The COVID-19 pandemic has further exposed the weakness of the professional cycling business model. The competition between the teams for funding from a limited number of sponsors undermines the stability of the profession. With marketing budgets under strain, more teams are likely to face difficulties, in spite of the great advertising and publicity that the sport provides. Douglas Ryder is fighting an uphill struggle trying to keep his team alive after the withdrawal of NTT as a lead sponsor. One aspect of stability is financial, but another measure is the level of transfers between teams.
The composition of some teams is more stable than others. This is illustrated by analysing the history of riders’ careers, which is available on ProCyclingStats. The following chart is a network of the transfers between teams in the last year, where the yellow nodes are 2020 teams and the purple ones are 2019. The width of the edges indicates how many riders transferred between the teams, with the thick green lines representing the bulk of the riders who stuck with the same team. The blue labels give the initials of the official name of each team, such as M-S (Mitchelton-Scott), MT (Movistar Team), T-S (Trek-Segafredo) and TS (Team Sunweb). Riders who switched teams are labelled in red.
Although there is a Dutch/German grouping on the lower right, the main structure is from the outside towards the centre of the network.
The spikes around the end of the chart show riders like Geoffrey Soupe or Rubén Fernández, who stepped down to smaller non World Tour teams like Team Total Direct Energie (TTDE), Nippo Delko One Provence (NNDP), Euskaltel-Euskadi (E-E), Androni Giocattoli-Sidermec (AG-S ) or U-XPCT (Uno-X Pro Cycling Team).
The two World Tour outliers were Mitchelton-Scott (M-S) and Groupama FDJ (GF), who retained virtually all their riders from 2019. Moving closer in, a group of teams lies around the edge of the central mass, where a few transfers occurred. Moving anti-clockwise we see CCC Team (CT), Astana Pro Team (APT), Trek-Segafredo (T-S), AG2R Le Mondial (ALM), Circus-Wanty Gobert (C-WG), Team Jumbo Visma (TJV), Bora-Hansgrohe (B-H) and EF Pro Cycling (EPC).
Deeper in the mêlée, Ineos (TI_19/IG_20), Deceuninck – Quick Step (D-QS), UAE-Team Emirates (U-TE), Lotto Soudal (LS), Bahrain – McLaren (B-H) and Movistar Team(MT) exchanged a number of riders.
Right in the centre Israel Start-Up Nation (IS-UN) grabbed a whole lot of riders, including 7 from Team Arkéa Samsic (TAS). Meanwhile likes of Victor Campenaerts and Domenico Pozzovivo are probably regretting joining NTT Pro Cycling (TDD_19/NPC_20).
A few of the top riders have contracts for next year showing up on ProCyclingStats. So far 2020/2021 looks like the network below. Many riders are renewing with their existing teams, indicated by the broad green lines. But some big names are changing teams, including Chris Froome, Richie Porte, Laurens De Plus, Sam Oomen, Romain Bardet and Wilco Keldeman, Bob Jungels and Lilian Calmejane.
What about networks of riders?
My original thought when starting this analysis was that over their careers, certain riders must have been team mates with most of the riders in today’s peloton, so who is the most connected? Unfortunately this turned out to be ridiculously complicated, as shown in the image below, where nodes are riders with links if they were ever teammates and the colours represent the current teams. The highest ranked rider in each team is shown in red.
It is hard to make much sense of this, other than to note that those with shorter careers in the same team are near the edge and that Philippe Gilbert is close to the centre. Out of interest, the rider around 9 o’clock linking Bora and Jumbo Visma is Christoph Pfingsten, who moved this year. At least we can conclude that professional cyclists are well-connected.
Which Lord of the Rings characters do they look like? Ask an AI.
After building an app that uses deep learning to recognise Lord of the Rings characters, I had a bit of fun feeding in pictures of professional cyclists. This blog explains how the app works. If you just want to try it out yourself, you can find it here, but note that may need to be fairly patient, because it can take up to 5 minutes to fire up for the first time… it does start eventually.
Identifying wizards, hobbits and elves
The code that performs this task was based on the latest version of the excellent fast.ai course Practical Deep Learning for Coders. If you have done bit of programming in Python, you can build something like this yourself after just a few lessons.
