In 1959, Charles Lindblom wrote The Science of “Muddling Through”, advocating an incremental approach to public policy and management. “Muddling through” does not work for pandemics. The science of pandemics is dominated by epidemiology, not behavioral science. The delay between policy decisions (or indecisions) and the resultant high UK death rate needs tracing back from the prime minister and his advisers to Cobra, the chief scientific adviser, and the Scientific Advisory Group for Emergencies. However, this is one form of contact tracing that can be delayed until the inevitable public inquiry.
Dr Duncan Robertson Fellow in Management Studies, St Catherine’s College, University of Oxford, UK
The real problem with Coronavirus Covid-19 is that when the health service becomes overloaded, the death rate goes up significantly. So, it is imperative that we keep the number of cases at any one time below or as near as possible to NHS capacity.
The Government’s model relies on shifting the peak. I am not clear that we are doing this fast enough. We are an outlier compared to other countries in our social distancing policy response.
There is a tension between epidemiologists – those who study how diseases spread (a very established science started around the time of the Spanish Flu 100 years ago), and behavioural scientists who study how people behave and react.
Epidemiologists have models validated against past pandemics. Behavioral scientists do not.
There are economic and social costs of social distancing. But if you delay social distancing too much, there are potentially very real human costs in increased mortality. Doctors will need to make very difficult moral decisions on who to treat and who not to treat.
I do not understand why, if the intention is to create herd immunity, why we are not isolating our vulnerable population, especially those in care homes.
Behavioral insights are great when you are trying to get people to pay their tax bills on time. And if people don’t, it doesn’t really matter. With a pandemic, if you get the timing wrong, more people die unnecessarily. Then you look back from your computer and say, yes, we got that behavioral model wrong while doctors and nurses on the front line are exposed to extra cases that could put their own lives at risk.
Here’s a video to show what we should be trying to do:
The United Kingdom is at a crossroads, an ideological battle between natural science and behavioral science. Let’s hope for all our sakes we get this one right.
Boris Johnson, the UK Prime Minister, is facing a dilemma. When do we go from the so-called containment phase for controlling Covid-19 Coronavirus, to the delay phase.
In the medical / natural science corner, is the Chief Medical Officer, Professor Chris Whitty, who has presented himself calmly, reassuringly, as completely on top of his brief. He is a physician and an epidemiologist (as well as a lawyer, and an MBA). His evidence at the newly formed House of Commons Coronavirus Committee was calm, frank, precise. He is exactly the sort of advisor that any government would be proud to have. Flatten the peak. Delay the virus spread. Keep the height of the peak low. Save lives.
In the behavioral science corner is, well, I am not sure who. Maybe it’s the Chief Scientific Advisor, who highlighted the need to take account of behavioral science. Yes, please do. It’s a wicked problem, and please include more complex social modelling.
But what we are now seeing is what the Director General of the World Health Organization (up until now also criticised for its seemingly political response to the issue) could be referring to as ‘alarming levels of inaction’.
I do hope, however, that Boris Johnson is being guided by the science, both behavioral and epidemiological, and not by advisors who profess to be superforecasters. You don’t have to be a superforecaster to forecast that if we get this wrong, many will die unnecessarily.
The 2011 film Contagion, starring the spectacularly ill-fated Gwyneth Paltrow, is a dramatization of a viral pandemic starting in pretty analagous circumstances to the current Wuhan Coronavirus (2019-nCoV) outbreak. It’s a good film, and is a great introduction to the work of Centers for Disease Control (CDCs) that monitor the spread – the epidemiology – of the disease. There are two scenes where R-nought, or R0, are described:
Despite the blogger character in the clip describing the spread, using a R0 of 2, as being a problem you can do on a napkin, it takes a little more thinking about. He also seems a bit confused about R0, talking about growth from 2 to 4 to 16, to 256, to 65,536 each day. That’s not what R0 is – it is not a rate, and actually if the rate was 2, this would mean 2 to 4 to 8 to 16 to 32 etc., each time doubling the number. It is possible that he is thinking that there are two generations each per day, but that’s not whatR0 is.
So, on to the professionals:
The CDC epidemiologist in the clip is more on point (despite having sloppy notation with no subscripts). This is better – it shows the reproduction number for the infection – note again, this is not a rate – no time dimension is involved – it basically shows the number of cases on average each case generates.
This population modelling – so called SIR (Susceptible, Infected, Recovered) system dynamics modelling – is just one of several approaches that can be used to model contagion across a population. My recent paper ‘Spatial Transmission Models: A Taxonomy and Framework’ sets out a review of what they are and the advantages and disadvantages of each. In brief, we can model the population numbers, the individual agents that carry the virus, the network of contacts between infected individuals, or the regions or cells in which individuals are located (city districts, for example). The paper is available to read by clicking on the link here.
members of Congregation. I look around this room and see privilege. Every one of us here in the Sheldonian
Theatre is privileged; every member of Congregation reading the Gazette is privileged. We are privileged not by our past but by our
present: we all have the power to share in the democratic self-governance of
the institution that is the Collegiate University of Oxford.
democratic self-governance is hard. It
is time-consuming and troublesome, and is most easily left to specialists. Specialists with a track record of delivering
strategic plans at high speed.
