This is a discussion paper launched in the Outbreak Science project. It’s a draft, any input and discussion is appreciated.
Author: Johannes Musseleck
With many countries and communities in shut-down, and measures beginning to show success, the pressure on leaders is rising to gradually move out of the shut-downs, i.e. to “re-open”. The most promising path towards this is by means of “smart disctancing”: It targets disproportionally protecting (and isolating) critical parts of the population (those having to expect the most severe consequences in case of being infected, e.g. older citizens or those with a relevant pre-condition) while bringing the rest of the population back into the direction of a “normal” mode.
While the shut-downs often took place in a rather general fashion, re-opening is a delicate task for political leaders, as there is not a lot of evidence, scientific data and experience to base the “game plan” of re-opening on. Therefore, here are some recommendations on a possible approach of structuring the process:
In contrast to the general shut-downs, re-opening shall be based on a modular approach. The modules are derived by modularization of the economy and social functions, so by creation of virtual clusters. Examples could be types of businesses (illustrative examples to show the granularity could be e.g. large shops, small shops, restaurants, hair dressers, primary schools, middle schools, etc.). These modules will be the potential units for which a particular respective re-opening approach can be designed.
The framework will be that on basis of the modularization, a scoring model to assess the re-opening priority of the respective modules is created along the factors of a) the risk of increasing infections, b) the direct and indirect economic impact of closure of the module and c) the social importance of the module.
Risk of increasing infections: This can have various thoughts factoring into the evaluation. Dentists probably have a higher risk of infection than workers in a closed office environment. High school students will probably be better able to keep distance to other students than children in a kindergarten, so the relative risk should be lower in high schools. This takes into account the ability to reduce infection risk in the module e.g. by enforcing hygienic or distancing related measures.
Direct and indirect economic impact of closure of the module: On the micro level, this will look at the individuals and their ability to earn their living, and the individual businesses, but also has broader perspectives. It may be difficult to assess in some areas, as these effect can be coming on the long term only or may not be directly visible – example: If parents will have to stay home to take care of their children because schools are closed, they may not be able to do their job which can create a detrimental effect on their income or their employer’s success. On the macro level, this should include a view at the economic cost for an economy, e.g. by looking at the criticality of the module to the countries’ economy and mitigation cost (support payments, short-time work compensation, etc.).
Social importance the module: While this may sound like a very soft factor, creating a positive social “feel” is important for various reasons: Avoiding social unrest, keeping citizens happy enough to buy in to the measurements, avoiding negativism and depression which can even lead to an increased number of suicides, and keep satisfaction with political decision makers up (which must not be underrated, as they have to take and implement the decisions and are interested in not being seen overly negatively). As examples, social exchange on a personal level and physical well-being are among the factors to consider here, so activities like allowing people to work out (e.g. not prohibiting
jogging, allowing fitness studios to open), meet and be entertained (restaurants, museums, theaters, etc.) are referred to here.
Based on the rating derived by the model, modules can be re-opened step by step, starting with the highest scores and working the way down to the lower score modules as time goes on.
This must be accompanied by carrying out a broad monitoring and testing in relevant areas in a representative fashion and irrespective of whether or not a suspicion of infection exists in the individual cases. Target is to create an early warning system, so a fast analysis of the test results and mining of the data is important. If a high correlation is found between people in touch with a particular re-opened module and an increased number of infections, this will give a direct feedback on the assumed risk of increasing infections (see above) and may lead to a downgrading and re-closing of the module. At the same time, identification of modules with a lower than expected risk of increasing infections can also change the scoring of these modules and modules with a similar profile (illustrative fictional example: If due to the scoring, the decision was taken to re-open primary schools and it was found that a few weeks in, teachers did not show a spike in infections, this may have an impact on the rating of kindergardens).
The approach suggested herein takes into account, that not for all areas scientific data and support is available yet, but decisions need to be taken instantly. So it mitigates and helps to steer the decision making, trying to avoid having to change course frequently in a trial and error fashion by applying a dosing mechanism. Still, given the little scientific knowledge and the unprecedented nature of the situation, wrong decisions cannot be prevented completely. This creates a challenge to political leaders, as admitting and correcting wrong decisions is not an easy task in the political area. Therefore, the process needs to be prepared by a) applying transparency, b) demonstrating “cautious optimism” and c) fostering a feeling of community. While overarching shut downs can be enforced by power (government orders can be given fairly broadly and people have to – and most will – adhere), re-opening effectively needs cooperation and discipline by all. The pre-condition for this is trust.
Therefore, communication by the political leaders is a critical success factor:
Applying transparency: It is of utmost importance for political leaders to tell the people what they know, but also what they do not know and why they implement certain measures. Taking the time and effort to set up the above mentioned scoring model will be of great help here as well, as the underlying analysis will show that decisions are well-founded, not taking in a light-hearted manner and based on facts as much as possible, which creates rationales to be used in communication.
Demonstrating cautious optimism: As in every crisis, it is the leaders’ job to show believe that the crisis can be overcome, to spread positivism, avoid lethargy and motivate to go the extra mile, both in adhering to the measures and in mitigating the negative effects. In the best case, this will create a “we can do this mentality and spirit.
Fostering a feeling of community: While coping with the situation typically results in social distancing of some kind, it is important that people feel a sense of belonging together and being in the same boat. This will decrease the perceived burden (by realizing how well others cope with the situation and by motivating each other), lead to creating formal and informal support structures mitigating the impact for the individual and avoid an “us (the people) vs. them (the leaders)” dynamic, which would
ultimately result in a vicious circle of decreased adherence to the implemented measures, higher infection rates, less believe in “them”, stronger “us vs. them” dynamic and so on. This feeling of community does not necessarily have to happen on the scale of a country, it can also happen in smaller communities, e.g. a city.