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.
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.
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
Loads of great points
- COVID on the way out in UK
- Antibody tests unreliable, don’t show prevalence
- Infections rates shouldn’t even be reported; dependent on testing
- R number depends on immunity and thus a red herring
- Everywhere same curve: up, then gently down (resonant of Michael Levitt’s interview in the same place). Immunity explains better than a load of different explanations for all the countries with different measures.
- And lockdown is retrogressive, not progressive; it’s dumb to bundle up the anti-lockdown people as on the right (or left I guess). It’s a luxury that the rich can afford and the poor can’t.
I haven’t yet read Angus Deaton and Anne Case’s book Deaths of Despair but it’s on my list. What strikes me about the discussion in the Boston Review is its resonance with the refrain from the alt right, much maligned by the alt left, that things are tough and getting tougher if you’re an uneducated white guy. Looks like we might now have some evidence to feed into that hitherto entirely ideological debate.
This Thursday at 11:30am (via Zoom) the @CHESS_DurhamUni reading group will be discussing ‘A Framework for Decisions in a Post-COVID World‘ by @AlexBroadbent . . . please contact email@example.com for the paper and joining instructions #COVID19 #socialpolicy #policymakers