Coronavirus economics and policy: from the mailbox

But first an update of the chart showing numbers of new announced coronavirus cases for New Zealand and Australia (the latter divided by five to produce a comparable per capita number).

new cases 15 apr

A few days on from the last time I ran the chart and New Zealand is still finding about twice as many new cases per capita as Australia.  At the margin, we seem to do be doing a few more tests per capita each day than Australia is –  in both countries the number of tests being done has fallen back from the peak.  But despite our more restrictive regime, we don’t seem to be producing better (lower new cases) results than Australia.    Who knows why, although those left-wing academics from Otago, professors Baker and Wilson, while lavishing praise on the Prime Minister in the Guardian

One critical success factor that is, unfortunately, harder to guarantee is high-quality political leadership. The brilliant, decisive and humane leadership of Ardern…

went on to note to Stuff yesterday that in their view the case for a tougher lockdown in New Zealand than in Australia rested on the fact that

“New Zealand has had to go to this strong, intense, lockdown, because it started from a pretty low base.

“It didn’t have really sophisticated systems for mass contact tracing…Things like basic contract tracing were working better in Australia from earlier on.”

Wasn’t Ardern Prime Minister for more than two years prior to this, including the two months apparently wasted prior to the lockdown? Wasn’t David Clark Health Minister?  In fact, wasn’t Ashley Bloomfield Director-General of Health for most of that time?

But my heading suggested this post was about things that had turned up in my mailbox.

First was a useful short note from the trans-Tasman economics consultancy Castalia on the respective experiences and approaches of New Zealand and Australia.  I found the summary table of the regulatory restrictions particularly helpful.

Second, and following on from my post on Monday about the New Zealand Initiative’s short attempt to think about some sort of cost-benefit approach to assessing interventions, I had an email from one of New Zealand’s leading academic economists, Professor John Gibson from Waikato.  I’ve never met Professor Gibson –  although it turns out we both spent time in Papua New Guinea early in our careers –  but he kindly sent me a note he’d done looking at similar issues through a slightly different lens.  Gibson’s paper is not available online, but he has given me permission to quote from it.

His note opens this way

The approach taken in New Zealand to dealing with Covid-19 may result in unnecessary loss of life. The outbreak of a hitherto unknown disease poses an important new risk to humans, so it is appropriate to devote resources to mitigating this risk. These risk mitigating resources mean some output is foregone. In other words, our lives are going to be somewhat more risky and people will be somewhat poorer, than would have been the case without this new disease.  A key question for public policy is where to strike the balance between reduced risk and foregone economic output.

Gibson doesn’t pull his punches in places

There are at least two grounds for concern about the New Zealand strategy. First, the people making decisions are the same ones who botched the preparation for the arrival of Covid-19 and so there is little reason to have faith in the wisdom of their choices. I say “botched” advisedly and would ask readers to consider the following four facts:

  • Taiwan recorded their first case of Covid-19 on 21 January, a full month before New Zealand’s first case
  • Taiwan usually has about three million visitors a year from China, while New Zealand gets about 400,000. The gap is even bigger in terms of visitors to China (who posed a risk of spreading the disease upon their return)
  • Taiwan has not had a lock-down
  • Yet despite earlier exposure and much greater risk due to more travel to and from China, Taiwan has just 373 cases (16 per million) of Covid-19 while the rate in New Zealand is currently 17 times higher (266 cases per million)

Similar comparisons could be made with respect to Hong Kong or South Korea, who also provided lessons on management of this new risk. The complacency by politicians and bureaucrats in New Zealand, who had the advantage of an extra month for preparation and much greater distance from China, is staggering. Obviously that chance to respond to the risk in a low-cost manner was missed and so a very costly lockdown has resulted. While we can hope for better decision-making going forward, there is little reason to be confident of this.