The course sets out to defy some myths about deep learning. You don’t need to have a PhD in computer science – the fastai library is brilliantly designed and easy to use. Python is the language of choice for much of data science and the course runs in Jupyter notebooks.
You don’t need petabytes of data – I used fewer than 150 sample images of each character, downloaded using the Bing Image Search API. It is also straightforward to download publicly available neural networks within the fastai framework. These have been pre-trained to recognise a broad range of objects. Then it is relatively quick to fine-tune the parameters to achieve a specific task, such as recognising about 20 different Tolkien characters.
You don’t need expensive resources to build your models – I trained my neural network in just a few minutes, using a free GPU available on Google’s Colaboratory platform. After transferring the essential files to a github repository, I deployed the app at no cost, using Binder.
Thanks to the guidance provided by fastai, the whole process was quick and straightforward to do. In fact, by far the most time consuming task was cleaning up the data set of downloaded images. But there was a trick for doing this. First you train your network on whatever images come up in an initial search, until it achieves a reasonable degree of accuracy. Then take a look at the images that the model finds the most difficult to classify. I found that these tended to be pictures of lego figures or cartoon images. With the help of a fastai tool, it was simple to remove irrelevant images from the training and validation sets.
After a couple of iterations, I had a clean dataset and a great model, giving about 70% accuracy, which as good enough my purposes. Some examples are shown in the left column at the top of this blog.
The model’s performance was remarkably similar to my own. While Gollum is easy to identify, the wizard Saruman can be mistaken for Gandalf, Boromir looks a bit like Faramir and the hobbits Pippin and Merry can be confused.
Applications outside Middle Earth
One of the important limits of these types of image recognition models is that even if they work well in the domain in which they have been trained, they cannot be expected do a good job on totally different images. Nevertheless, I thought it would be amusing to supply the pictures of professional cyclists, particularly given the current vogue for growing facial hair.
My model was 87% sure that Peter Sagan was Boromir, but only 81.5% confident in the picture of Sean Bean. It was even more certain that Daniel Oss played the role of Faramir. Geraint Thomas was predicted to be Frodo Baggins, but with much lower confidence. I wondered for a while with Tadej Pogacar should be Legolas, but perhaps the model interpreted his outstretched arms as those of an archer.
I hoped that a heavily bearded Bradley Wiggins might come out as Gimli, but that did not not seem to work. Nevertheless it was entertaining to upload photographs of friends and family. With apologies for any waiting times to get to it running, you can try it here.
A report in VeloNews on the eve of the Tour de France stated that the French government had insisted that the “two strikes and you are out” policy must be enforced by the ASO. This means that if two positive COVID-19 test arise within a team or its support staff, the team will be removed from the race. This raises the possibility of the yellow jersey rider being ejected from the race if, for example, two mechanics record positive tests. This would be particularly unjust if it turns out that a test result was a false positive. So what are the chances that this might happen?
One of the great frustrations of the reporting on COVID testing has been the lack of clarity about what type of testing is being discussed. Tests fall in to two categories. Antigen tests use a sample from a nasal or pharyngeal swab to detect patients who currently have the disease, whereas antibody tests use a blood sample to identify patients who have developed antibodies as a result of exposure to the disease in the past – more than 28 days earlier.
There are two general types of antigen test. Real time polymerase chain reaction (RT-PCR) tests looks for specific viral fragments and need to be conducted in a laboratory, typically requiring at least 24 hours for a result. Less reliable rapid tests look for proteins associated with the COVID-19 virus, producing results in as little as 15 minutes.
The UCI requires riders and staff to be tested using RT-PCR, which is a very reliable method, having both high sensitivity (ability to detect those with the disease) and high specificity (ability to clear those without the disease). The relevant question for the Tour de France is the probability of a false positive RT-PRC test. Indeed Larry Warbass recently said he thought his result was a false positive, as he had experienced no symptoms and had maintained strict self isolation during training.
The evidence indicates that the machines performing the RT-PRC test are extremely unlikely to generate a false positive, because the test needs to find significant levels of three different targets to confirm the presence of COVID-19. In FDA experiments, 100% of negatives where correctly identified – there were no false positives. However, it remains possible that, in the moving circus of the Tour de France, a sample could become contaminated before it is tested or that samples might somehow be mislabelled. A high level of responsibility falls on the shoulders of team doctors to minimise these risks, but we can never be sure that it is zero.
