Great economist John Maynard Keynes, accused of inconsistent policies, is credited with saying, âWhen the facts change, I change my mind. What are you doing?”
This point comes to mind in connection with the heated debate at Australia’s ânational levelâ to relax restrictions on the 70% and 80% immunization rate thresholds.
This plan was approved by the National Cabinet on August 6 – although apparently with a different interpretation for each government involved.
Read more: National Cabinet leaves us in the dark about reopening the nation, so we need to join the dots
The modeling by the Doherty Institute, carried out in July of this year, is the basis of that. There have been many arguments about particular assumptions in Doherty’s model. But the most basic problem is that it is indeed obsolete.
Delta changed the landscape
Ideally, the models of any phenomenon are based on directly relevant data. In recent years, the development of techniques for “big data” (large data sets, often derived from administrative archives) has produced a wide range of new perspectives.
In the case of COVID-19, the data needed for such an approach includes evidence of how changing conditions alter the âReffâ – the virus’s effective reproduction rate.
The Reff is a number that indicates the average number of new cases generated by each existing case. It is influenced by vaccination rates, movement restrictions, and the ability of screening and tracing measures to locate and isolate those infected. To avoid an uncontrolled pandemic, we need to keep the Reff below 1 most of the time.
Read more: We’ve heard about R numbers and moving averages. But what are the k numbers? And how do they explain the super-spread of COVID?
When the Doherty Institute undertook its modeling, there was virtually no data for Australia relevant to the scenario policymakers are currently considering.
There had been no sustained outbreaks of the Delta variant, and only one extended lockdown to prove the effectiveness of various measures. So the Doherty Institute had to use a theoretical model with parameters derived from a combination of foreign evidence (from countries with very different experiences than Australia) and the best guesses its experts could make.
We now have a lot of data on the Delta variant and the effectiveness – or ineffectiveness – of various restrictions, and the extent to which people change their behavior during an outbreak.
Read more: Grattan Friday: Transition to life with ‘endemic’ COVID could be difficult
In particular, we have learned that testing and contact tracing becomes significantly less effective once the number of cases reaches hundreds a day, as has happened in New South Wales and Victoria. The Doherty Institute reportedly revised its modeling to take this into account and presented revised advice to the National Cabinet, but neither the model results nor the modeling changes were made public.
By the time we reach the immunization levels designated in the national plan, probably in October or later, we will have a lot more data. Importantly, we will know if the effective reproduction rate is greater than 1 (indicating exponential growth) or lower (indicating a contraction in the number of cases).
Rather than sticking to a predetermined timeframe, we must be prepared to adjust our policy responses in light of the latest data and the most recent models available to us.
It’s like climate modeling
This situation is somewhat analogous to modeling climate change.
The basic science behind climate change has been known for over 150 years. As early as 1896, the Swedish chemist Arrhenius estimated that doubling the global concentration of carbon dioxide would increase the average temperature of the earth by 5 â.
However, when global warming became a concern in the 1980s, we had little more to do than simple simulation models, with no certainty as to whether they were realistic representations.
This changed during the 1990s. Climatologists refined general circulation models of the atmosphere and the ocean, running on supercomputers and capable of incorporating large amounts of data, and began to model the effects. on soil and vegetation. In addition, we increasingly suffer the predicted consequences of climate change, learning the hard way that models were, to say the least, conservative in their predictions.
Consideration of new evidence
With COVID-19, we don’t have time to develop the massive models that would make optimal use of the data that becomes available on a daily basis. But we can use this data to improve our understanding of how the virus spreads and how our behavior responds.
To some extent, this is already happening with the work being done by the Burnet Institute, which provides weekly updates to the NSW government. But this work is not being made public and it appears that policies announced by the NSW government may be contrary to modeling-based health advice.
The course of the pandemic is an interplay between the mutant characteristics of the virus and the way humans respond, interactively and collectively. For this reason, it is a mistake to let disciplinary boundaries decide which advice should be heeded and which should be ignored. Epidemiologists, public health researchers, economists and other social scientists all have relevant expertise. In this emergency, it should be “all hands behind the wheel”.
Read more: Delta tempts us to trade lives for freedoms – a choice it seemed like we wouldn’t have to make
In these rapidly changing times, it makes no sense to set a political plan based on a model that is several months old.
We need to respond flexibly to new evidence as it comes to us. We need to consider all kinds of data, including new evidence on the transmissibility of the virus, estimates of likely vaccine use, and observations on how restrictions reduce travel in our cities.
What we don’t need is more speculation about the hypothetical dates and vaccination rates at which various restrictions will be lifted (or perhaps, looking at the overseas experience, reimposed). Let us focus on the facts as they are now.