Qaecologist Posts

New website!

Sun, 13 Aug 2023 03:23:55 +0000

This website has fallen out of date. Head to https://ms.unimelb.edu.au/research/groups/mathematical-ecology-epidemiology-and-biosecurity

Environmental Science and ARC Future Fellowships

Tue, 13 Sep 2022 21:16:24 +0000

Environmental Science is one if Australia’s leading research areas. It is a field with Australia’s highest representation of highly-cited authors. And for all the foibles of the Excellence in Research for Australia (ERA) ratings, it is the field with the most universities “well above world standard” (ERA 2018).

Australia’s environment is clearly important – to things like our national identity, our health and wellbeing, and some of our main industries (e.g., agriculture, tourism). The state of Australia’s environment, however, is poor.

“Environmental change” is also recognised as a national “Science and Research Priority” by the Australian government.

So given that background, you might expect that the Australian Research Council (ARC) would prioritize funding for Environmental Science.

Of the 100 ARC Future Fellowships recently awarded, only one was in the area of Environmental Science (congratulations to Vanessa Adams – excellent stuff). This is no anomaly. In the last 5 years, out of more than 500 Future Fellowships awarded, only 12 have been in the area of Environmental Science.

Plot the number of Future Fellowships versus the number of universities judged to be “well-above world standard” and there are some clear outliers.

The number of ARC Future Fellowships awarded in different disciplines between 2018 and 2022 versus the number of Australian universities judged to be well above world (ERA 2018).

On the positive side, Engineering seems to be getting a very large share of the Fellowships. The reason for this is unclear, but it seems to be an ARC priority.

Biological Sciences also might seem disproportionately over-represented. Countering that view, Medical and Health Sciences is very much lower. The latter is expected since the ARC does not fund “medical research”, so many of Australia’s large population of top medical researchers are likely applying for (and being awarded) Future Fellowships in areas of Biological Sciences.

Mathematical Sciences and Agricultural and Veterinary Sciences are also somewhat under-represented.

But the biggest outlier? Environmental Sciences. Twenty one Australian universities were rated as being well above world standard in 2018 by the ARC, yet this field has only received 12 Future Fellowships in the 5 years since then. I have no idea why, but it seems very odd.

Big news!

Fri, 02 Sep 2022 16:27:41 +0000

Since February 2022, I am working in Spain, at the Pyrenean Institute of Ecology (IPE-CSIC). I will update my website soon.

How I’ll watch the 2022 Australian Federal election

Wed, 18 May 2022 07:37:07 +0000

With a few days to go until ballots begin to be counted in the 2022 Australian federal election, I thought I’d give an overview of some of the things I’ll be tracking on Saturday once counting starts. In addition to watching Antony Green on the ABC and keeping an eye on Kevin Bonham’s twitter feed and blog, I’ll be watching the AEC website and its booth-by-booth data. Here’s why…

My interest in analysing election results was born in the contest between Sophie Mirabella and Cathy McGowan in Indi in 2013. Cathy McGowan would have been described as a “teal” candidate in today’s terms, even though her campaign brand was orange. McGowan presented as an economically conservative yet socially progressive candidate in a similar way to today’s teal candidates.

Here are the things to keep an eye on:

The overall margin that the sitting member had at the last election based on the two-candidate-preferred vote.
First preferences, particularly swings on a booth-by-booth basis.
Flows of preferences, leading to two-candidate preferred votes, and the swing of that.

The current Australian Treasurer’s seat of Kooyong is a good example. Let’s look at that seat, where Josh Frydenberg is facing a challenge from the teal candidate Monique Ryan.

The 2019 result

Frydenberg won that seat in 2019 with 55.7% of the vote on a two-candidate-preferred basis (2CP). He beat Julian Burnside, a Greens candidate who got 45.3% of the 2CP vote. Therefore, Frydenberg cannot afford to lose 6% of the 2CP vote if he is to retain the seat in 2022.

There are approximately 100,000 voters in Kooyong, so a 6% swing represents about 6000 votes.

In 2019, Frydenberg won 49.4% of first preferences, so he gained a little over 6% on top of his first preference votes once distributing preferences allocated by primary voters of other candidates. In contrast, Burnside won only 21.2% of first preferences, so he more than doubled his vote via the flow of preferences. Preferences matter.

