How COVID health advice and modelling has been opaque, slow to change and politicised in Australia

William Bowtell, UNSWIn a recent article, The Australian’s health reporter asked: “has any modelling put forward by scientific institutes throughout the pandemic ever proved accurate?”

It’s a good question but the answer lies in understanding the truth about modelling — it cannot predict the future.

Rather, it’s a process that identifies variables most likely to shape the course of, say, a pandemic and to quantify their impacts over time.

Politicians commission modellers to assess the present state of things then consider what might happen if various policy settings were to be adjusted.

By providing assessments of the costs, benefits and impacts of proposed policies, good modelling provides governments with a firm foundation for deciding which policies will have what effects.

Politicians know invoking “health modelling” generates public support for their policies.

This week, federal Treasurer Josh Frydenberg claimed his decision to scrap COVID support payments at 80% double-dosed vaccination coverage accorded with the National Plan as informed by the Doherty Institute modelling.

But in neither the plan nor the modelling is any connection drawn between ending support payments at any level of vaccination coverage.

Nor was any modelling apparently commissioned on the likely impact of removing financial support for the most vulnerable when infection rates are high – as in Sydney – and rising alarmingly as in Melbourne.

Read more:
Scientific modelling is steering our response to coronavirus. But what is scientific modelling?

The power of ‘health advice’

Since the beginning of the COVID pandemic, politicians have justified the many difficult decisions they’ve had to make as being based on “health advice”.

As it should be, “health advice” provided to politicians by chief health officers is informed by modelling commissioned from a range of well-respected and credentialed scientific research institutes.

The public draws a strong causal link between health modelling inputs and policy outcomes.

They are more likely to accept policies buttressed by modelling and health advice than not.

Modelling is therefore a powerful political tool.

In a pandemic, political decisions have human and economic impacts that are irrevocable, significant and for many a matter of life and death.

Even more reason, therefore, for the scientific integrity of modelling that informs those decisions to be beyond reproach.

The brief given to the modellers is critically important in setting parameters and assumptions and selecting the variables that will be assessed and measured.

Transparency is essential

The key to building public trust in modelling is full transparency.

But in Australia, these briefs and processes are often shrouded and opaque. Secrecy and a lack of transparency has greatly affected the quality of Australia’s response to COVID.

At the beginning of the pandemic, the federal government’s Emergency Response Plan for Novel Coronavirus did not canvass the cessation of international travel and closure of borders, domestic lockdowns and the use of masks as possible or desirable responses to the pandemic.

Yet within weeks of this advice being published, the modelling had been overtaken by events.

Travel from some but not all countries was stopped, international and domestic borders closed from late March 2020, and lockdowns implemented across Australia.

In the initial planning and options, lockdowns, cessation of travel and masks were not among the assumptions. The entire response was based on a paradigm of influenza rather than the facts of coronavirus and need for rapid, preventive responses.

The assumptions informing the initial modelling should have been published, interrogated and debated before, and not after, the initial and ineffectual policy settings were adopted.

Read more:
Australia’s COVID plan was designed before we knew how Delta would hit us. We need more flexibility

Separating science from politics

Over the course of the pandemic, the assumptions of modelling commissioned by governments should have been published, scrutinised and debated before, not after, the modelling was undertaken.

Modelling ought to have been commissioned from a range of Australia’s excellent scientific institutions.

Open debate might have meant aerosol transmission of first Alpha and then Delta would have been factored into projections and policy-making about the efficacy of hotel quarantine and border protection far earlier than it was.

This unnecessary addiction to secrecy has eroded the trust and confidence that should exist between governments and the people.

Politics and science each have their separate and distinct roles to play in the managing the pandemic and reducing to the lowest possible levels the damage it causes to lives and livelihoods.

In the response to HIV/AIDS, the politicians of the day ensured scientific advice was provided independently of governments and published as it became available.

The advice became the foundation of the political decision-making process.

Now, as then, Australians expect a similar standard of open and independent scientific advice, information and assessment about the present and likely impact of the pandemic.

Whether commissioned by governments or acting independently, Australia’s pandemic modellers have lived up to their responsibilities to science and the Australian people.

They have applied their expertise to quantifying COVID and the costs and benefits of policy options.

But the critical decisions on assumptions, debate, contestability and transparency are made by politicians, not modellers.

As much as some politicians may wish to deny it, they alone are responsible and accountable to the Australian people for the decisions that have created Australia’s COVID response and will shape its future.

Modelling is integral to building the most robust, sustainable and well-supported response to the increasingly complex challenges of the pandemic.

The Australian people will be best served by separating science from politics.