He goes on to lament the lack of an informed open public discussion

In that regard, it is mainly the BIG SCARY NUMBER approach to political decision-making that has been evident, rather than a careful discussion about trade-offs. When announcing the lockdown, the Prime Minister claimed that if strong action was not taken against the spread of the SARS‑CoV-2 virus, New Zealand’s health system would be overwhelmed and tens of thousands would die:

“If community transmission takes off in New Zealand the number of cases will double every five days. If that happens unchecked, our health system will be inundated, and tens of thousands of New Zealanders will die”[1]

There is very little critical scrutiny of this claim, which seems to be based on modelling done by public health academics at the University of Otago. No empirical data from New Zealand on key parameters informed this modelling, which used an off-the-shelf European model (http://covidsim.eu). It is unknown whether the spread of the virus in a low-density, younger population like New Zealand is the same as in high density, public transport-reliant Northern Hemisphere populations, that are older, and have worse respiratory health to start with.[2] At best, these models are informed projections. It is also the case that across the six scenarios modelled by the Otago academics, the average number of forecast deaths was just over 8000 (mean 8,300, median 8,600).[3] So Prime Ministerial claims that “tens of thousands will die” are vague at best and alarmist at worst.

Moreover, the projections have not been presented in a way that empowers people to make considered judgements. We are shown big numbers of projected deaths, with no context to interpret them. Deaths are bad, more deaths are worse, so it feeds into the narrative that there is no alternative to the approach taken. This approach to public policy tends to disempower people who are not privy to the epidemiological models.

Of course this is unwarranted, as ordinary people make decisions about trade-offs all the time and typically use careful judgement when doing so.

[1] https://www.stuff.co.nz/national/health/coronavirus/120501534/coronavirus-jacinda-ardern-just-made-the-most-consequential-decision-of-her-career-putting-nz-on-house-arrest

[2] There is also a more subtle criticism of the “If that happens unchecked…” phrasing by the Prime Minister. This implies no change in behaviour but we know from the Lucas Critique that when circumstances change, people change their behaviour, so forecasts that are not based on “deep” or “structural” parameters provide poor predictions. The virus reproduction rate is unlikely to be a deep parameter, which is why careful treatments distinguish between R0 – the reproduction number in the absence of behavioural change or immunity, and R‑effective (see https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/). So a forecast that assumes no behavioural change by people seeing an epidemic unfolding is not a plausible counterfactual.

[3] https://www.health.govt.nz/system/files/documents/publications/report_for_moh_-_covid-19_pandemic_nz_final.pdf

It is perhaps worth noting here that Professors Baker and Wilson are among the academics with their names on that modelling.

And here I would interject to note that whatever modelling the government may have solicited or received unsolicited, so far we have none of the official advice (from the Ministry of Health, from Treasury) on what officials made of the modelling, how they assessed risks, costs etc.   As I’ve noted before it is our country, our lives, not those of a few Cabinet minister and officials: transparency of key documents should have been an integral part of any sort of serious confidence-building approach.

Gibson’s own contribution is to attempt to recast things in terms of the potential impact on life expectancy

One contribution I want to make with this essay is to show that it is entirely possible to recast these projections of forecast Covid-19 deaths in terms of reductions in life expectancy, and there are four advantages of doing so:

  • Life expectancy is intuitive to most people, as a measure of the lifespan that can be expected by the average person;
  • The calculated impact on life expectancy can be used for risks that change in the future, such as if controls on spread of SARS-CoV-2 fail;
  • Life expectancy summarizes the impact on everyone in New Zealand, and is naturally weighted in the sense that the people who will suffer the consequences the longest (the young) contribute more to the average value; and,
  • Life expectancy is affected by health shocks and by income shocks and so it naturally allows analysis of trade-offs without needing so-called contests between health and the economy which may initially seem incommensurable.

In terms of this last point, a key fact being ignored in New Zealand discussions is that poorer people and poorer societies have lower life expectancy. The actions being taken to deal with the Covid-19 risk are making New Zealand poorer, and so will reduce life expectancy.