It is interesting to see where the preferences came from. Frydenberg primarily got preferences from voters whose first preferences were Jana Stewart (ALP candidate; 2770 votes), Oliver Yates (an independent; 1793 votes), and Steven D’Elia (United Australia Party (936 votes). He got a little over 700 votes from three other candidates (two independents and an Animal Justice Party candidate).

You might be surprised that Frydenberg got so many preferences from ALP voters. The reason was the number of ALP voters. Jana Stewart won 16666 first-preference votes, and Frydenberg got 16.6% of these via preferences. So he got a relatively small proportion of relatively large number of votes.

Similarly, Oliver Yates netted a healthy number of first-preference votes (8890), but Frydenberg won only a small fraction of those (20.1%). Oliver Yates ran on a platform that very much reflects the current teal mood (a revolt against the direction of the Liberal Party, and demanding stronger climate action).

In contrast, preferences from primary voters for the UAP candidate flowed strongly to Frydenberg (79%).

If we add the first-preferences for the Greens, ALP, and Oliver Yates in 2019, then we have quite a strong block of potential votes for Monique Ryan in 2022 (47%). Clearly, Monique Ryan can’t be sure of winning all those first preference votes from 2019 (either as first preferences or on preferences ahead of Frydenberg). But given she has a chance of also taking some first-preference votes directly from Frydenberg, then Ryan seems clearly in the running.

First preferences in 2022

So I initially will be looking at Frydenberg’s first preference votes in 2022. The 2CP votes are what really matters, but first preference votes are normally reported before the 2CP counts. In 2019, Frydenberg’s first preference vote fell 8% but his 2CP fell by only 6%. But a drop in his first preference vote of around 6-8% in 2022 will worry him.

One key when looking at the swing in votes is to realise that the vote differs between booths. In particularly, pre-poll votes and postal votes have tended to favour Frydenberg in the past. The votes counted on Saturday evening will tend to be the votes from regular booths. So Frydenberg might suffer an apparent swing against him on Saturday evening, but some of that swing will likely be recovered in the following days as more pre-poll and postal votes are counted.

But any swing against him on regular votes will tend to be replicated across booths. You can see that in the 2019 result.

Swing to Frydenberg in the 2019 compared to 2016 for each booth versus the total votes per booth. The negative values are swings against Frydenberg. The only seat with a positive swing was a small “hospital” booth (a team that collects ballots from hospitals). You can see that the swings for the non-ordinary votes (orange circles, including pre-poll, absent and postal votes) were similar to the average overall swing. Once we have swings for 10-15 larger booths, we’ll have a good idea about where the seat is heading.

So, I will be looking at the swings of individual booths. If those swings on first preferences average greater than 6%, then the result becomes interesting. I would guess if the average swing on first preferences is greater than 10% then Frydenberg will lose his seat, unless the UAP or some other right-leaning candidate is picking up a large fraction of those first-preference votes.

Preference flows

If the AEC starts publishing 2CP counts for individual booths along with the first preference counts, then it is possible to estimate the preference flows via linear regression. If those data become available, here is the approach I will use. The y-variable would be the number of 2CP that Frydenberg gains in each booth. The x-variables will be the first preference votes to each candidate. The regression coefficients will estimate the proportion of voters for each candidate who preference Frydenberg above Ryan. The coefficient for Frydenberg will be set to 1 (we know that his voters have all preferenced Frydenberg above Ryan). For the others, the coefficients will be restricted to between 0 and 1, and I will use the booth-by-booth data to estimate the coefficients (0 means none of those voters preferenced Frydenberg; 1 means they all preferenced Frydenberg). That will allow me to estimate the 2CP vote in booths that have reported first preference votes but not 2CP votes.

So there you have it. First, I will look at the swing needed to unseat the sitting member. I’ll monitor the swing of first preferences and then project how that swing will translate to the 2CP vote (either guessing based on previous years or a linear model based on booth data). That will allow me to predict the final 2CP vote for the whole electorate once the first preference votes become available. It would be possible to do a similar analysis for all the seats that are in play. This analysis relies on the AEC reporting booth-by-booth results on election night. Hopefully they will!

Models – what are they good for?