Read more:
Explainer: do the states have to obey the COVID national plan?

The Conversation

William Bowtell, Adjunct professor, Kirby Institute for Infection and Immunity, UNSW

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Why Sydney’s COVID numbers didn’t get as bad as the modelling suggested

Jamie Triccas, University of Sydney and Megan Steain, University of SydneyLast Monday, Sydney emerged from a lockdown of more than 100 days after reaching the milestone of having 70% of the over-16 population fully vaccinated.

Modelling predicted New South Wales would “open up” with around 1,900 daily cases when this target was reached.

However, the state recorded just 496 new local cases on that day. And the current seven-day average for NSW is 488 cases, with numbers trending downwards.

What’s more, other modelling suggested COVID-19 hospitalisations would peak between 2,200 and 4,000 in greater Sydney in late September.

On September 21, peak COVID hospital occupancy for all of NSW was 1,268 patients. There are currently 711 COVID patients hospitalised in NSW, as of October 14.

We propose there are two main factors which might account for these discrepancies.

Vaccine effectiveness underestimated

Firstly, predictions of vaccine impact have typically used estimates of effectiveness against the Delta variant based on the UK Scientific Advisory Group for Emergencies (SAGE) roadmap, published in June. This suggested an effectiveness against hospitalisation of 87% for Pfizer and 86% for AstraZeneca.

However, more recent data across numerous countries has shown effectiveness against severe infection and hospitalisation is somewhat greater. A different UK study suggested 95% protection against hospitalisation for both Pfizer and AstraZeneca. And a study from the Netherlands found 96% and 94% protection against hospitalisation for Pfizer and AstraZeneca, respectively.

This difference may account for the disparity between the actual NSW hospitalisation numbers and those predicted based on the current vaccine rollout.

Real-time protection

The second reason for the current NSW situation could be a concept we’ve termed “protection in real-time”.

The rapid pace of vaccine uptake during NSW’s Delta wave ensured there was a large proportion of recent vaccines within the population.

This may offset the impact of waning vaccine immunity.

Optimal immunity after vaccination occurs at about two weeks after getting the second dose. But a partial protective effect of vaccination with Pfizer was apparent in clinical trials as early as 12 days after the first dose.

In addition, protection against severe infection may only require a lower level of immune response after vaccination.

Read more:
How long does immunity last after COVID vaccination? Do we need booster shots? 2 immunology experts explain

How has this played out overseas?

The protection in real-time concept can be used to explain the impact of vaccination in other countries, which may provide a “real world” perspective of the future of the pandemic in Australia.

Denmark reached 25% vaccination of the total population before the arrival of the Delta variant. During the Delta wave there were reduced hospitalisations and deaths compared to previous waves and a dissociation between case numbers and deaths.

You can see the black line (cases) starts to separate from the green line (hospitalisations) and the red line (deaths) as the vaccine rollout progresses.
Data from and, Author provided

NSW’s achievement of reaching the 70% threshold last week actually equates to around 56% of the total population of NSW. At the peak of its Delta wave in July, Denmark reached 50% vaccination coverage of the entire population.

The restrictions in place at this time in Denmark were requiring proof of vaccination, past infection or a recent negative COVID test to enter certain indoor settings, such as restaurants and cinemas.

With a population size similar to greater Sydney, the coming months in Denmark may serve as an important comparison as to how the pandemic may unfold in Australia.


Similarly in Singapore, vaccination rates are high, at around 80% of the total population, and the pace of the vaccine rollout is very similar to Denmark.

Singapore has seen a recent spike in cases since the relaxation of restrictions, with case numbers at their highest. However, 98% of these cases are mild or asymptomatic. This suggests vaccines are having a major impact on lessening the severity of COVID, but a less pronounced ability to completely interrupt disease transmission.


Another example of the impact of real-time protection is the situation in Israel. Israel is often used as as the benchmark of vaccine effectiveness. Its vaccine program involved a rapid rollout of mRNA vaccines, predominately Pfizer’s. Initial studies in the country found the vaccine had high effectiveness against symptomatic COVID-19 and hospitalisation.

However, the arrival of Delta in Israel resulted in a large increase in COVID-19 cases with accompanying spikes in hospitalisations and deaths.

While this may provide some insight into the impact of Delta in Australia, there are key differences.

Israel experienced a large increase in COVID cases, hospitalisations and deaths after the arrival of the Delta variant.
Data from and, Author provided

Why did hospitalisations rise in Israel? And what are the lessons for Australia?

Israel saw a large proportion of the eligible population vaccinated quickly. Around 50% of the total population was fully vaccinated by mid-March. But after this, there was a marked slow-down in uptake.