Specifically

It turns out that life expectancy in New Zealand is more sensitive to changes in real income than is so for many countries. Using World Bank data on real GDP per capita (in purchasing power terms) and life expectancy, from 1990 to 2017, the income-elasticity of life expectancy for New Zealand is estimated as 0.171±0.009. In other words, a ten percent decrease in real per capita GDP reduces life expectancy by 1.7 percent. The most recent period life tables for New Zealand report that male life expectancy was 79.5 years and female life expectancy was 83.2 years, so 1.7 percent of the average of those two values is 1.4 years. In other words, if real per capita GDP in New Zealand falls by ten percent due to the lockdown and other effects associated with Covid-19, life expectancy would be predicted to fall by 1.4 years.

and

No one knows how much lower New Zealand’s real GDP per capita will be as a result of the lockdown and other steps taken to deal with the risk of Covid-19. So to help people think about the trade-offs, I present a range of values: if real GDP per capita falls by five percent, that would translate into a fall in life expectancy of 0.7 years; ten percent would be 1.4 years lower life expectancy, and with a 15% fall in real income then life expectancy would be reduced by 2.1 years. Some readers might object that these effects are on some unidentified people sometime in the future, but exactly the same point can be made about the forecast deaths from the epidemiological models. Moreover, the current calculation has the benefit of being based on actual New Zealand data, rather than just on assumed values.

He works through various calculations concluding

With these estimated impacts on life expectancy it is now possible to compare both the health shock and the income shock using the same measuring rod of life expectancy. For example, if the lockdown leads to a ten percent fall in real GDP per capita, we can expect life expectancy to be 1.4 years less than otherwise. This could be justified as a rational investment in risk reduction if it prevented a Covid-19 death toll that would be ten times the usual flu shock. In contrast, if the death toll would otherwise have been four flu-shocks or less (so 3500 or fewer deaths), shrinking the economy by ten percent would reduce life expectancy by 1.4 years, in order to avert a risk that otherwise would have produced a 0.6 year decline in life expectancy. In other words, this mix of risk and response would take almost one year off the expected life span of everyone in New Zealand. The apparent kindness of doing everything possible to limit deaths due to Covid-19 would, instead, be killing more people by making them poorer.

There are no easy choices here. Nevertheless, it is possible to present the trade-offs in ways that can be interpreted by ordinary people, who rightly should have far more input into these decisions than they have to date.

I found it an interesting approach –  even if I was a bit less convinced than Gibson that the framework was likely to be widely accessible –  but had a couple of questions about his approach.  I went back and asked him about this one

It is an interesting way of looking at the choices/tradeoffs issues. I suppose my only question is how we should think of a single year very steep drop in GDP per capita.  Given that GDP per capita is usually a very persistent series (a bad recession might be perhaps only a 3% fall), do you think that (say) a 15% fall in GDP per capita fully regained perhaps three years hence, would have a material life expectancy effects (by contrast, if the whole future path of GDP per capita was pulled down by 15% the likely effect seems pretty clear?)

To which the relevant bit of his response

What I do think would occur if we lost three years of progress in raising GDP is that the strong progress that has been made in extending life over the past couple of decades would weaken, so we would lose much of the rise in life expectancy that would have been expected to occur over that time with rising incomes. I have two reasons for thinking this: a. even though we haven’t lost the old blueprints for longevity improvements turning those blueprints into reality often takes a lot of resources that will be in short supply – we already see this with expensive treatments that can be afforded in Australia but not NZ, reflecting our falling income relative to over there, and b. even with a three year deviation from trend rather than a permanent lower track in real GDP per capita, that is three years where there will be far fewer resources for developing new blueprints for improving life expectancy – those blueprints might be both the technological ones (where we can free-ride a bit on international leaders) but also the social ones like improved childhood nutrition, interventions on family violence, substance abuse and so on.

That seems plausible.

I suppose my other observation, which I have made about all manner of contributions to these discussions over recent weeks, is that in thinking about what policy approach the New Zealand government should take we have to be careful to look only at the truly marginal effects of that particular set of policies.  For example, even if GDP per capita this month is 40-50 per cent lower than normal (roughly where The Treasury and my own guesses would put it) only part of that is attributable to the lockdown regime, only part of it is even attributable to any New Zealand policy choices.  Gibson’s note does not attempt to directly address that point. Perhaps his response might be to note that, whatever the source of the sharp losses in GDP per capita, we should expect them to result in worse long-term health outcomes (life expectancy) whatever effect they may have in saving lives from Covid itself.

That, in itself, makes it a worthwhile contribution and I was glad to receive it.