One in a thousand
As a thought experiment, suppose that a negative RT-PCR test is 99.9% reliable, i.e. that one COVID-free person in a thousand somehow produces a false positive result. What is the chance that a team is unjustly sent home from the Tour?
Each team has eight riders plus support staff. Although teams might want to reduce the number of staff in the team bubble, it may be necessary to have extra catering staff in order to remain self sufficient. Let us assume an average of 17 staff on each of the 21 teams and that everybody has passed the required two negative tests prior to the start of the race. Assume further that nobody contracts COVID-19 throughout the race.
It has been indicated that everyone will be tested on the two rest days. Reassuringly, the probability of two or more false positives in a single team bubble of 25 people would be 0.03% (1-0.999^(25*2)). However, the probability that every team rider receives a negative result would be only 85% (0.999^168), meaning that there would be a 15% chance that at least one rider is unjustly ejected from the race. In fact, since at total of 1,050 tests would be taken by everyone in a team bubble, the chance of at least one person receiving a false positive would be surprisingly high: 65% (1- 0.999^1050).
Perhaps the assumption of 1 in a thousand false positives was a bit alarmist. Reducing it to 1 in thousand still produces a probability of 10% that somebody would be sent home during the Tour.
In some situations, draconian sanctions might deter team members or staff from reporting symptoms. One could imagine a soigneur or mechanic having to go home quietly after mysteriously spraining a wrist. However, this could create very negative press coverage if word got out that this person was infected.
Furthermore, the UCI rules place responsibility on the teams and specifically the team doctors to apply strict daily monitoring and controls to detect suspected COVID-19 cases.
While in the above scenarios no one actually contracted COVID-19, there is, of course a not inconsiderable chance that one of the 525 people in the team bubbles does actually become infected. If the virus spreads to more than one team, the whole race could become a fiasco.
But let’s keep our fingers crossed and hope Tour makes it to the Champs Elysées.
It was shocking to see footage of Remco Evenepoel’s horrific crash in Il Lombardia. Reports indicate that he broke his pelvis after falling from a bridge into a ravine. This follows the injuries sustained by his Deceuninck-QuickStep team mate Fabio Jakobsen in the Tour of Poland.
The video above shows the repairs to my pelvis carried out by the specialist team at St George’s Hospital. My accident was less spectacular than Remco’s, I just hit a large pothole, while riding in the Kent lanes last March. It took the ambulance two and a half hours to arrive, as this was just at the beginning of the COVID-19 crisis. In fact, lock-down was announced on the evening of my crash. There was a lot of uncertainty about the virus back then, so it was a pretty scary time to be in hospital. Nevertheless I have immense respect and gratitude for the NHS staff who looked after me.
I was given crutches the day after the operation and returned home the day after that, with strict instructions to remain non-weight-bearing on the injured leg for six weeks and then only partial weight-bearing for the next six weeks. An NHS physiotherapist contacted me and regularly provided a progression of exercises. I set myself additional challenges, like doing extra press-ups.
After six weeks of doing no proper exercise, I had lost 4kg. The circumference of my left thigh was 5cm less than the right. However, following a review at the hospital, I was given permission to start gentle exercise on my static turbo trainer. I began by removing the left pedal and performing single leg drills, but after a couple of days it was easier to put my injured leg on the pedal as a passenger. This also gave the hip some mobility.
After a week on the turbo, I was up to one hour a day at about 160 watts. It took a long time to increase this above 200 watts. I watched a lot of old cycling films, without any particular urge to go on Zwift. I started riding outside in mid-June, 12 weeks post op. My Garmin pedals allowed me to monitor the left-right balance as well as average power.The following chart shows that 21 weeks after my accident, balance is hovering around 48:52 and five minute power is back over 300 watts.
The psychological aspect of rehabilitation has been very important. I have focussed on targets and deadlines, marking each little achievement as a milestone. I am now walking without a limp, though running is still off limits. I even went kitesurfing a couple of weeks ago (don’t tell my surgeon about that one). I have been busy learning Italian, composing music and programming in Python.