The NSW and Australian populations have been vaccinated much more recently than Israel’s.
Data from and, Author provided

Thus, a combination of waning immunity and a large unvaccinated population may have exposed Israel to Delta.

While the Pfizer vaccine demonstrates excellent effectiveness against severe COVID-19, recent evidence from Israel suggests some waning of protection against severe disease over time, which prompted the introduction of the country’s booster program in July. A third dose was initially offered to over-60s, before being extended to everyone aged 12 and over.

In Australia, the widespread rollout of booster shots in the near future would be premature. The priority now is to get everyone eligible fully vaccinated, and consider boosters for targeted groups.

The federal government announced last week booster shots would be available to Australians who are “severely immunocompromised” from this week.

Governments should also consider a “mix and match” approach of booster shots. This strategy is being pursued in the UK, based on evidence that combining different vaccines may lead to stronger immunity.


The Conversation

Jamie Triccas, Professor of Medical Microbiology, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney and Megan Steain, Lecturer, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Opening with 70% of adults vaccinated, the Doherty report predicts 1.5K deaths in 6 months. We need a revised plan

Stephen Duckett, Grattan Institute and Anika Stobart, Grattan InstituteOne consequence of the escalating COVID outbreak in New South Wales has been increased political tension around the “national plan” for COVID reopening.

The prime minister has argued that states signed up to the plan – albeit “in principle”, whatever that means – and they should do whatever the plan says, whenever the plan says to do it.

Some premiers are now pushing back, arguing the Doherty Institute modelling was based on certain assumptions which no longer hold true so the previous agreement no longer stands.

There are three distinct questions at issue here. Is the Doherty Institute modelling still applicable? How does the national plan stack up? And what should happen next?

1. Is the Doherty Institute modelling still applicable?

The Doherty Institute was given a very specific remit. It was asked “to define a target level of vaccine coverage for transition to Phase B of the national plan”, where lockdowns would be “less likely, but possible”.

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In identifying the vaccination coverage target for the transition to Phase B, Doherty’s experts assumed that testing, tracing, isolation, and quarantine (TTIQ), would be central to maintaining lower case numbers.

They highlighted two scenarios in terms of testing-tracing-isolation-quarantine capacity – an “optimal” scenario and a “partially effective” scenario – summarised in the table below.

Doherty Institute modelling outcomes

TTIQ = testing, tracing, isolation, and quarantine. This assumes an all adults vaccination allocation strategy.
Doherty Institute

While these numbers may look acceptable, the assumptions underlying them are now hanging by a thread.

Case numbers have been rising rapidly, putting significant pressure on testing-tracing-isolation-quarantine capacity.

Doherty Institute described its assumptions thus:

We assume that once community transmission becomes established leading to high caseloads, TTIQ [testing-tracing-isolation-quarantine] is less efficacious than the optimal levels observed in Australia because public health response capacity is finite.

This tells us that given our current high case numbers, we can probably only assume, at best, “partially effective” testing-tracing-isolation-quarantine capacity.

It’s also important to note the Doherty modelling did not incorporate scenarios where the virus was in uncontrolled spread after target vaccination levels are achieved.

But it now seems unlikely that NSW – and maybe even Victoria – will be able to suppress COVID down to zero before any vaccination target is reached.

If lockdowns are eased according to the modelled targets, while there is still substantial community transmission, testing-tracing-isolation-quarantine is unlikely to be enough to suppress further spread sufficiently, potentially resulting in higher numbers of hospitalisations and deaths than initially modelled.

2. How does the national plan stack-up?

The federal government used the Doherty Institute report’s findings as the basis of the “national plan” it put to National Cabinet.

But it glossed over the options, scenarios, and caveats in the Doherty modelling, and assumed the most optimistic testing-tracing-isolation-quarantine scenario: that everything would be rosy if Australia started opening up once 70% of adults (equivalent to only just over half the population) are vaccinated.

Read more:
National Cabinet’s plan out of COVID aims too low on vaccinations and leaves crucial questions unanswered

The transition to Phase C, where lockdowns would be targeted and vaccinated people would be exempt from restrictions, was also optimistically adopted at 80% adult vaccination, despite the lack of modelling for this scenario in the Doherty report.

In a bid to make it appear convincing – but also realistic, given all the uncertainty – a veil of vagueness was cast over the national plan. The document is full of weasel-words and caveats, which means it is impossible for anyone to be held to account.

The equivocal “in-principle” condition on National Cabinet’s approval makes it even harder to know exactly what premiers signed up to.