The final item this morning is still work in progress, although I gather the author hopes to finish and release it in the next day or two. My former Reserve Bank colleague Ian Harrison, now operating as Tailrisk Economics, has developed something of a knack for picking apart papers used by officials and politicians to support favoured regulatory interventions.  Ian is much more willing to read every single cited source than, say, I am, and his deep dives have often revealed significant weaknesses and issues in papers officials and ministers claim to rely on.    His collection of past efforts is here.

Over the last week or two Ian has turned his attention to the Otago modelling (also referred to above by Professor Gibson) released by the Ministry of Health and waved around  –  for support if not necessarily illumination –  by the Prime Minister.  He focuses particularly on this paper, dated 23 March (which Baker and Wilson are among the co-authors).

Ian has given me permission to quote from his draft paper.  In that paper he raises some significant concerns about the Otago modelling, but also reports some modelling work he has been doing (Ian is pretty experienced with models, having been primarily responsible for the Reserve Bank’s capital modelling in years gone by and done related work since leaving the Bank).  For now, I will focus on the critiques and concerns about the Otago work, given the role we are led to believe it played in government decisionmaking (or at least in post-decision spin).

Ian begins his paper this way

The only publically available information on the Ministry of Health’s (the Ministry) modelling of the impact of Coronavirus on New Zealand is a set of reports on the Ministry coronavirus website, all produced by the University of Otago Covid-19 Research Group (OCRG). These papers address a range of issues and the later ones used a model, that is in the public domain and is available at covidsim.eu. Here we focus on one paper -‘Potential Health Impacts from the COVID-19 Pandemic for New Zealand if Eradication Fails: Report to the NZ Ministry of Health’ that was dated 23 March. It has received a substantial amount of media attention.

The key headline result in the report is that if this lockdown shock therapy fails. the consequences are serious. Between 8560 and 14000 could die over the next year. The conclusion was:

If New Zealand fails with its current eradication strategy toward COVID-19, then health outcomes for New Zealand could be very severe. If interventions were intense enough however, in some scenarios the epidemic peak could still be suppressed or pushed out to the following year (at which time a vaccine may be available.

and

Like any modeling the OCRG results depend on the most critical assumptions used by the modelers. To a degree modeling results can be what the modellers want them to be, so it is always critical that modellers clearly report their main assumptions and their impact on the results, upfront. They should not be hidden in the technical detail.

We found that OSRG’s model runs grossly overstated the number of deaths because they made an assumption about the critical tool in the Ministry’s arsenal. It was assumed that there would be no tracing and isolation of cases. This led to an explosion in the number of cases and deaths. The reporting of the range of deaths was also inflated by the simple expedient of excluding the model runs that produced low numbers. One of their six scenarios showed just seven deaths over a year.

But that is not what the public saw. The Stuff report on the modeling read as follows:

Up to 14,000 New Zealanders could die if coronavirus spread is uncontrolled, according to new modelling by the University of Otago, Wellington.

Covidsum is, apparently, relatively easy to use.

When we ran the Covidsim model we found credible paths that could reduce the pace of infections to sustainable levels. Deaths in the range of 50 – 500 over a year are more realistic numbers. 500 deaths is around average for normal seasonal flu [note that Gibson used 870 annual flu deaths]. We found that the higher OCRG numbers were mostly generated by their assumption that tracing and testing would be abandoned.

and

The main purpose of this report is to look behind the ‘shocking’ headlines to assess the robustness of the ORSG modeling, and to present our own modeling results, using the same model. We also present the arguments for the lockdown being less restrictive and present a rough cost benefit analysis of the decision to lockdown the building and construction industry.

From the body of the paper

The OCRG modelling

The OCRG ran six scenarios with the Covidsim model. Three R0 assumptions (1.5, 2.5 and 3.5) were combined with two intervention scenarios that assumed either a 50 percent contact reduction in R0 over six months; or a 25 percent reduction over nine months. As previously noted there was no reduction in the post intervention effective R0 reduction due to case management.