Since heading back out on the roads, I have been riding cautiously, as my hip will not regain full strength until next spring. I plan to enter a couple of time trials to rekindle a sense of competition, without the danger of riding in a peloton. Racing again next season remains a goal.
Probably the most important mental aspect has been to stay positive at all times and never to spend time feeling sorry for oneself. This has been difficult as, inevitably, there have been a couple of set-backs when progress has seemed to reverse. But on the whole, my recovery has been astounding and, like Chris Froome, I remain optimistic about regaining my peak.
Remco will be back on the road next season, with the potential to pick up some results later in the year.
In previous blogs, I described how mathematical modelling can help understand the spread of the COVID-19 epidemics and provide privacy-preserving contact tracing. Looking forward at how the world will have to deal with COVID-19 in the coming months, it is likely that a significant percentage of the population will need to be tested multiple times. In a recent BBC science podcast, Neil Turok, Leon Mutesa and Wilfred Ndifo describe their highly efficient method of implementing large-scale testing that takes advantage of pooling samples. This is helping African governments save millions on the cost of testing. I offer an outline of their innovative approach, which is described in more detail in a paper published on arxiv.org.
The need for large-scale testing
The roll-out of antigen testing in some countries, like the US and the UK, has been painfully slow. Some suggest that the US may need to carry out between 400,00 and 900,000 tests a day in order to get a grip on the epidemic. When antigen tests cost 30-50 US dollars (or 24-40 UK pounds), this could be very expensive. However, as long as a relatively small percentage of the population is infected, running a separate test for everyone would be extremely inefficient compared with approaches that pool samples.
Pooling offers a huge advantage, because a negative test for a pooled sample of 100 swabs, would clear 100 people with a single test. The optimal size of the pools depends on the level of incidence of the disease: larger pools can be used for lower incidence.
The concept of pooling dates back to the work of Dorfman in 1943. His method was to choose an optimal pool size and perform a test on each pooled sample. A negative result for a pool clears all the samples contained in it. Then the infected individuals are found by testing every sample in the the positive pools. Mutesa and Ndifo’s hypercube method is more efficient, because, rather than testing everyone in an infected pool, you test carefully-selected sub-pools.
The idea is to imagine that all the samples in a pool lie on a multidimensional lattice in the form of a hypercube. It turns out that the optimal number of points in each direction is 3. Obviously it is hard to visualise high dimensions, but in 3-D, you have 27 samples arranged on a 3x3x3 grid forming a cube. The trick to identifying individual infected samples is to create sub-pools by taking slices through the lattice. In the diagram above, there are 3 red slices, 3 green and 3 blue, each containing 9 samples.
Consider, for simplicity, only one infected person out of the 27. Testing the 9 pools represented by the coloured slices will result in exactly 3 positive results, representing the intersection of the three planes passing through the infected sample. This uniquely identifies the positive individual with just 9 tests, whereas Dorfman would have set out to test all 27, finding the positive, on average after doing half of these.
Slicing a hypercube
Although you can optimise the pool size to ensure that the expected number of positives in any pool is manageable, in practice you won’t know how many infected samples are contained in any particular pool. The hypercube method deals with this by noting that a slice through a D-dimensional hypercube is itself a hypercube of dimension D-1, so the method can be applied recursively.
The other big advantage is that the approach is massively parallel, allowing positives to be identified quickly, relative to the speed of spread of the pandemic. About 3 rounds of PCR tests can be completed in a day. Algorithms that further reduce the total number of tests towards the information theoretical limit, such as binary search, require tests to be performed sequentially, which takes longer than doing more tests in parallel.
In order to make sure I really understood what is going on, I wrote some Python code to implement and validate the hypercube algorithm. In principle, it was extremely simple, but dealing with low probability edge cases, where multiple positive samples happen to fall into the same slice turned out to be a bit messy. However, in simulations, all infected samples were identified with no false positives nor false negatives. The number of tests was very much in line with the theoretical value.
Huge cost savings
My Python program estimates the cost savings of implementing the hypercube algorithm versus testing every sample individually. The bottom line is that the if the US government needed to test 900,000 people and the background level of infection is 1%, the algorithm would find all infected individuals with around 110,000 tests or 12% of the total samples. At $40 a test, this would be a cost saving of over $30million per day versus testing everyone individually. Equivalent calculations for the UK government to test 200,000 people would offer savings of around £5million pounds a day.