But the severity of the New South Wales outbreak has forced some of our leaders to take off the rose-coloured glasses and adopt a more realistic view. Premiers are now saying they did not sign up to high death tolls.

According to Doherty modelling, deaths could reach 1,500 within six months of implementing Phase B. Agreeing to such a scenario is politically untenable for states that currently have zero cases.

3. So, what should happen next?

With states divided over the national plan, and the modelling potentially out of date, it’s time for National Cabinet to come back with a new approach. We need a revised national plan – one that all states can sign up to, one that is not full of caveats and conditions.

This should include a realistic plan for scaling up testing-tracing-isolation-quarantine capacity so that it can manage in a feasible way when each infected person could have at least ten new contacts per day.

And it should include a plan to protect primary schools and childcare centres while a vaccine remains unavailable for younger children.

Grattan Institute has also done its own modelling.

But our model was about Phase D – what Australia needs to do to avoid obtrusive restrictions such as lockdowns altogether – which was not modelled by the Doherty Institute.

We argued that it is only safe to open the borders, to lift restrictions, and to manage without lockdowns and use only unobtrusive measures such as masks on public transport, if we vaccinate at least 80% of the total population and continue the vaccination rollout to 90% throughout 2022.

Recent modelling from other academics has come to similar conclusions, with some even suggesting a slightly higher threshold for safe re-opening.

Governments cannot keep making unrealistic promises about easing restrictions at 70% and 80% adult vaccination, a plan that relied on optimistic scenarios in the first place, and one that now bears little relation to the real world. It is irresponsible to build public momentum and hope around targets that are unlikely going to be enough.

Australia needs the National Cabinet to come clean and accept that the changing circumstances require a change in the plan.The Conversation

Stephen Duckett, Director, Health and Aged Care Program, Grattan Institute and Anika Stobart, Associate, Grattan Institute

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Latest coronavirus modelling suggests Australia on track, detecting most cases – but we must keep going

Trent Yarwood, The University of Queensland

Late yesterday, epidemiologists from the Doherty Institute released what the Chief Medical Officer described as “nowcasting”: modelling that uses data from the previous 14 days to more accurately understand the present state of the COVID-19 epidemic.

In short, the findings are reassuring and suggest the inconvenience of social isolation is helping control the spread of SARS-CoV-2 in Australia.

It also indicates the spectre of “unidentified community transmission” is very unlikely indeed. This should be especially reassuring for healthcare workers, who may worry about coming into contact with COVID-positive patients presenting with a non-COVID problem.

What I don’t think it means, however, is that our outbreak control has been so effective that we should consider loosening the restrictions now.

Overseas methods, Australian data

The important thing to know is that this latest modelling uses Australian data.

One of the earlier criticisms of the Australian government’s response to COVID-19 was that the expert advice was kept behind closed doors.

When the modelling was made public, those determined to find fault (especially on Twitter) pivoted to “But it’s based on overseas data!”

That’s not a criticism that can be levelled at this latest Doherty Institute modelling, which borrows methods developed by the London School of Tropical Medicine and Hygiene but uses really recent Australian data to build some estimates.

Read more:
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We are likely detecting most COVID-19 cases

First, the modelling suggests there’s probably not some huge secret cohort of COVID-19 cases out there that we are not picking up due to insufficient testing.

The researchers compared the reported case-fatality rates (the proportion of COVID-19 positive people who died) in Australian states with that from a large Chinese study (1.38%).

This is then used to infer the proportion of cases with symptoms which have been found by testing.

All states/territories have case detection rates above 80% – meaning that in each state, of all the people who have COVID-19 with symptoms, we are picking up about 80% or more.

If it wasn’t for the recent outbreaks in Tasmania, then all states would be above 90%. And in fact, the overall estimated case detection rate Australia-wide is 93%. Good news!

And as time goes on, the researchers are growing more certain about this conclusion (the technical term for this is the change in the “90% confidence interval” but in plain English that means the scientists are growing more confident these estimates are pretty accurate).

The change in the light blue shaded area means scientists are growing more confident that their estimates are accurate as more Australian data becomes available.
Doherty Institute

An effective R below 1: meaning social distancing is working

What scientists call the effective R is the way the virus spreads in a world where social distancing measures are in place. It refers to the average number of people each COVID-19 positive person is infecting. If it is below one, then it means the social distancing measures are working well.

The next model in the new Doherty Institute paper looks at the effective R ₀ in the six states over time.

The effective R ₀ is under one in all states except Tasmania, but treat the Tasmanian data with caution: they have a small number of cases and a recent uptick, so that could be blowing out the average.
Doherty Institute

In most states, the effective R has always been below one – indicating Australia has been effective at controlling spread since the beginning of the outbreak.