Vice Chancellor warned us of the dangers of high speed in her 2017 Oration, and
I quote: Over 2,000 years ago Tacitus
pointed out that ‘Truth is confirmed by inspection and delay; falsehood by
haste and uncertainty’.
is tempting to react quickly to short term opportunities in order to gain transient
rewards, but this is, as my strategic management colleagues will confirm, often
at the expense of more attractive opportunities foregone. We must, at the very least, be able to give ad hoc proposals the service of being
fully inspected. The proposal to establish
a new Society – or is it a College? – is a significant one, particularly when
it is to have a distinctive culture as was the case with Templeton College
reason that an ‘education priority’ within the Strategic Plan has abruptly
become a press release announcing Parks College, without the knowledge of
Congregation, is that such proposals are now increasingly made without such scrutiny. While the Strategic Plan was put to
Congregation for approval, the Implementation Plan referred to within the
Strategic Plan was not. This ‘Plan
within a Plan’ is administered by Programme Boards whose agenda and minutes are
secret. In short, Congregation does not
know what is going on, and its ability to give informed consent is subverted.
of the strengths of Oxford that sets it apart from its ‘competitors’ is its
self-governance. This has allowed the
University to evolve and adapt to a changing environment, and mercifully not be
suffocated by the latest management fads and fashions. It is bewildering that ‘Senior Managers’ do
not appear to recognize the capabilities available to them within Congregation,
preferring to operate in a more comfortable ‘command and control’, top-down
fashion. If strategy is imposed, we as a
University lose the ability to adapt and to take advantage of opportunities
that may emerge – opportunities that may not be visible from the board room but
are visible from the diversity of perspectives that each one of us holds as a
unique member of Congregation.
combined organizational capabilities of Congregation – all members of
Congregation, experts in their own fields whatever they may be – are truly awe-inspiring. It is not always easy to find consensus, but
that does not mean that this University should give up and follow the lowest
common denominator of managerial hubris.
must be allowed to review and guide the Legislative Proposal to create Parks
College prior to giving its approval. The Strategic Plan spoke of creating a
new College by 2023, not a new Society in 2019.
Nolan Committee on Standards in Public Life was established 25 years ago. The principles of openness and accountability
which it set out are as relevant now as they have ever been. I urge you to vote against the Legislative
proposal while we still have the right to exercise that privilege.
Wednesday’s indicative votes in the House of Commons produced no definitive answer of the way forward. By using social network analysis showing the size of each voting bloc and ‘Hamming distances’ (ironically usually used for error correction), we can map how close MPs are to each other, giving an indication of how a coalition could be made if each block of MPs flipped their vote in order to form a Parliamentary consensus.
Brexit is currently turning out to a failed experiment in direct democracy, something I pointed out nearly three years ago.
However, with the House of Commons opening up data, it does allow us a rare insight into the goings on of the population and the MPs that are our representatives.
One interesting data source is released by the UK Parliament showing the voting record of every MP for every ‘division’ (vote). One particularly interesting vote was that done on Wednesday 27 March, where MPs were able to cast their votes for 8 motions:
By making a so-called bipartite network, we can map individual MPs to the votes for which they voted yes. This results in a map of MPs shown below.
While this is interesting, it doesn’t really show the distance between MPs’ voting intentions.
We can redraw the map by using the distance between MPs according to the votes they cast. We can do this by constructing a binary string of their votes. For simplicity, we count only the ‘aye’ or yes votes, and ignore abstentions and nos.
For instance, if an MP voted yes, no, no, yes, no, yes, yes, no, they would be given a string of 10010110, whereas if another MP voted no, no, yes, no, yes, yes, no, yes, they would be given a string of 00101101. So, what is the ‘distance’ between 10010110 and 00101101? For this, we use the Hamming distance – count the number of locations where there is a difference. In this case, the Hamming distance between the MPs is 6.
By constructing a graph of Hamming distances of 1, we can construct neighbours of individual groups of MPs. This is shown in the graph below.
I have listed the votes in the following order:
Common Market 2.0 Confirmatory Public Vote Contingent Preferential Arrangement Customs union EFTA and EEA Labour’s Alternative Plan No Deal Revocation to Avoid No Deal
However, this isn’t very useful, as it doesn’t show the type of MP that voted for each of these. So we can relabel the nodes with a representative MP from that bloc.
From this, you can work out the number of intermediate MPs to get to any other MPs. What is quite interesting is that every MP was just one vote away from another – no-one is isolated. Which, in some little way, gives us hope.