It is great to see leading edge science being developed in Africa. Cost conscious governments, for example in Rwanda, are implementing the strategy. Western governments lag behind, delayed by anecdotal comments from UK officials who worry that the approach is “too mathematical”, as if this is somehow a vice rather than a virtue.
Suppose you are in a Zwift race that comes down to a sprint finish. How long does it take for your avatar to respond to your heroic effort in the final dash for the line? Could a time lag cost you the race?
Consider the steps involved. First the ANT+ signal travels from your power meter to your device (i.e. computer or phone) then it goes to your router and on to Zwift’s server somewhere on the cloud. At some point your watts per kilo are converted into a velocity, taking account of your previous speed, the gradient, rolling resistance, drafting and any PowerUps in play. This calculation can be performed pretty much instantaneously compared with signal transmission time.
The ANT+ signal travels at the speed of light to your device, which is likely to be very close by, so there is little to be gained as long as there is a clear line of sight. The next step, to the router, can be slower, especially if you are relying on a wireless signal from your garage, while running a raft of other applications on your device (best to shut these down). Serious e-gamers often use a direct wired link to the router. It also helps if you have a super-fast high bandwidth internet connection. However, the time taken for the signal to travel from your router to Zwift’s gaming server, called latency, typically introduces the longest delay, especially if it has to go halfway around the world.
We don’t know the precise location of Zwift’s server, but let’s suppose it is in San Francisco. You can check the latency from your location to other parts of the world on web sites like this one. When I looked, the latency from London to San Francisco was 136ms (milliseconds) and from Cape Town it was 281ms.
In the past, banks have moved their trading desks as close as possible to exchanges, in order to obtain prices nanoseconds earlier than their rivals. As a general rule for interactive online gaming, you need a latency of less than 100ms for acceptable gameplay and over 150ms can become frustrating. But we are not talking about playing DOTA, so how do these figures apply to Zwift?
Zwift not DOTA
Let’s go back to our sprint finish, where the bunch is riding at 60kph. This equates to 16.7 metres per second, which is just a bit less than one bike length every 100ms. However, your ability to overtake your rival depends on your relative speed, not the absolute figure. Imagine a situation where you make a Herculean effort to increase your speed to 18 metres per second (64.8kph), drawing level with the leader’s rear wheel with 30 metres to go. To win the race, you have to make up a bike length, say 1.8m, travelling at a measly 1.3m/s faster than the leader. Who will cross the line first?
If you have 30m to go and the leader is a bike length ahead, he only has 28.2m left, taking 1.69 seconds. But at your higher speed you will cover 30m in 1.67 seconds, so you win by about half a wheel. However, if your avatar had responded to your acceleration with a 100ms lag, you would certainly have lost the race. If you experience this level of latency, a slower rider could beat you, just because he is located closer to the gaming server. The speed of your avatar really is limited by the speed of light.
However, sometimes it can feel like a zPower rider is overtaking you at an appreciable proportion of the speed of light. If this really were the case and Zwift wanted to represent the avatar correctly, what would it look like?
The physicist George Gamov posed this question back in 1938. He highlighted the effect of relativistic length contraction, predicted by Einstein’s theory of special relativity. In fact, the avatar would change colour, due to the Doppler shift, and light intensity would fluctuate. These effects would be further be complicated by our binocular vision, causing an unnerving blurring effect. This is helpfully explained in detail by physicists in a recent scientific paper. Surprisingly, there are practical applications for this work that may help interpret data gathered by spacecraft passing objects at very high speeds.
The Covid-19 epidemic provided a huge boost to the Zwift streaming service. Confined by a global lockdown, cyclists freed themselves from the boredom of pedalling on a static turbo trainer by logging into one of a broadening range of online virtual worlds. Zwift racing has become particularly popular. While it is relatively straightforward to simulate variations in gradient and even the effects of drafting, it is not possible for riders to demonstrate superior bike handling skills. Nor can racers benefit from adopting a superior aerodynamic position on the bike, in fact this may prove to be a disadvantage.