However, the numbers in Tasmania should be interpreted with caution. Their overall case numbers are small and they just had a big cluster, which affected their average disproportionately.

Crucially, the study team calculated the effective R based on cases identified as local transmission, rather than imported cases. That means, in real life, the effective R may be even better than this model estimates (because this estimate doesn’t account for border restrictions and quarantine of travellers).

In other words, this modelling is aiming to look at how effective our domestic control measures are. And the answer is: they’re working pretty well.

Too soon to relax social distancing rules

The social distancing measures take time to have an effect in stopping transmission.

It would also take time to become visible if we back off too early.

See-sawing our control measures would probably be far more disruptive than holding the course for just a little bit longer, and pose a risk of coronavirus rebound.The Conversation

Trent Yarwood, Infectious Diseases Physician, Senior Lecturer, James Cook University and, The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Scientific modelling is steering our response to coronavirus. But what is scientific modelling?

Rachael L. Brown, Australian National University

As they released the modelling of the COVID-19 pandemic behind Australia’s social isolation policies this week, Prime Minister Scott Morrison and Chief Medical Officer Brendan Murphy were guarded.

They emphasised the limits of scientific models, and how they could easily be misinterpreted.

This is not surprising. Many people don’t have a clear understanding of what scientific models are, and what we can and can’t expect from them.

Scientific models can be powerful tools for understanding complex phenomena such as pandemics, but they can’t tell us everything.

Read more:
Yes, we’re flattening the coronavirus curve but modelling needs to inform how we start easing restrictions

What is a scientific model?

Scientific models are representations of parts of the real world. They range from small-scale physical models of real systems, such as the famous San Francisco Bay Model – a miniature version of the bay used to investigate water flow – to the type of mathematical models used to understand the spread of COVID-19.

The San Francisco Bay Model was built in the 1950s to study the effects of a proposal to build dams in the bay.
Something Original / Wikimedia Commons

Models can be used to indirectly explore the nature of the real world. They can help us understand which features of real-world systems are important, how those features interact, how they are likely to change in the future, and how we can alter those systems to achieve some goal.

Why are models so valuable?

Scientific models let us explore features of the real world that we can’t investigate directly. In the case of COVID-19, we can’t do direct experiments on what proportion of Australia’s population needs to engage in social distancing to “flatten the curve”. Even if we could devise good experiments, it takes days or weeks for people to become sick and transmit COVID-19, so any experimental results would arrive too late to be useful.

Models are invaluable in situations like the COVID-19 pandemic, where time is of the essence and we are interested in effects on a large scale.

What are the limits of scientific models?

A model’s usefulness depends on how accurately it represents the real world. To make an accurate model, you need good data.

That’s one reason why models of the spread of COVID-19 that use data from densely populated parts of Europe are unlikely to offer valuable insights into the situation in suburban Sydney. Data from one situation may not apply to the other.

This is a major challenge for the COVID-19 pandemic, especially in Australia. The lack of extensive local data has left our policymakers relying on models based on a combination of overseas data, general theory and pre-existing modelling of influenza pandemics.

Because of this, the models are not designed to be used for making predictions about what will happen.

Read more:
Modelling suggests going early and going hard will save lives and help the economy

For example, Imperial College London is producing relatively detailed modelling that can be used to make accurate predictions about specific cases in the United States and the United Kingdom. But such models require detailed data.

The Australian modelling generated by the Doherty Institute to look at the impacts of interventions on the spread of COVID-19 is simpler and more general. These models offer valuable large-scale insights, but far less local precision.

Such general models have been particularly useful early in the pandemic, when localised information is scarce. As we build a more detailed picture of Australian circumstances, modelling will become more specific and more accurate, and these general models will be less important.

One challenge for modelling in a real-world context like COVID-19 is that our models may not get it right every time. This is partly because we lack enough fine-grained information about the real-world situation.

It is also because individual actions and sheer bad luck in the short term can make big differences in the longer term. A single individual who fails to isolate or quarantine themselves can produce a very large ripple of downstream effects. We have seen this in the case of South Korea’s Patient 31, who triggered an enormous cluster of infections in her church.

What does this all mean?

Despite the uncertainty inherent in the COVID-19 pandemic, we should be optimistic about the science. The general principles behind the models we are basing our public policy on are the product of decades of testing and research, and we are learning more and more specific information about COVID-19 every day.