The results are shown in table x

Table x: OCRG scenario results 

Model assumptions Deaths over one year
R0 1.5 25% control over 6 months 2520
R0 1.5 50% control over 9 months 7
R0 2.5   25% control over 6 months 12,700
R0 2.5   50% control over 9 months 8560
R0 3.5   25% control over 6 months 14400
R0 3.5   50%   control over 9 months 11800

As noted above, the R0 1.5 results were not reported in the media. The range was reported as between 8560 and 14400. If the OCRG had so little confidence in the 1.5 estimate then they should have replaced it with a more plausible lower estimate, such as a R0 of 2, and then reported that number. Similarly the upper estimate could have been set at a high, but still reasonably possible 3. Instead the public is given a range of between 8560 and 14400 deaths, giving the misleading impression that there is a good deal of certainty around the estimates of high death numbers because the upper and lower bands are relatively close together.

More importantly the headline results are absent the impact of any case management. They should never have been released to the general public without explaining that there was no contract tracing and isolation process in effect. The numbers with contract tracing should also have been prominently reported.

Who knows what mandate was given to the Otago researchers, but again the bigger issue here is that we do not have any access to the papers the government itself used, to know whether or not the Ministry of Health, Treasury, or ministers themselves were really influenced by these headline numbers –  or whether, for example, they were just being used to sway the public.

There are lots of other specifics in Ian’s paper and when he releases the final version I will be sure to link to it here.  As allluded to earlier, he attempts a cost-benefit analysis for restrictions on one particular large sector –  construction, which has not been closed down in some other lockdown countries –  and concludes that any benefits are vanishingly small relative to the economic costs of that particular marginal restriction.

Ian also tells me that he has attempted to contact the Otago researchers but has not (yet) had any response.

In giving these papers some coverage I am not myself advancing any particular policy view.  I have more questions than answers –  in part because of the continued refusal of the government to make core pieces of analysis and advice public, and instead to fling around numbers that others have generated but that don’t seem to stand up to much scrutiny (and in some cases are not public at all).   As I noted the other day, at a visceral level (rather than an analytical one) I am somewhat uneasy about the rush towards easing restrictions, and I’m not sure 20 cases a day (that the authorities know of) is yet that reassuring.  But, equally, the costs of the path the government has taken to date have also been very high –  and those costs are not just economic in nature (if anything I worry that all the pressure building to ease restrictions is economic in nature, and little at all about families, civil society, and so on.)

And now it is almost time for Orr’s appearance at the Epidemic Response Committee.   Of the Reserve Bank’s institutional failings around this crisis –  and, par for the course, utter lack of transparency –  I am much more confident.

UPDATE: Ian Harrison’s report is now available here.

44 thoughts on “Coronavirus economics and policy: from the mailbox

  1. Michael this post could be one of your most important ever. I am absolutely horrified by both of the papers you cover. [insert three synonyms here]
    Please don’t stop this blog – this is great journalism.

    Liked by 2 people

  2. Are we really surprised. Our leaders are being forced into making difficult decisions, with an election on top of it. Waving scary numbers around, even dodgy ones, that show how we are all in danger unless the leader saves you are par for the course. Create enough fear and your decisions will never be questioned.

    This is not the first, or last time these tactics to cover backsides gets used. Of course it is not just here where this is used, but most democracies would be guilty.

    As for getting it out in the open, good luck, but I would be loathe to place a a bet on the chance of success. To many in positions of power could be at risk of exposure to allow it to happen.

    Liked by 4 people

  3. Just a small point on the per-capita graph at the start. I was curious about why many similar graphs used are absolute numbers of cases rather than per-capita cases. For the early stages of an outbreak, absolute numbers are probably more appropriate to understand how the virus is spreading than per-capita numbers. Until the virus is spread throughout the community, it doesn’t matter how big the total population is, it matters how rapidly it is spreading out from a small number of cases to those around them. This is affected by population density, but not overall population size, until later on when saturation has a dampening effect.
    When swimming (or drowning) it doesn’t matter how deep the water is, it matters how well you are coping with the first 2 meters.

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    • I’m not full persuaded. One could reasonably look at the aus numbers by separate states and each (perhaps not NSW) would then be less than nz in absolute terms.

      But there are clearly no perfect single ways of looking at these things.

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    • I’ve been entirely convinced by that argument, at least in the NZ vs Aus case. One could look at each Aus state individually and NZ and (I think) except for NSW new cases number for each would be less than NZ (and of course in NZ itself there is now pretty extreme geographic isolation between the islands and even between cities/regions.

      Still, almost certainly no one lens is perfect.