Setting aside e-doping suspicions, such as riders understating their weights, in the artificial world of a Zwift race, the outcome largely comes down the the ability to sustain a high level of power (watts per kilo). The engagingly competitive nature of simulated races encourages everyone to push their limits. However, since Zwift offers no penalty against maintaining a non-aerodynamic body position on your trainer, it is quite possible that regular Zwifters might become habituated to riding in position that is far from optimal for the road.
Once out in the fresh air again, many riders may have noticed improvements in the levels of power they are able to sustain, thanks to the high levels of exertion required to compete on Zwift. But in the real world, when it comes to beating other riders in a race or a time trial, the principle force a rider has to overcome is aerodynamic drag, not electromagnetic resistance.
Maximum speed is attained by adopting a riding position that provides the optimal tradeoff between the ability to generate power and a low level of aerodynamic drag. Drag depends on a rider’s CdA, which represents the drag coefficient multiplied by frontal area. Since power rises with the cube of velocity, there comes a point where it is better to compromise on power in order to reduce frontal area. This is the key to time trialing and successful breakaways.
When the race season begins, skilful and more aerodynamic racers will be able to benefit from drafting in the huge wind shadow created by Zwift diesels, while offering back much less assistance when they pull through. So after prolonged training on Zwift, racers and time trialists really need to focus on improving their aerodynamics
There are various ways to reduce drag, starting withs some basics as described in an earlier blog. Post ride analysis can be performed using Golden Cheetah, BestBikeSplit or MyWindSock. There is also a range of devices that claim to offer real time measurement of CdA. These have been primarily targeted at the TT/triathlon market, but there’s no doubt that these could be incredibly useful for both training or even, perhaps, a race breakaway. Cycling Weekly recently reviewed the Notio device, but, while useful, these tools remain expensive and a bit clunky.
Whatever you choose to do, stay safe and stay aero.
As the initial global wave of COVID-19 infections is brought under control, the world is moving into a phase of extensive testing, tracking and tracing, until a vaccine can be found. The preservation of personal privacy must be paramount in these initiatives.
The UK government’s target of performing 100,000 tests a day by the end of April 2020 provided a fine example of Goodhart’s law: “When a measure becomes a target, it ceases to be a good measure”. One tragic consequence was the willingness, even encouragement, to define just about anything as a “completed test”, including the action of simply dispatching a kit by post. This has discouraged the distinguish between different types of test: antigen or antibody, nasal swab or blood test, pin-prick or venous sample, laboratory analysis or on-the-spot result.
For those who suspect they might have been exposed to COVID-19, an antibody test is the most useful. Although there has not been time to gather sufficient information to be absolutely sure, the detection of antibodies in the blood should provide immunity from infection, at least in the short term, unless the virus mutates sufficiently to bypass the immune response. Private tests are available from providers, such as Forth, where reliable results of IgG antibodies are provided by laboratory tests performed using the Abbot Architect method.
A second area where the UK government seems to be going wrong is in hiring thousands of people to carry out intrusive tracking and tracing. Not only is this hugely inefficient, it is also a massive unnecessary invasion of personal privacy. That a data leak occurred before it even started hardly inspires confidence.
Privacy Preserving Contact Tracing
A team of epidemiologist and cryptographers called DP-3T has released open source software that makes use of Bluetooth messages exchanged between mobile phones to track and trace COVID-19 infections entirely anonymously. It does not require users to surrender any personal information or location data. The approach is the basis for the technology announced jointly by Apple and Google.
The method is explained very nicely in this video 3Blue1Brown or in comic form by Nicky Case. This is a summary of how it works. Once you download a privacy preserving app onto your phone, it transmits random numbers over Bluetooth, at regular time intervals, and simultaneously listens for the random numbers of other users. Since the numbers are random, they contain no information about the you. Your phone locally maintains a list of your transmitted random numbers. It also stores locally a list of all numbers received, possibly including a timestamp and the Bluetooth signal strength, which gives some information about the proximity of the other user. Items older than, say, 14 days can be deleted from both lists.
If a person falls ill and tests positive for COVID-19 antigens, that person can voluntarily, with the permission of a healthcare professional, anonymously upload the list of transmitted random numbers to a central database. The phone app of every user periodically checks this database against its local list of received messages. If a match is detected, the app can identify the date, time and duration of contact, along with an estimate of proximity. This allows the app to advise a user to “self-isolate” for an appropriate period. This matching can all be done locally on the phone.