Fri, 24 Sep 2021 04:18:28 +0000

Models are everywhere at the moment! Everyone in Australia will have heard of the Doherty model, which has helped set Australia’s path out of the pandemic. Modelling from the Burnett Institute is helping to steer both New South Wales and Victoria out of their lockdowns.

But what are scientific models, and why are they useful? Answering these questions is not easy. Sure, there are various answers to the questions. But the answers are not always easy to communicate, and secondly, the answers depend on the purpose of the models. While models are used for a range of reasons including synthesis, explanation, estimation, experimental design, etc., I will focus here on models that are used for prediction.

I teach Environmental Modelling to graduate students at The University of Melbourne. The subject introduces students to a wide range of models used in environmental management – the topics covered include noise propagation, hydrology, climate, species distributions, and population dynamics. The population dynamics ones are particularly relevant when thinking about epidemiological models – epi models are almost identical mathematically to predator-prey models.

I have a couple of major aims when teaching this subject. Firstly, I want students to become less intimidated by models. Secondly, I want students to better understand the steps of modelling so that they are better placed to use and critique models.

I aim for my students to be less intimidated by models. Some models might be hard to understand, but in the end they are something that people created. So it is possible to understand them with sufficient effort (image from despair.com).

One of the most persistent, yet naïve, critiques of models is that they are not sufficiently realistic. Let me say up front – models are meant to be imperfect descriptions of reality. That is, arguably, the whole point of using models instead of reality. The key, to paraphrase Einstein, is to make the models as simple as possible, but no simpler. That is easy to say, but it is perhaps the most challenging thing to deliver.
So why do we want models to be imperfect? Because we need a simplification to make sense of complicated systems. Essentially, models are useful when reality is complicated. Models help to describe the system we are studying in simpler terms so that we can make predictions within a reasonable time scale, and better understand the key processes.

Models are meant to be imperfect descriptions of reality – that is their entire point. Models encapsulate all the good, bad and ugly assumptions that are thought to be true. And then they predict the logical consequence of those assumptions.

So why should we trust a model’s predictions? Well, should we trust them? Lots of lives and livelihoods currently depend on the predictions of epidemiological models. Perhaps in answering that question of trust, we can first consider what the predictions represent. I think the simplest way to think about them is that model predictions are the logical consequences of a set of assumptions. The model encapsulates a set of assumptions, and the model then simply tells us the consequence of those assumptions.

For example, build a model of COVID transmission among people that describes: the rate of transmission under different scenarios of public health orders; the effect and uptake of vaccines; the rate at which people enter hospital and/or die; how vaccination influences those rates; the effectiveness of contract tracing to identify cases and reduce transmission; etc. Each of those components will have their own details. It gets complicated quickly. And that is without considering every nuance of human behaviour. But once the model is built, we can then ask, “How many deaths and hospitalisations should we expect as a logical consequence of these assumptions?” The model provides a precise answer to that question for a given set of assumptions.

We can then ask how sensitive the predictions are to changes in the assumptions. Change one or more assumptions, and we get a different answer. This sensitivity analysis is valuable, because it tells us where we might want to focus policy interventions, and also where we might want to get better data.
What would be the alternative to using models? An obvious alternative is to let people make their own judgements with the same information as used in the model. That would certainly be simpler. The drawbacks of this approach are many. The logic of such subjective decisions is opaque. You might counter that models are opaque. But how about you try to get your mind around the thinking of a decision maker where their assumptions are not spelled out in black and white?

Subjective decisions are also prone to a wide range of biases. And I’m not just talking the biases that might arise from the influence of lobby groups. Even well-intentioned decision makers are prone to the effects of biases.

Perhaps the biggest benefit of using models to support decisions is that their predictions are transparent. If the predictions are wrong, it can tell us that there were one or more errors in the set of assumptions that underpinned the model. Perhaps the model omitted an important detail. Or one or more of the model’s parameters were astray. Regardless, errors in predictions challenge the assumptions that underpinned the model and allow us to refine our understanding.

So, what are models good for? They allow us to predict the logical consequences of what we believe to be true, and test the degree to which the outcomes depend on those assumptions. In short, it seems wise to use a model to test a policy with far-reaching implications for lives and livelihoods before taking that policy into the real world.