mathematical modelling – 今日吃瓜 I Championing Mathematical Sciences for Australia鈥檚 Advancement 今日吃瓜 Mon, 07 Sep 2020 04:22:36 +0000 en-US hourly 1 https://wordpress.org/?v=5.8.17 /wp-content/uploads/2015/11/cropped-今日吃瓜_icon-32x32.png mathematical modelling – 今日吃瓜 I Championing Mathematical Sciences for Australia鈥檚 Advancement 32 32 鈥楽low and steady鈥� exit from lockdown as Victorian government sets sights on 鈥楥OVID-normal鈥� Christmas /2020/09/07/slow-and-steady-exit-from-lockdown-as-victorian-government-sets-sights-on-covid-normal-christmas/ Mon, 07 Sep 2020 00:55:45 +0000 /?p=10021 鈥楽LOW AND STEADY鈥� EXIT FROM LOCKDOWN AS VICTORIAN GOVERNMENT SETS SIGHTS ON 鈥楥OVID-NORMAL鈥� CHRISTMAS – 7 September 2020

Expert epidemiology and biostatistics commentary from and 听颈苍 .

The Victorian government today announced the eagerly anticipated roadmap out of COVID-19 lockdown. It features several steps that reflect a much slower relaxing of restrictions than last time around.

While the government has provided a provisional time frame for the various steps, it is data, not dates, that will determine when restrictions are actually eased.

We applaud this strategy. The virus does not obey a timeline. Rather, we have to beat it down to a level at which easing of restrictions is safer.

Erik Anderson/AAP

What was announced?

Metropolitan Melbourne鈥檚 current stage 4 restrictions will be extended for two weeks, to September 27. But from 11:59pm on September 13, there will be a few key changes.

The nightly curfew will be shortened by one hour, and will be in place from 9pm to 5am. Also, two people or a single household can meet outdoors for two hours maximum, up from the previous one hour, for exercise or recreation.

For people living alone, and single parents with children under 18, there will be a 鈥渟ingle person bubble鈥� policy that allows them to designate one other person who can visit their home.

Regional Victoria is already faring better than Melbourne, and will have a faster timeline.

Premier Daniel Andrews wants to maximise the chance of getting to Christmas in something like stage 1, while minimising the chance of a third wave of infection that sends the state back into lockdown. This means staying in strict restrictions for longer, and easing out more gradually.

How did data influence the decision?

The Victorian government鈥檚 decision was based in part on the output of a model developed by researchers at the University of Melbourne and the University of New England. It simulates population movements in a simplified world, based on parameters that describe the spread of COVID-19 and people鈥檚 interactions with each other.

In the real world, these patterns are highly random. So the researchers ran the model 1,000 times, with thresholds for relaxing (or tightening) restrictions set at an average of 25, 10, and 5 cases per day on a fortnightly basis. The model could then report the probability, under a given set of policy settings, of having to lock Victoria down again before Christmas.

Opening up too soon is likely to cause a third wave. In simulations in which restrictions were eased once average daily cases dipped below 25 per day, there was a 62% likelihood of new lockdowns. But with restrictions retained until daily cases dropped below 5 daily cases, the lockdown likelihood was just 3%.

Viewed in that light, it is easy to see why the Andrews government opted to set strict criteria for lifting restrictions, knowing that short-term pain is better than the economic ravages of another lockdown in the long term.

What might hold Victoria back?

First, there鈥檚 the elephant in the room 鈥� the quality of Victorian contact tracing (especially in comparison to New South Wales). Living with the virus requires high-quality contact tracing. There鈥檚 no doubt contact tracing in Victoria has improved since June when our second wave started. There is therefore a real possibility that we may get the case numbers down faster, and hold off resurgences of case numbers more effectively or for longer than the modelling suggests.

Second, infection disease control in health care and aged care has not been up to scratch in Victoria (compared with, dare we say it again, New South Wales). These represent particularly dangerous settings. Older adults are much more likely to become severely ill with COVID-19, whereas health-care workers who become infected with the coronavirus risk infecting the most vulnerable and reduce health capacity when it is most needed.

And of course, health and aged care workers live in the community too, and if community restrictions are relaxed the virus will leak back out through family members and surge again. The Victorian government decided to deal with both community and health-care transmission simultaneously. We think that it is the right approach.

Is elimination still possible?

There were strong arguments for an explicit , requiring 鈥済oing hard鈥� for a six-week lockdown. Unfortunately, Victoria didn鈥檛 go hard early enough. The government waited three weeks, numbers got out of control, and then we went into stage 4. With the benefit of hindsight, it was a huge missed opportunity.

The Grattan Institute is also for an explicit elimination strategy and much longer hard lockdowns. It argues this will result in better economic outcomes in the long run. An by Australian National University researchers also supports the theory that elimination is better for both health and the economy in the long run (although this paper has not yet been peer-reviewed).

However, things have changed in the past two months. First, we are now closer to a vaccine, so in theory the long-term payoff for short-term pain will arrive sooner. Second, New Zealand (and Queensland) have taught us that elimination can be lost. Third, NSW has taught us you can live with the virus at low levels (so far). Fourth, the imminent border openings and hotspot strategy are not really consistent with the hard border controls needed to defend elimination in places that achieve it.

Andrews aptly termed the state鈥檚 strategy 鈥渁ggressive suppression鈥�. It may even achieve elimination, as the first wave effort so nearly did. We hope it does 鈥� but do not bank on it.

It鈥檚 in our hands now, both the government and citizens. With some good luck 鈥� and few would begrudge Victorians a little of that 鈥� the roadmap will pan out as planned.The Conversation

, Research Fellow, Population Interventions Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, and , Research Fellow, Population Interventions Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health,

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

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Bureau of Meteorology issues Climate Driver Update for La Ni帽a phenomenon /2020/08/18/bureau-of-meteorology-issues-climate-driver-update-for-la-nina-phenomenon/ Tue, 18 Aug 2020 08:33:04 +0000 /?p=9880 Modelling by BoM moves ENSO Outlook to La Ni帽a Alert status.