Thanks in large part to the power of model-based science, we are in a far better place than any generation before us to deal successfully and efficiently with a pandemic of this scale.The Conversation

Rachael L. Brown, Director of the Centre for Philosophy of the Sciences and Lecturer at the School of Philosophy, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Modelling suggests going early and going hard will save lives and help the economy

Quentin Grafton, Crawford School of Public Policy, Australian National University and Tom Kompas, Australian National University

In 1997, a bestselling book by Jared Diamond purported to explain how the West “won” world dominance based on the good luck of geography, and because western countries were the first to industrialise.

Fast forward to 2020, and to COVID-19. Geography still matters, but the West is no longer “winning”.

Despite initial mistakes, it seems China has been successful at containing the virus, and other countries such as South Korea and Singapore have, so far, been able to dramatically slow the rate of infection.

Western countries were slow to respond and are paying a very high price. As of March 30, Italy had 98,000 confirmed cases and 10,800 COVID-19 deaths.

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While cross-country comparisons on confirmed cases are problematic because of large differences in testing, the United States currently has more than 137,000 confirmed cases – the highest in the world, more than in China.

This number will get much larger very quickly if cases continue to double every few days.

The number of Americans who will die will soon be in the thousands, and possibly tens of thousands, if the US does not do much more at a national level to ensure physical distancing.

If the current growth rate continues, parts of its health system, especially intensive care units, will be overwhelmed.

Exponential growth

Currently, the rate of infection – without sufficient measures – tracks very closely exponential growth.

The Conversation, CC BY-ND

This allows us to accurately predict, with a basic disease spread model, the minimum, maximum, and most likely number of confirmed cases, at least for the next week or so (although it should be noted that an increased rate of testing will increase this number).

The data tells us that for countries in the earlier phase of the pandemic such as Australia the number of confirmed cases doubles every few days.

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In Australia it began by doubling roughly every four days, and is now doubling every seven days. (The number undoubtedly underestimates the rate of infection.)

Australia had about 2,000 confirmed cases on 24 March. Given rates of infection and changes in growth, our forecasts of infections made on March 27 for Sunday March 29 ranged from 3,950 to 4,460.

Robust short-term predictions

The actual reported number on Sunday March 29 was 3,984, near the low end of that range.

Our forecast for 6pm on Wednesday April 1 now ranges from 5,080 to 5,970 cases, with 5,220 most likely.

For Thursday April 2 the range is 5,510 to 6,835, with 5,715 most likely.

Until physical distancing has had an effect, exponential growth is as good as certain. This will make our forecasts robust.

The current measures might already be cutting infection growth rates, but it is too early to tell. Even stricter measures will be needed to cut the number infected.

With sufficient physical distancing, Australia could end up with an infection rate as low as 1%. By comparison, if it fails to control the infection by not implementing sufficient physical distancing, it could end up with a much worse rate of 20%.

The payoff from going hard and going early

What is the difference in the number of deaths between an infection rate of 1% versus 20%?

Overseas death rates suggest Australia could face an additional 48,000 premature deaths without distancing. This is equivalent to about 30% of annual deaths in Australia.

Although recent evidence suggests young people might be more vulnerable than previously thought, those premature deaths would be clustered in the old and those with other illnesses, and those also in remote Indigenous communities, should the virus get there.

Read more:
New OECD estimates suggest a 22% hit to Australia’s economy

Economists use the value of an economic life for cost-benefit analysis of public projects. It is a measure of society’s willingness to pay to reduce the risk of an additional death.

Using the New South Wales Treasury’s value of a statistical life of $4.2 million, the economic loss of 48,000 premature deaths amounts to some $200 billion, or about 10% of Australia’s annual economic output.

This means it makes sense to act early and hard before the infection rate gets too high, cutting it as quickly as possible. The Spanish Influenza pandemic suggests aggressive physical distancing works.

The question Australians should ask of their leaders is this: is strict physical distancing a cost worth paying?

Costs and benefits from distancing

The main economic benefit from insufficient physical distancing would be that, at least initially, more Australians would stay employed, there would be more economic activity, more taxes would be paid, and government would need to spend less.

But not imposing a lockdown or equivalent measures would come at the cost of a higher infection rate, which would also mean more non-pandemic patients might die because of insufficient beds or medical equipment or staff to look after them.

A higher infection rate would also increase the death rate of pandemic patients as there would be fewer ventilators available to treat each one.

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And the economy would suffer even without sufficient physical distancing, although the worst would be delayed. Many people would still get sick and be unable to work until they were recovered.

A much higher infection rate would also isolate Australia from the rest of the world. Why would any country want Australians to visit if it had high rates of infection, and why would anyone from another country want to visit Australia?