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      • I agree there are a range of ways you can cut it. My observation is that people tend to pick the methodology that supports their prior assumptions and the point they are trying to promote. In this case the country-level absolute cases vs per-capita cases imply opposite conclusions about NZ’s vs Australia’s approach. Lies, damned lies, and statistics.

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      • I’m not particularly persuaded by thornley et al or by this article. Re Australia, the per capita death rates are now v similar, but actually mostly relate to activities per the lockdown. I’m struck by the apparent indifference of Wiles and those like her to the costs of the absolutist strategy they continue to champion.

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      • I think Wiles etc pays no attention to the costs as they are generally unaffected by them. Academics etc in state funded jobs

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  4. great of you to share these analyses.

    The government panicked. I doubt modelling influence than that much because they just looked at what was happening in Lombardy and didn’t want to see it happen here. They did have the nerve to look elsewhere and say well the economic cost of the lockdown will be devastating.

    Hospital waiting lists will be a nightmare. I read that the Victorian County Court is suspended all jury trials until next year.

    The bigger problem is the only idea the government has is to spend their way out of this with infrastructure spending which by the way takes a long time to start.

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    • The other problem with big infrastructure is personal. Do we have the necessary project managers, engineers of various types, digger drivers, electricians, fitters and so on. Or does it just become another way to keep immigration running at high levels.

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      • Pessimism bias is best illustrated to me from my experiences at the Department of Labour after the Canterbury earthquake.

        I made a bit of progress pointing out in the week of the earthquake that the share market hadn’t crashed despite the earthquake.

        I was tasked to write a paper on the supply of labour to the construction industry. I used the construction boom in the early 2000 is to illustrate how labour supply to construction can increase and contract by 1/3 inside a few years.

        It was incredibly difficult to write that paper because of management opposition to saying anything optimistic about the ability of the economy to adapt.

        There is a separate panic about the inability to find Filipino labourers to come in to help because of the Japanese earthquake not long after. I made a little bit of progress pointing out that we only wanted a couple of hundred Filipino labourers and there were 90 million people in the Philippines and that doesn’t include 10 million overseas contract workers.

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  5. I have been impressed by the public relations aspect of this epidemic; both Dr Bloomfield and our prime minister have been effective speakers and their message is important. However I couldn’t help thinking back to when I was last impressed by a set of press conferences and all I could recall was the manager at Pike River saying all the right words with just the right tone; it was later that he was prosecuted for failings related to the accident. We must have a royal commission.

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  6. I knew it! Their modelling was just ridiculous…Well done Michael
    NZ has a serious reputation of bad political knee jerk reactionary responses

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  7. This part didn’t make the cut in the report

    Part nine: Being cruel to be kind?

    The problem with the stamp it out at all costs mentality is that it does not consider the costs and benefits at the margin. Instead the test has become whether the activity is ‘essential’. Anything else is fair game for repression, almost regardless of the cost.

    One of the ‘signature tunes’ of the Covid-19 public information campaign is to ‘be kind’. Unfortunately the eradication at (almost) any cost mentality is resulting in actions that are anything but kind. They can be variously described as cruel, heartless or harsh, and at the least thoughtless. The effective ban on funerals is an obvious example. A funeral with 10 people (the Australian government recommendation) with social distancing requirements would not pose a material risk, but this is not allowed.

    Then there was the television news report of the flower grower who was destroying his crop (and with it his business that he had built up over 30 years). These flowers could have gone to supermarkets where they could be picked up by people shopping for food. Picking up a bunch of flowers poses no health risk, but as it is not ‘essential’ it appears to have been banned.

    The ban on some exercise activities is another example. The Minister of Health was pilloried for taking his car to go mountain biking. Aside from using his car, he was engaging in a ‘risky’ activity, which could put pressure on health resources. The problem with this rationale was that there were just 15 COVid-19 cases in hospital and three in intensive care at the time. Hospitals are quiet with substantial unused resources. While there might be a case for restrictions on risky activity in a few weeks if the caseload grows, there certainly isn’t one now. Risky behavior, it is now claimed, could divert police resources to (a small number) of rescue operations. This might be a good thing if it diverts the police from activities that are giving a sense that we are living in a police state. Incidents of ‘risky behavior’ that has been stopped by the police that have come to our attention include: paddling in Oriental Bay and fishing at Evans Bay.