We can then weight the edges to show the possible coalition that could be made if these blocs were to join. And here it is:
The size of each circle represents the number of MPs that voted the same way as the representative MP named on the circle, and the thickness of the links shows how many MPs would join together if one vote were flipped.
If the linked blocs join up, you can see how there could be a path to a Parliamentary majority – for the blocs to join, it would mean switching one vote from ‘aye’ to ‘no’ or vice versa.
For completeness, the list of MPs and their associated binary string is linked here. You can find the MPs that are part of each bloc by searching for the MP name in the label on the network graph. The Hamming distance between each and every MP is available on request. I leave it to the reader to construct an affinity matrix – or what I would call currently describe as a ‘Matrix of Hate’ for each MP pair.
Right now, Houston is going through one of the most severe storms ever to hit the USA. The main conversation on today’s news was whether the Mayor (who has authority to do such things) should have evacuated the City prior to the arrival of Hurricane Harvey.
For a start, NOAA did not forecast a direct hit on the City. But it was forecast that potentially devastating rains were on the way.
Houston has been here before, of course, in 2005 when the then Mayor did order that the city be evacuated. And around 100 died, as a result of the gridlock and heat.
But let’s think about what an uncontrolled evacuation of Houston would mean.
While there is, of course, a Houston evacuation plan, assuming you want to avoid the Gulf of Mexico, the main routes are via the north and west: I69 to the north-east, I45 to the north, US Route 290 to the north-west, and I10 to the west.
Now let’s consider the capacity of these roads. The capacity of roads in the US is given by the Department of Transportation’s Highway Capacity Manual. While there is a whole science devoted to calculating freeway flow measurements, you need to take into account not only the capacity of the road (the number of cars), but also their speed. Combining these gives us a flow rate, i.e. the number of cars that will pass a point in a particular length of time. We can look at the academic literature to see what this is. Dixit and Wolshon (2014) have a nice study where they looked at maximum evacuation flow rates. Their Table 2 shows the empirical data, but it’s around 1,000 vehicles per hour per lane. Assuming the Houston metro system evacuation routes of the north and west are around 4 x 4 lanes. Give a factor of 1.5 for contraflows, and you have around 25 lanes. So that’s 25 x 1000 = 25,000 vehicles per hour. And let’s assume an occupancy of 4 passengers per vehicle (i.e. most would evacuate by car). So that’s 100,000 passengers per hour.
The problem with Houston is that it’s the USA’s fourth largest city. And that means it’s big. It (Greater Houston) has a population of 6.5 million. So that means 6.5 million / 100,000 = 65 hours. Non stop, day and night. Without accidents. A very bold move for a hurricane that was not due to hit directly.
I am looking for high quality, numerate, candidates to fill these exciting PhD studentships with me as a (co-) supervisor at Loughborough’s School of Business and Economics. Please note that this post has been updated with new links (in blue, below).
The first is modelling dynamic responses to dynamic threats; the second is using analytics in traditional industries. Please see the links below for further details and how to apply. Note that for further information, you will need to click on the blue links below.
One of the most challenging issues for policy makers dealing with bio-security threats is their dynamic nature: diseases may spread quickly and deadly among vulnerable populations and pandemics may cause many casualties.
Finding the appropriate response to threats is a major challenge. Whilst models exist for understanding of the dynamics of the threats themselves, responses can be largely ad-hoc or ‘firefighting’. The aim of this research is to produce robust responses for dynamic threats.
The research will build up as follows, from low to high complexity: static responses to static threats; static responses to dynamic threats; dynamic responses to static threats; and dynamic responses to dynamic threats.
We will use a variety of methods to define the best response: cellular automata, network analysis, spatial modelling, agent-based modelling, and the generation of dynamic fitness landscapes.
This PhD studentship is most suitable for candidates with a background in a quantitative discipline such as management science, operations research, engineering, physics and other natural sciences.
The rise of business analytics has given rise to enormous opportunities within the private sector, but these benefits have yet to be fully realized in public services and regulated industries such as energy, water, and transportation networks. Conversely, governments are mandating collection of data by installing smart metering devices. This gives rise to the need for innovative ways of thinking in industries that are still largely based on traditional economic thinking involving conventional assumptions on optimization and behaviour.
As an example, the energy sector is characterised by strongly defined market structures with incumbents and an ultimate need for energy network security, which not only prevents the quick adoption of technical changes but also translates into regulatory outcomes, such as price caps.
This exciting PhD opportunity will integrate theoretical and empirical approaches and spans two strengths of Loughborough’s School of Business and Economics: microeconomics and particularly rigorous analysis of the determinants of productivity and performance (including cost modelling) and management science (including simulation and network analysis).
We are therefore seeking a student with a quantitative background (whether in economics, management science, engineering, physics or other natural sciences). A willingness to learn new techniques such as, cost modelling, performance measurement, agent-based modelling and network analysis is desired.