MY EDITORIAL ON YOU TUBE
I am returning to the thread of research devoted to cities and their role in the human society. My goal is to outline an informed prediction as regards the impact of COVID-19 pandemic on our civilisation, and the prediction is based on a stylized fact I can observe: the most severe outbreaks of COVID-19 take place in densely populated areas, cities or conurbations.
As I connect two threads of my writing and blogging, namely research on cities and collective intelligence, on the one hand, and my investment strategy, on the other hand, one big coin dropped, with ‘logistics’ stamped in that place where traditional coins would display the profile of some king or queen. If my intuition is correct, i.e. if COVID-19 is really forcing us and is going to force us even more into a spatial rearrangement of our settlements, logistics will be a pivotal industry. Here comes that funny coincidence. In Poland, we have that express delivery company, Integer Capital Group, which has pretty much revolutionized the landscape of parcel deliveries. In United States, there is another Integer, namely Integer Holdings Corporation, specializing in portable medical devices, such as neuro- and cardio- modulators. Unfortunately, only the second Integer has its stock publicly listed and available to small investors. Still, there are stocks such as Deutsche Post (the mothership of DHL), UPS or Fedex, which are all booming, stock-price-wise, and them booming seems to have strong foundations in the economic environment. Cool. Looks like I have just found another wave to ride (see The moment of reassessment for the underlying logic of the concept). The strategy I am forming in my mind consists in selling out my positions in Airway Medix and Bioton, whose fundamentals seem a tiny bit wobbly, then take the next rent I collect from that apartment in town, and invest it all in a basket of stock made of four companies, all of them doing logistics: Deutsche Post, UPS, Wisetech, and XPO Logistics. This time, under those hyperlinked names of companies, instead of the habitual ‘investors relations’ websites, my readers can find Excel workbooks with the technical analysis I did, i.e. moving average price (the ‘Mov’), mean reverted price and volumes traded (under the ‘MR’ label), and extrapolated return on the last closing price (‘Return’), whilst in the spreadsheet labelled ‘Sheet1’ you can find the source data with equations that I use to transform it.
Right, I went off track. I was supposed to focus on cities, COVID-19 and stuff. Still, what do you want: things just connect in surprising ways. The entire topography of those surprising ways is called life. Now, as my internal curious ape is back on track of the serious science to do, I am formalizing my scientific take on two issues: the measurement of urban space, and geographic patterns of the COVID-19 pandemic. Here comes an interesting paper, still at the stage of preprint: ‘Time, Space and Social Interactions: Exit Mechanisms for the Covid-19 Epidemics’ by Scala et al. 2020[1]. The authors attempt to trace the possible scenarios of SARS-Cov-2’s epidemic spread in Italy after the lockdowns are lifted. They use a simple compartmental epidemic model, and I use their model as base for my own thinking about the long-term impact of COVID-19 on our society, mostly on the way that cities live their life. What? Cities are not alive? They don’t live any life, they just function? Well, just go, one day, and observe a city at dawn, as people inside it start going about their business. Just look how those streaks of light, at sunrise, move through that giant urban body, akin to a bloodstream. Those things (i.e. cities) are alive, and we are alive in them.
Scala et al. 2020 stroll down the same cognitive avenue which I am taking: they assume that our exposure to COVID-19 is a combination of three types of factors: biological (our biology vs that of the virus), technological (the really available science we have), and social, i.e. the way we interact and hand the virus over to each other. This distinction is useful to remember. The way most countries go through the epidemic curve is mostly social. We observe a mounting wave of contagion, at first, then a peak comes, which we pass over, and the curve starts to flatten. All that plays out over something like 8 ÷ 11 weeks. Technology did not change during those 11 weeks. I mean, even in Star Trek it wouldn’t. Biology stays more or less the same, both on our part and on the part of the virus. What makes that specific shape of the epidemic curve is our behaviour.
This pandemic paved the way to fame for a previously shy coefficient, the R0. Hello everybody, I am R0. I am the average number of people that can be infected by one already infected and infectious person. I am the proportion between the coefficient β of transmission, and the coefficient γ of removal. The latter means either recovery or death. Whichever happens, the given person is removed from the ranks of those susceptible to infection. Thus, I, R0, spell: R0 = β/γ. The γ is essentially made of biology and technology. It is all about the way our body responds to the pathogen, and the way that doctors can have a few words to say about it. On the other hand, the coefficient β of transmission is a mixture of biology and social behaviour. It is about the way we can infect each other by coughing, and about the odds that we have any opportunity to cough at each other. The β can spell β = Cλ, where C is the rate of social contact between people, and λ is the likelihood of infection once such contact happens.
