and the subsequent ones are subject to a great deal of uncertainty. It seems that the government and some of its advisors want to have the best of both worlds: they want to use dramatically incorrect predictions by early models to claim success of their interventions. This is misleading and does not meet the most basic standards by which academics in quantitative disciplines establish causal effects of policy interventions.
In an earlier article, I noted that “if the current lockdown fails to drastically curb transmission, which is possible, it would layer one disaster on another … the country may exhaust various resources by the time the potentially more dangerous winter period arrives”.
This appears to be the situation in which South Africa finds itself.
Seán Mfundza Muller, Senior Lecturer in Economics, Research Associate at the Public and Environmental Economics Research Centre (PEERC) and Visiting Fellow at the Johannesburg Institute of Advanced Study (JIAS), University of Johannesburg
https://www.ecologi.st/post/covid/ Evidence from phone data that W Cape adherence to lockdown has been quite strict thus lack of adherence is less likely to be the cause of the spike there. Thanks to Monomiat Ebrahim for the share.
Wondering if this means it is more likely to be:
1. A demographic feature such as age
2. A latitude feature – around the equator, COVID-19 has generally been less prevalent
3. A climate feature
4. High concentrations of “starters” leading to a critical mass for an epidemic
Katy Balls talks to Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford. An expert in the fight against infectious diseases, she is the lead scientist behind the Oxford study that disputed Imperial College’s dire coronavirus predictions. She is also a novelist and translator. On the podcast, she talks to Katy about her writing and how it was inspired by her intellectual father; her dispute with the mentor of Imperial College’s Neil Ferguson; and how she has found being in the public eye.