Consider the picture below of two hypothetical Accident and Emergency departments (A&E), one that has no covid-regulations and simply has the available nurses trying to help all comers as fast as possible. In the other one the nurses try to prevent mingling by testing newcomers (or do some other form of screening) in one part of the A&E and then funneling the patients, causing a queue that is an ideal infection place for covid.
In this picture, the covid-infected are the ones with little green dots floating around them. The red figures are the nurses. In the left-hand side situation patients come and go, helped relatively quickly by the three nurses who do nothing but help patients. You see that using the whole space as much as possible means most patients are spaced relatively widely anyway, so there will be some close-encounter mingling but not much.
The right-hand side situation depicts the same hypothetical A&E where they got extremely worried about being criticised for mingling patients. So the manager of that A&E decided to become ‘responsible’ and demand all comers get a covid-test (or questionnaire) administered by one of the nurses behind the red line. The covid-infected are then subsequently put in one sealed part of the hospital whilst the uninfected go to another part, helped by different nurses.
Note the congestion effect in this hypothetical example (which is inspired by the eye-witness account of an actual nurse: see this nurse statement Jan 29th lockdown sceptics): because the test (or questionnaire) takes time, you now have a much longer mingling of patients before being helped, whilst there are also fewer nurses actually helping, which further increases the queue. So that waiting room becomes smaller, more crowded, and exactly full of the wrong people: infected and non-infected bunched up for a considerable length of time. The total infection numbers are far higher in the right-hand side situation than the left-hand side as a result of the ‘covid-safe’ policies of the A&E manager. This is an example of covid-congestion effects: congestion caused by well-meaning rules designed to prevent infections in fact cause more of them.
Of course you can think of counter-reactions to the problem in the A&E example: move the queue outside, have tests done off-facility, ask everyone to wear masks, tell people not to come to hospitals, stop treating people altogether, etc. Each of these counter-reactions has actually been implemented in the UK but have simply caused other types of congestion problems that I will explain more fully later on. For now, just consider that in winter weather you cannot let very ill patients stand outside in the cold rain, whilst masks at best reduce the inflow of infections a bit but don’t count on them when people are exposed for any length of time. Also, telling people not to come to A&E means infected people are untested, getting worse without early treatment (so more likely to die) and also infecting people elsewhere. The congestion effects hence simply cannot be avoided if you reserve a large slice of your health space and health staff for covid-avoidance activities like testing and re-organising: however you slice it, you are then reducing the productivity of the health system which means less care and a larger queue somewhere of people getting more and more ill, hence more vulnerable and infectious.
If you look closely at the way the health system operates in the UK, but also in other countries, you see an awful lot of these kinds of congestion effects: the creation of bunched up patient groups because some part of the system has become very concerned with being seen to fight against infections in a way that reduces their productivity and their available space. This is my suggested explanation for the curious phenomenon that the more stringent the lock-downs in Europe and the Americas, the higher the covid death toll often is. Over the fold I unpack this phenomenon in greater length and give more examples of covid-congestion effects at many levels. I thus set out the case for the importance of covid-congestion and what the insights would mean for the efficacy of the policies followed in different regions of the world at different times, and for what optimal policy would look like in various circumstances. Obviously, nothing in the below is certain (if you want certainty, go see your favourite astrologer).