The wage subsidy provides a way out

A high enough wage subsidy for all workers (including part-timers and casuals) who cannot work because of control measures, coupled with the already announced additional $550 a fortnight COVID-19 supplement to the Jobseeker Payment, could provide most Australians with enough income to survive and pay the bills during a lockdown.

Such an approach combines “sharing the burden” with “flattening the curve”, a two-fold economic and public health approach that would save lives while minimising economic disruption, especially for younger and casual workers who are the most disadvantaged by severe physical distancing.

It’s the smartest and safest strategy, and Australia appears to be adopting it.

Our model for the spread of the infection is an adapted [SEIR-M] model. It is still under development and needs further validation and also peer-review.

For now, we assume a homogeneously mixed population. We are also working on a spatially explicit model to account for more complex population contact.

Read more:
‘Overjoyed’: a leading health expert on New Zealand’s coronavirus shutdown, and the challenging weeks ahead

Our current results are roughly in line with changes in basic growth rates and their projections by state.

We will continue to provide forward projections that can then be compared with actual numbers.

All data is sourced from state and commonwealth websites.

A valuable discussion of this and more complicated infectious disease models is found on the University of Melbourne Pursuit website.

This piece is co-published with Policy Forum at the ANU Crawford School of Public Policy.The Conversation

Quentin Grafton, Director of the Centre for Water Economics, Environment and Policy, Crawford School of Public Policy, Australian National University and Tom Kompas, Visiting Professor, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Coronavirus modelling shows the government is getting the balance right – if our aim is to flatten the curve

Tony Blakely, University of Melbourne

Australian government recently endorsed stricter social distancing measures to tackle the spread of the coronavirus, requiring four square metres of space per person in an enclosed room. This came only two days after it limited non-essential indoor gatherings to no more than 100 people. The situation is changing quickly, and the prime minister announced the national cabinet will meet to consider further restrictions.

With the number of cases around the world increasing rapidly and countries taking different measures, there is considerable concern and debate about if and when to start more drastic social or physical distancing measures.

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What is the right social distancing policy for Australia if we are flattening the curve?

Should we close schools and workplaces? And should this happen now? My analysis and modelling of the situation suggests this is unlikely to be our best strategy — just yet. If our endgame is to flatten the curve, a steadily increasing amount of social distancing over the coming weeks is the best way to go.

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This is the approach the government appears to be taking. To assist people to understand what is happening, and will happen in the future, I have assembled a model of how the COVID-19 epidemic will play out based on four different implementations of social distancing measures. It’s not perfect – but the patterns it produces are useful and likely close to the truth (we will not really know the truth for months).

Four different social distancing policy scenarios

The model simulates different levels of social distancing by varying the reproductive rate (R0) of the virus. The R0 value refers to the number of people an infected person will transmit COVID-19 to if everyone they contact is susceptible.

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The model assumes the number of cases was doubling every four days in mid-March, and allows for a diminishing pool of susceptible people as the epidemic progresses.

I have not specifically modelled the elderly and those with chronic diseases. For these people, social isolation now is warranted given their higher risk of death if infected.

The result is unpleasant – disastrous even. New infections per day in Australia would peak at over 500,000 (and from estimates deduced from Imperial College model parameters, a third of which would be symptomatic), absolutely swamping health services.

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The second scenario approximates the current government policy of steadily increasing social distancing measures, simulated by reducing R0 from 2.5 to 1.2 by the end of May. The measures are then slowly relaxed, meaning R0 slowly increases. (There are an infinite number of options of when and by how much to relax social distancing). In this scenario the daily new infections peak at about 125,000 in late May, with the peak of symptomatic cases lagged up to a week – still not pleasant, but considerably better.

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The third scenario models the case where social isolation measures are rolled out faster to get the R0 down to 1.2 by the end of April. This scenario pushes the peak of the epidemic to June, but does not significantly reduce the daily load of new cases compared to the previous model.

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The final scenario models what will happen if we implement more drastic social distancing measures now, reducing R0 to 1.2 by the end of March. This approach reduces the peak number of new infections per day to about 100,000, which the model predicts will happen in mid- to late-June.

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When compared to the scenario of steadily increasing social distancing measures, the last two strategies have the benefit of pushing peak caseloads in hospitals to early or late June. But the downside – and this is important – is that social distancing policies (with all their economic consequences and social disruption) will have to be in place for longer to achieve the same total number of cases at year’s end (the total cumulative number of cases by year’s end will depend on when and by how much social distancing is eased).

What happens to forecasts if the virus was actually spreading faster in mid-March?

The above is what I think are reasonable modelling scenarios. However, what worries me is the possibility the cumulative number of infections is doubling every three days, not every four days as in the above models.