Lockdowns have driven the C factor down, in response to an alarmingly rapid increase number O of clinically observed patients with acute symptoms of COVID-19. It is important to understand: as societies, we do not react to the number of people infected and we do not even react to the number of people with acute symptoms. When was it the last time we closed all the roads and cancelled all traffic thereon because of the number of people injured in traffic-related accidents? Have we ever been tempted to do so in the view of people getting serious cardio-vascular problems as a result of them spending hours a day in their cars and being, most of those hours, viscerally pissed about the way they are? No, we just make more comfortable cars and roads, because all those bad things in traffic happen at pretty a constant rate. We, humans, are programmed to notice gradients of change rather than absolute states (see e.g. We really don’t see small change and The kind of puzzle that Karl Friedrich was after). The pandemic introduced a new gradient of disquieting change into our social system, and we reacted by taking cover.
Social response to the pandemic can be represented very simply as elasticity of social contacts to change in the occurrence of acute COVID-19 cases, or ∆C/[∆(O/N)] (once again, O stands for the aggregate number of acute, clinically observed cases). In the presence of epidemic danger – and this specific danger is going to stay with us for a while – we react by inducing a sinusoid pattern into our ∆C: we lock down, then we release etc. As I said before, I deeply believe that lockdowns as such manifest panic behaviour at the collective level rather than a rational response. They are not sustainable economically, and even psychologically. Whatever we reward, we reinforce, and whatever we reinforce grows. If we reward fear of social contact, that fear is going to grow and our European history tells us very explicitly what happens next: isolated colonies of (allegedly) sick people, erosion of socially cohesive behaviour, lynching etc. Question: how can we develop a collectively rational reaction to the pandemic, whilst staying functional as a society? Answer: by modifying our set of social roles so as to be flexible in the ∆C department, and so as to get healthier and more resilient to infections, thus to drive down the λ likelihood of serious infection due to social contact.
Question: are there any historical precedents of societies purposefully changing their repertoires of social roles so as to achieve those two outcomes? Well, yes, and we keep doing it all the time. A good person is clean, right? We don’t like interacting with smelly people, and we socialize easily with folks who are visibly clean in their personal hygiene and wear clean clothes. We like the company of manifestly healthy people much more than the company of someone obviously sick. We shake hands only when we have reasonable chances to shake a clean hand. We sustain an elaborate game of social rivalry where a higher position in hierarchy means a bigger personal space indoors, both at work and at home.
We have a set SR = {sr1, sr2, …, srm} of ‘m’ social roles. Each social role sri is characterized by a frequency of direct, potentially infectious social interactions – the coefficient C(sri) – and by a probability p(sri) that any given individual endorses that specific role. The overall intensity C of such interactions in the given society is a weighted average of individual intensities and comes as C = ∑ [p(sri)*C(sri)]. At this point, I return to the assumption I phrased out in ‘City slickers, or the illusion of standardized social roles’: social roles are essentially individual and idiosyncratic. Categorial social roles, such as ‘a doctor’, ‘a housewife’ etc. are cognitive simplifications that we build in order to save bandwidth in our brain. Therefore, the C(sri) coefficient is really local and individual, and the summation sign ∑ in the C = ∑ [p(sri)*C(sri)] expression has a lot of summing work to do.
When we want to cut down our overall C, and do it more sensibly than by closing all hairdressers for 2 years, we need to reshape our C = ∑ [p(sri)*C(sri)], i.e. we need to increase the prevalence p(sri) of social roles with relatively low C(sri), and reduce the occurrence of those who go the opposite, contact-abundant way in their C(sri). Yes, ‘who go’, and not ‘which go’. They are idiosyncratic phenomena in individual people, remember?
In my update entitled ‘The perfectly dumb, smart social structure’, I sketched a piece of artificial intelligence supposed to simulate the interplay of social roles, and I ran a few experiments with it. Those experiments indicate that it is not really possible to kick selected social roles out of the system. Even if we attempt to, they end up by coming back, through one hole or another. On the other hand, the emergence of new social roles can naturally push the incumbent ones out of the system, as long as the society tries to keep all its marbles together and assures coherence between those newcomers and the incumbent ones.
The way out of the shitty spot which we are currently in, some place between the epidemic spread running amok, with reins dangling loosely on its neck, on the one hand, and the how-much-longer-can-we-stay-in-lockdown absence of sensible strategy, on the other hand, consists in triggering the creation of new social roles, endowed with relatively low incidence C(sri) of infectious social contacts, whilst maintaining as much social cohesion as possible.