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    • An excellent report, concurring in almost all respects with my own (43 years of experience in the risk management in oil industry) thoughts. I’m afraid that it will be in vain though as the approach chosen by the Government appears to be guided, to a large degree, by political considerations.

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    • Attempts to eradicate via lockdown effectively amount to holding 5 million in limbo, at a cost of hundreds of millions per day as we wait for a handful of infections to run their course. The slow rate of disease progression in Covid means it can take weeks for infections to show. We can’t wait that long, and South Korea and Taiwan show a more rational path to eradication. Govt should be spending all their money on measures that root out the disease (testing and tracing) with less restrictions on useful activities, trust NZers to mostly do the right thing, insist everyone wears masks in public (so cheap to do). Not this vast wasteful and now patently pointless lockdown ‘show’ they are running. Eradication will not happen in the next month no matter what they do.

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    • ‘vindication’, but only if you use very naive measures. Loss of wealth kills people – quite clearly as measured by the pharmac price on quality of life years (~$40k per Qaly from memory), or in the road safety budget budgeting a few million per life saved. Economic shocks see death rates from suicide and destructive behavior rise, long term GDP growth is hampered as children do less well through education and young adults are denied careers and skill development in the workplace – leaving dead weight in social welfare costs and great personal unhappiness as exemplified by the sick economies of southern Europe.

      To be blunt the politicians deciding these matters are far too unsophisticated in their understanding of the seismic consequences of their actions – responding re actively on gut-feeling like ignorant teens with Manichean world views rather than as well educated leadership who understand nuances of the decisions and the workings of the world. The current cabinet is utterly deficient.

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  8. Excellent analysis and commentary, thank you. There are many ways to look at the data; but net cases drives hospitalizations (which have capacity constraints) and NZ is behind (in a negative sense) Australia on this metric. despite having what is an NZ L3 regime. The second is deaths per capita, where the death rate of over 75’s in NZ is seems to have increased by 1-2% – but it will be months before we see this with any certainty. Not ideal, but certainly marginal.
    What I have not seem commented on is the exponential curve of business failure. The longer the L3 restrictions are in place, the more businesses will fail and it is not a linear relationship.

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  9. I found Ian’s work very interesting although I have skimmed it rather quickly.

    It is pretty charitable to call these things “models”. They are really just back-of-the-envelope calculations that have been typed up, although I see there are many parameters that can be specified, and you can generate pretty much any scenario.

    A key point is that a parameter such as R0 is clearly endogenous and not constant. Many factors have been identified as influencing it, including population density, temperature/seasonality, social customs, business practices, distancing behaviour, maybe age/gender, etc etc.. Some or all of these also impact the parameters on hospitalisation rates and death rates.

    A proper model would be able to disentangle the relative influences of all these things, and explain the rather puzzling differences in the experiences of different countries and regions. I suspect that such a model is still a long way off, and meantime we will still be flying in the dark to a fair extent.

    And, by the way, as a former modeller I used to say that models always let you down just when you need them most

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  10. 1.4 is the lower bound (via Wikipedia) of estimated R0, so 1.5 is probably unrealistic to include. 5.7 is the upper, so even the 3.5 estimate looks quite possible. I recall hearing somewhere between 3 and 3.5 as an official estimate a few weeks ago.

    I would personally like to see a lot more testing, tracing and mandatory quarantine, but I note that the government has not been afraid to spend vast amounts of money lately on things not directly related to testing, tracing and confining, so I’m picking it’s not a lack of will that is holding us back from increasing these things, but rather a lack of staff, infrastructure, and supplies. I would be very surprised if they are not working hard to change that.

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    • I had similar hope, but after the govt took 6 weeks to organise something as critical and as easy as arrivals quarantining I think the truth is they are an utter omnishambles – as is born out by everything else they have tried to achieve in the last two years. There are 400000 employees in the public sector, yet there has been an almost total failure to enact effective measures in a timely manner – or work towards reducing the calamity befalling us (eg why aren’t there huge programs to expand hospital capacity while everyone is sitting around?)