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Under this assumption, implementing stricter social distancing measures faster (as per model 03 above) seems a prudent way to go. The health systems would not cope with the daily infection peak of 300,000 if we took till the end of May to fully implement policies (as per model 02 above).

Assuming the government is taking the “flattening the curve” strategy, this is presumably why it is not rushing to close schools and workplaces just yet. But such closures may soon be likely.The Conversation

Tony Blakely, Professor of Epidemiology, University of Melbourne

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The first economic modelling of coronavirus scenarios is grim for Australia, the world

Warwick McKibbin, Crawford School of Public Policy, Australian National University and Roshen Fernando, Australian National University

The COVID-19 coronavirus is spreading across the world. Initially the epicenter was China, with reported cases either in China or in travellers from China. There are now at least four further epicenters: Iran, Italy, Japan and South Korea.

Although the World Health Organisation believes the number of cases in China has peaked and should fall, case reports are climbing from countries previously thought to be resilient due to stronger medical standards and practices.

In a strongly connected and integrated world, the impacts of the disease will go beyond mortality (deaths) and morbidity (people incapacitated or caring for the incapacitated and unable to work).

Companies across the world, irrespective of size, depend on inputs from China – much more so than during the 2002-04 China-centred Severe Acute Respiratory Syndrome (SARS) pandemic.

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In 2003 China accounted for less than one twentieth of world trade. It now accounts for one seventh, making it the world’s biggest importer and an integral part of most global production chains.

Just as important to the world economy, panic is distorting spending. Global stock markets have plunged.

Fear is as important as trade

Entire cities in China have closed and travel restrictions have been placed on people entering from infected countries.

The fear of an unknown deadly virus is similar in its psychological effects to the reaction to terrorism threats and produces a high level of stress, often with longer-term consequences.

A large number of people feel at-risk at the onset of a pandemic, even if their actual risk of dying is low.

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The International Monetary Fund expects COVID-19 to knock 0.4 points off China’s economic growth target of 5.6% and 0.1 points off global growth, an assessment it will continue to update.

On Monday the Organisation for Economic Co-operation and Development sliced 0.8 points off its forecast for China’s growth and 0.5 points off its forecast for Australian growth.

As part of a large research project in the Centre for Excellence in Population Ageing Research (CEPAR) at the Australian National University, we have applied experience gained from evaluating the impact of SARS for the World Health Organisation in 2003 and 2006 to seven scenarios for COVID-19:

The scenarios vary the attack rate (the proportion of the total population contracting the virus), the mortality rate (the proportion of total
population who dies), whether epidemic is a one-off (essentially temporary) or recurrs each year (essentially permanent), and whether it spreads globally or is largely confined to China.

Australian faces a significant hit to GDP

We find that in the four scenarios where the epidemic goes global, Australia’s GDP which in the 12 months to December grew just 1.7%, would suffer a hit in the first year of between 2% and 7.9%, most likely sending GDP backwards (a recession).

In all countries the sharp hit to growth would be followed by a gradual recovery.

The results are very sensitive to the assumptions used, including government responses in each country.

In the short term, central banks and treasuries will need to make sure disrupted economies continue to function.

Australia: percentage change in real GDP

Percentage deviation from business as usual.
Source: McKibbin and Fernando, March 2020

While cutting interest rates is an option, the shock will require a mix of monetary, fiscal and health policy responses. Quarantining affected people and reducing large scale social interaction would be an effective response.

Wide dissemination of good hygiene practices can be a low cost and highly effective response that can also reduce the extent of contagion and keep down the social and economic cost.

The longer-term responses are even more important.

Many governments have been reluctant to invest sufficiently in their health care systems, especially in public systems in less developed countries where many infectious diseases are likely to originate.

Investments in overseas public health matter

The idea that any country can be an island in an integrated global economy is being proved wrong.

Poverty kills people. However, the outbreak of COVID-19 shows that diseases, potentially generated in poor countries due to overcrowding, poor public health and interaction with wild animals, can kill people of any socioeconomic group in any country.

There needs to be vastly more investment in public health and development in the richest but also, and especially, in the poorest countries.

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Our study suggests big economic costs in countries such as Australia can be avoided through global cooperative investment in public health in all countries.

We have known this for decades, yet politicians continue to ignore the scientific and economic evidence about the role of global public health in improving the quality of life and driving economic growth for us all.The Conversation

Warwick McKibbin, Chair in Public Policy, ANU Centre for Applied Macroeconomic Analysis (CAMA), Crawford School of Public Policy, Australian National University and Roshen Fernando, PhD Student in Economic Policy, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.