We are facing a functional paradox. Cities are the only social contrivance that we have invented, so far, in order to speed up the creation of new social roles, and cities are demographic anomalies, displaying abnormally high density of population, thus by abundant social contacts. Now, with the pandemic around, we need to create new social roles with lower typical occurrence C(sri) of potentially infectious social contacts. Can we induce lower intensity of social interactions and maintain social cohesion in an environment which is naturally made for rich social interactions?
A thread of observation has come to my mind. We have had cities for quite a long time, right? Quite a long time means centuries and even millennia. We have also had epidemics in the past, and, as a matter of fact, we tend to forget how many of them we had, and how brutal they used to be. We tend to be anti-vaccine because we have been spoilt by the prevalence of vaccines and by the absence of serious epidemic outbreaks. Anyway, cities have been there for a long time, and epidemics had been there for a long time, sort of hand in hand, and cities have been and still are the most privileged spot of infection. Does it make sense? Somehow it does, and I want to understand how exactly.
Potentially infectious social contacts fall in two categories: contacts with people whom we don’t know or haven’t checked on for a long time, for one, and contacts with people heavily exposed to other infectious contacts in their environment. Thus, I need to introduce a scale of infectious risk in social interactions associated with any social role sri in the set SR = {sr1, sr2, …, srm}. I obtain something like the Itô calculus: an integral of social interactions inside the integral of a social role. It looks complicated, but we can simplify it by assuming that any set SR = {sr1, sr2, …, srm} of ‘m’ social roles is coupled with a set SC = {sc1, sc2, …, scn} of ‘n’ social interactions. The set SC is structured over an axis (dimension) of infectious risk. I can approach risk in a classical way, called the VaR method AKA Value-at-Risk: risk is a quantity, which, in turn, results from associating a given magnitude of damage with a probability of happening. In the case of an epidemic disease, the magnitude of damage ranges along a scale of severity in symptoms combined with their durability.
The so-far collective behaviour during the COVID-19 pandemic indicates that societies tend to minimize aggregate epidemic risk, defined as the arithmetical product: ‘likelihood of infection * severity of symptoms’. In the case of each infected person, the real danger are the most acute symptoms, and thus, in our practical perception of epidemic risk, severity of symptoms can be considered as a subjective constant: we are afraid of the worst that can possibly happen to us. When we reduce the epidemic risk by lockdowns and social distancing, we control the likelihood of infection.
In the presence of prolonged pandemic, and COVID-19 is likely to play out precisely this way, we are likely to minimize epidemic risk by remodelling our social roles. We can maximize the occurrence of predictable social interactions with knowingly healthy people, and minimize haphazard interactions with people of unknown exposure to infection. With a bit of science, we can reasonably narrow down the category of ‘knowingly healthy people’ to those whom we can categorize as non-symptomatic of COVID-10 for a sufficiently long time to assume they are non-symptomatic because they are either non-infected or they have successfully battled the infection, and not because they are asymptomatic. In plain terms, we discreetly observe someone for 3 weeks and we can make and educated guess as for what likelihood of infection that person conveys. Of course, this is just partly scientific, as we never quite know, and yet I think this is the way that people in the past – when epidemic diseases were daily bread, so to say – used to identify those whom they can reasonably hang out with.
At this point, I am going back to the very definition of urban structures, and to the strange and interesting discrepancy in the assessment of what actual, present-time cities are (see Demographic anomalies – the puzzle of urban density). Cities are distinctive from the countryside by their abnormally high density of population, which is a proportion between population and the territory it occupies. There are two distinct methods of measuring both: administrative and GPW (Gridded Population of the World). I am sharpening my understanding of these two approaches so as to understand the dynamics of urban structures as such. My approach is empiricist. I hope to understand better the boundary between cities and the countryside through understanding the fine distinctions as regards the way we perceive that boundary.
Administratively, towns and cities are being defined by the law. In Antiquity and in the feudal society, legal definition of a town was that of a general privilege. City dwellers were allowed to do things, which people living in the countryside couldn’t do. It was frequently about holding a regular marketplace, and some sort of local government, incorporated as city council and/or the office of mayor. That privilege-based approach to the legal definition of a city seems to have vanished during the 19th century, when cities became nests of large-scale industry, and, interestingly, the number of officially defined cities seems to have frozen approximately at the same time. At some point in time – in Europe it could be around 1900 – the process of legal-administrative identification of urban settlements came to a virtual standstill. Further changes consisted in spatial extension of the already defined towns and cities. Interestingly, that pivotal moment coincided with the progressive elimination of epidemic diseases, through sanitation, healthcare, vaccination etc.
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[1] Scala, A., Flori, A., Spelta, A., Brugnoli, E., Cinelli, M., Quattrociocchi, W., & Pammolli, F. (2020). Time, Space and Social Interactions: Exit Mechanisms for the Covid-19 Epidemics. arXiv: Physics and Society.