If set up appropriately, neither Google nor Apple nor any government body would be able to identify any particular individual. Privacy is preserved. No human trackers or tracers are required. No ankle bracelets or police guards are necessary. The system is entirely voluntary, but if sufficient users join up, say, 60% of those susceptible, it can still have a significant impact in controlling the spread of the virus. This is the correct way forward for a free and democratic society.
In a fascinating white paper, Bert Blocken, Professor of Civil Engineering at Eindhoven University of Technology, comments on social distancing when applied to walking, running or cycling. His point is that the government recommendations to maintain a distance of 1.5 or 2 metres assume people are standing still indoors or outdoors in calm weather. However, when a person is moving, the majority of particulate droplets are swept along in a trailing slipstream.
Cyclists typically prefer to ride closely behind each other, in order to benefit from the aerodynamic drafting effect. Cycling is currently a permitted form of exercise in the UK, though only if riding alone or with members of your household. Nevertheless, there may be times when you find yourself catching up with a cyclist ahead. In this situation, you should avoid the habitual tendency to move up into the slipstream of the rider in front.
Professor Blocken’s team has performed computational fluid dynamics (CFD) simulations showing the likely spread of micro-droplets behind people moving at different speeds. As the cloud of particles, produced when someone coughs or sneezes, is swept into the slipstream, the heavier droplets, shown in red in the diagram above, fall faster. These are generally thought to be more considerably more contagious. You can see that they can land on the hands and body of the following athlete.
Based on the results, Blocken advises to keep a distance of at least four to five meters behind the leading person while walking in the slipstream, ten meters when running or cycling slowly and at least twenty metres when cycling fast.
Social Distancing v2.0
The recommendation, for overtaking other cyclists, is to start moving into a staggered position some twenty metres behind the rider in front, consistently avoiding the slipstream as you pass.
The results will be reported in a forthcoming peer-reviewed publication. But given the importance of the topic, I recommend that you take a look at the highly accessible three page white paper available here.
As a growing number of people seek to educate themselves on coronavirus COVID-19, while confined to their homes, a better understanding can be gained by taking a look at how to model an epidemic.
Researchers have created highly complex models of the spread of infections. For example, BlueDot’s disease-tracking model, described in this podcast, monitors the Internet with AI language translators and evaluates the network effects of transmission based on air travel itineraries. However, a surprising amount of insight can be gained from a very simple approach called the SIR model.
The SIR model divides the population into three classes. The susceptible class (S) includes everyone who can catch the infection. In the case of a novel virus like corona, it seems that the entire global population was initially susceptible. The infected class (I) includes all those currently infected and able to transmit the virus to susceptible people. The removed class (R) includes everyone who has recovered from the virus or, unfortunately, died. In the model, these people no longer transmit the disease nor are they susceptible. The idea is that people move from the susceptible class to the infected class to the removed class.
Although there is much focus in the media on the exponential rise of the total number of cases of coronavirus, this figure includes recoveries and deaths. In one sense this is a huge underestimate, because the figures only includes people who have taken a test and returned a positive result. As explained by Tomas Pueyo, many people do not display symptoms until around 5 days after infection and for over 90% these symptoms are mild, so there could be ten times more people infected than the official figures suggest. In another sense, the figures are a huge exaggeration, because people who have recovered are unlikely to be infectious, because their immune systems have fought off the virus.
The SIR model measures the number of infectious people. On the worldometers site these are called “active cases”. The critical insight of the SIR model, shown in the diagram above, is that the class of infected people grows if the daily number of new cases exceeds the number of closed cases.
Closed cases – removal rate
In a real epidemic, experts don’t really know how many people are infected, but they can keep track of those who have died or recovered. So it is best to start by considering the rate of transfer from infected to removed. After some digging around, it appears that the average duration of an infection is about 2 weeks. So in a steady state situation, about on person in 14 or 7% of those infected would recover every day. This percentage would be a bit less than this if the epidemic is spreading fast, because there would be more people who have recently acquired the virus, so let’s call it 6%. For the death rate, we need the number of deaths divided by the (unknown) total number of people infected. This is likely to be lower than the “case fatality rate” reported on worldometers because that divides the number of deaths only be the number of positive tests. The death rate is estimated to be 2-3%. If we add 3% to the 6% of those recovering, the removal rate (call it “a”) is estimated to be 9%.