      Wouldn’t it be great if they asked for help. Vast numbers currently unemployed and unoccupied or just straight up wanting to help stave off this apocalypse in any way possible. We could be achieving huge things if only we had some competent leadership.

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      • I would like to know what is really going on too. Weren’t people who didn’t have a credible plan supposed to be isolated straight away though? I could see people being reluctant to enforce it though.

        One thing I do know about through checking for myself is that they were looking for call centre staff straight after Healthline started becoming overloaded. Of course, it was patently obvious that would happen, and I couldn’t tell you when they started looking, but they were onto it pretty fast.

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      • I was quite amazed when someone I know was worried re symptoms and rang Healthline: unlike stories from a few weeks ago, the call was answered almost immediately and a proper nurse rang back within an hour or two.

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  11. Thank you for commenting on these two papers. I have read Ian Harrison’s paper in particular after being horrified that the Otago study assumed no contact tracing or testing with the upper level of estimated deaths being what appears to be used to justify the Level 4 lockdown (along with other information not publicly available). I believe using this information would be professionally irresponsible if Ian Harrison’s conclusions are validated by the authors of the Otago modelling once he can speak to them.
    I noted that the early version of the Otago model was available to Government in late February at least two weeks before the border was controlled on March 16 with only self isolation arrangements in place for incoming travellers until April 10 when mandatory quarantine measures for 14 days were activated. Would it be possible for Ian Harrison’s model to estimate the number of confirmed cases if the model assumed mandatory quarantine for 14 days from March 16?
    It may be simplistic, but if the Government had put full quarantine measures in place as at March 16 and concurrently expected those over 70 and vulnerable from health reasons to self isolate, that the rest of the population could have continued to live at perhaps Level 2 until all threat from incoming travellers had passed.

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  12. I am glad this analysis is being done. John Gibson makes a good point about comparison with Asian countries. But his life expectancy argument seem very weak. There is a correlation between wealth/income and life expectancy, but that doesn’t mean a sudden drop in income or wealth will result in a corresponding change in life expectancy. Wealthy people may live in quieter neighborhoods with less crime, have better knowledge and education, better food and healthcare. Those factors are not going to change proportionately if you have a 10% or 20% drop in income.

    I’d expect the difference in life expectancy to be most pronounced for the lowest incomes, and be fairly flat for middle and higher incomes. If that is the case, we could even see a rise in life expectancy if the loss in income were mainly borne by the wealthy and with a $25 increase in benefits. The loss in income won’t affect the life expectancy of the wealthy much (except for private medical care at end of life), but $25 a week for extra food, less pollution and car crashes could bring up life expectancy.

    There are very real costs to the lockdown but I doubt life expectancy is a useful way of showing them.

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  13. I agree that the modelling had glaring omissions, and that the societal harm from unjustifiably strict lockdown has been excessive and not well acknowledged by Government representatives, including the increase in domestic violence, damaged mental health and reduced educational achievements, let alone the financial harms which will continue to compound all of these.
    However the biggest failure is not acknowledging the gamble of awaiting an effective vaccine, while striving to eliminating cases reducing any herd immunity amongst those who are very low risk. Coronaviruses are naturally poorly immunogenic, even natural infection does not give long term protection (although very likely reduces the intensity of future infections), which is why we continue to get colds fro coronoviruses. To date there have been no effective coronavirus vaccines. Some trials were abandoned as the vaccine primed the immune system causing an increased inflamatory response on exposure to the virus (it is this response that is thought to be the cause of the severe illness in the subset with COVID who get very unwell). To date the shortest wo to go vaccine development has been 4 years (not the quoted 12-18months), and logistics of who gets it first and number of doses required internationally are also substantial issues. Duration of protection from any COVID vaccine is very uncertain. This virus is endemic worldwide and will reinfect as soon as borders are open, leaving us sitting ducks currently. The safety required of this vaccine for the young (almost zero deaths and minimal severe illness with natural infection) is thus extraordinarily high (or do we leave them to get infected later?). The efficacy (protection rate offered) does not need to be high to allow it to be registered as effective overseas, where infection is almost gauranteed otherwise – 30% protection is better than none – but has to be very high here, where the whole population has no baseline immunity, usually we aim for 95% coverage for complete “herd” immunity.

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