Read more at the .

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Predicting the pandemic鈥檚 psychological toll: mechanistic and statistical modelling /2020/06/01/predicting-the-pandemics-psychological-toll-mechanistic-and-statistical-modelling/ Mon, 01 Jun 2020 05:44:36 +0000 /?p=9442 Jayashri Kulkarni, Professor of Psychiatry at Monash University, on the challenges of modelling in health.

Read more in

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“A forecasting model is an opinion column with more maths” /2020/05/26/a-forecasting-model-is-an-opinion-column-with-more-maths/ Tue, 26 May 2020 02:44:05 +0000 /?p=9421 What will happen when we lose faith in modelling? Parnell Palme McGuinness in today’s (Nine Media paywall access required).

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Before epidemiologists began modelling disease, it was the job of astrologers /2020/05/20/before-epidemiologists-began-modelling-disease-it-was-the-job-of-astrologers/ Wed, 20 May 2020 01:38:16 +0000 /?p=9371 We turn to epidemiologists and infectious disease models; during the Bubonic plague people turned to astrologers.

[Read more history from UQ’s Michelle Pfeffer at ]

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Swedish model trades more disease for less economic damage /2020/05/20/swedish-model-trades-more-disease-for-less-economic-damage/ Wed, 20 May 2020 00:11:15 +0000 /?p=9368 “If we are to reach a new normal, in many ways Sweden represents a future model.”

– Nine Media paywall access required

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‘Life Beyond Coronavirus’: The expert view [podcast]; Part 2 of 6 /2020/05/04/life-beyond-coronavirus-the-expert-view-podcast-part-2-of-6/ Mon, 04 May 2020 01:10:33 +0000 /?p=9245 Professor Jodie Mcvernon and a panel experts from across the University of Melbourne explore how mathematical modelling got us to this point, the new trace-and-track app, how it works and if it has a role to play in lifting restrictions, and just how long can Government keep us at home? Hosted by Professor Shitij Kapur.

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Welcome Inge Koch: Choose Maths Executive Director /2015/08/10/choose-maths-executive-director/ Mon, 10 Aug 2015 03:27:49 +0000 http://amsi.org.au/?p=3250

Associate Professor Inge Koch聽completed an MSc at the University of Oxford, and a research MPhil at the University of London, and then worked for industry and the University of Aberdeen in the UK, and for the CSIRO in Canberra. She completed a PhD in statistics at the ANU in 1991 on theoretical problems in image analysis. Inge has joined 今日吃瓜 as Executive Director and will head the Choose Maths initiative.

We caught up with Inge to find out about her maths, her stats,聽her life and her goals for the secondment with us.

1) Why did you become a mathematician?

Mathematics was fun and a challenge all through my school years. Partly because of the widespread folklore that girls can鈥檛 do maths it was not until late in high school that I considered mathematics as a serious career option. By then I enjoyed mathematics more and took it more seriously than other subjects, and I was eager to learn more mathematics.

2) What are some areas of mathematics/statistics that you find particularly interesting?

I enjoy the interplay between theory, data analysis and solving real problems. I feel passionate about developing new statistical methods and theory in statistical learning or machine learning, dimension reduction and selection, and I am keen to apply and adapt these new methods to complex high-dimensional data in proteomics, other biotechnology applications and, more generally, in areas that deal with data with very many and typically too many variables.

3) Do you have any advice that may make more students choose maths as a future career path?

There are many branches of mathematics ranging from the purest pure, to applications in biology, medicine, sport, marketing, climate and the environment, television and finance to name just a few. Each part is important, and you need to work out which part of mathematics speaks to you, and what you like about it.

Go for the part of mathematics that you enjoy most.

Knowledge of mathematics does not have to be an end in itself, but can open doors to new areas and fascinating careers. If you are adaptable, the rigour and insight you learn in mathematics are聽transferable to other areas that require analytical and thinking skills.

When looking for a job or career, don鈥檛 just search under ‘mathematician’, there is a big world out there that needs your skills and enthusiasm; convince them that you will be a good asset to them.

4) Biggest maths/stats regret?

I wish I had realised earlier how wonderful and exciting statistics can be. It is so much more than what you learn in school or in your early university education as ‘statistics’. It integrates areas of pure mathematics, statistical ways of thinking, computing and having to find efficient and workable solutions for real and diverse data and problems.

5) Biggest maths/stats success?

Classical multivariate statistical theory does not meet the needs of big data and problems arising in machine learning or data science. In the last few decades the often-ignored multivariate Gaussian theory has become increasing relevant again 鈥� driven by the demands of complex high-dimensional data and data experts who require answers to their problems. The combined research effort of statisticians such as Peter Hall, Iain Johnstone and Steve Marron and many others has led to new frameworks for principal component analysis and discriminant analysis which are suitable for the analysis of modern data with many more variables than observations. My own research in dimension-reduction methods and my research and graduate text Analysis of Multivariate and High-Dimensional Data are both contributions to integrating the classical with the recent theory and bridging the gap between theory, data analysis and computing for our modern big-data era.

6) What do you hope to achieve as the Executive Director of the Choose Maths program?

Promoting and enhancing an environment in which girls and young women can keenly embrace mathematics in their education and career choices is what makes the Choose Maths program exciting and worthwhile for me. I hope to make progress towards this goal by breaking down barriers, and by actively promoting and working towards change at educational and government levels and in the workplace. This will help to encourage girls and young women to pursue mathematical areas and applications with the passion that I feel for mathematics.

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