In the absence of a cure or treatment for the virus, it is unlikely that the duration of infectiousness can be reduced. As long as hospitals are not overwhelmed, those who might otherwise have died may be saved. However, there is not much that governments or populations can do to speed up the daily rate of “closed cases”. The only levers available are those to reduce the number of “new cases” below 9%. This appeared to occur as a result of the draconian actions taken in China in the second half of February, but the sharp increase in new cases that became apparent over the weekend of 6/7 March spooked the financial markets.
New cases – infection rate
In the SIR model, the number of new infections depends on three factors: the number of infectious people, the number of susceptible people and the something called the infection rate (r), which measures the probability that an infected person passes on the virus to a susceptible person either through direct contact or indirectly, for example, by contaminating a surface, such as a door handle.
Governments can attempt to reduce the number of new infections by controlling each of the three driving factors. Clearly, hand washing and avoiding physical contact can reduce the infection rate. Similarly infected people are encouraged to isolate themselves in order to reduce the proportion of susceptible people exposed to direct or indirect contact. Guidance to UK general practitioners is to advise patients suffering from mild symptoms to stay at home and call 111, while those with serious symptoms should call 999.
When will it end?
As more people become infected, the number of susceptible people naturally falls. Until there is a vaccine, there is nothing governments can do to speed up this decline. Eventually, when enough people will have caught the current strain of the virus, so-called “herd immunity” will prevail. It is not necessary for everyone to have come into contact with the virus, rather it is sufficient for the number of susceptible people to be smaller than a critical value. Beyond this point infected people, on average, recover before they encounter a susceptible person. This is how the epidemic will finally start to die down.
When people refer to the transmission rate or reproduction rate of a virus, they mean the number of secondary infections produced by a primary infection across a susceptible population. This is equal to the number of susceptible people times the infection rate divided by the removal rate. This determines the threshold number of susceptible people below which the number of infections falls. The critical value is equal to removal rate relative to the infection rate (a/r) . When the number of susceptible people falls to this critical value, the number of infected people will reach a peak and subsequently decline. More susceptible people will still continue to be infected, but at a decreasing rate, until the infection dies out completely, by which time a significant part of the population will have been infected.
This looks very scary indeed
Running the figures through the SIR model produces some extremely scary predictions. At the time of writing, new cases of infection were rising at a rate of about 15%. At this rate, the virus could spread to 2/3 of the population before it dies out. If three percent of those infected die, the virus would kill 2 percent of the population. Based on results so far these would be largely elderly people or those suffering from complications, so it is extremely important that they are protected from infection. If the virus continues to run out of control, the number of deaths could run into the millions before the epidemic ends.
It is absolutely essential to reduce the infection rate and keep it low, particularly among the elderly and vulnerable groups
We should watch China carefully for new cases. If none arise, it suggests that a large proportion of the population gained immunity through infection, even though lower numbers of infections were reported. However, if the imposition of constraints temporary reduced the infection rate, leaving a large susceptible pool still vulnerable, the epidemic could re-emerge once the constraints are relaxed.
What to do?
The imposition of governments restrictions on travel and large gatherings are forcing the people to rethink their options. Where possible, office workers, university students, schoolchildren and sportsmen may find themselves congregating online in virtual environments rather than in the messy and dangerous real world.
Among cyclists, this ought to be good news for Zwift and other online platforms. Zwift seems to be particularly well-positioned, due to its strong social aspect, which allows riders to meet and race against each other in virtual races. It also has the potential to allow world tour teams to compete in virtual races.
In fact the ability to meet friends and do things together virtually would have applications across all walks of life. Sports fans need something between the stadium and the television. Businesses need a medium that fills the gap between a physical office and a conference call. Schools and universities require better ways to ensure that students can learn, while classrooms and lecture theatres are closed. These innovations may turn out to be attractive long after the coronavirus scare has subsided.
While the prospect of going to the pub or a crowded nightclub loses its appeal, cycling offers the average person a very attractive alternative way to meet friends while avoiding close proximity with large groups of people. As the weather improves, the chance to enjoying some exercise in the fresh air looks ever more enticing.