An agent-based model of the Corona-pandemic

To the model.

How do different policy instruments influence the Corona-pandemic? Examine the effects of the different instruments on the development of the epidemic in an online policy laboratory.

What mortality rates and what temporal course of the crisis are to be expected?

The corona-pandemic makes us all feel insecure. For many days we have been keeping an eye on the publications of the John Hopkins University and the Robert-Koch-Institute regarding the latest number of people infected with the corona-virus. Politicians all over the world react with different instruments: While most nations have declared the slowdown of the infection as their ultimate target, others discuss a possible strategy of total contamination to evoke a wider immunization of the people in a short period of time. However, the last-mentioned strategy does not represent the most convincing one, since it will lead inevitably to the overload of the health system and related to an increasing mortality rate. But even the strategy of deceleration is not uncontroversial since it is unclear how long the applied measures, which are heavily affecting the social and economic life, need to be maintained.

Two of the central terms in this debate are the exponential growth of infections and critical limits in hospital systems which could be exceeded and cause a total collapse concerning the provision of services. These two notions, exponential growth and critical thresholds, are typical ingredients of complex systems, which are hard to forecast and consequently are always good for unexpected surprises.

Assisted by modern computer simulations the development of complex systems can be described and an intuition for their apparently incalculable paths of development can be gained. For the consideration of possible development paths, we have designed a policy laboratory, which is easily can be used by everyone.

In our model, we refer to a typical Europe city with its different activities of the citizens (working places, supermarkets, schools etc.) and residential areas. The inhabitants of the town live a very normal life, which can be described by a calendar. In the morning, the adults are on their way to the offices and factories, the children go to school and to their sport activities thereafter. In the afternoon, there are also more activities in the shopping malls. In all of these places, frequent encounters and social interactions take place. For a highly infectious virus like the Corona-virus these are ideal conditions to spread. On the monitor, you can see how little by little a large percentage of people gets infected and partly also gets seriously ill or even dies. Since the capacity of the hospitals in our town is limited, the rate of fatalities increases when the capacity limit has been exceeded. After a certain period of time, the virus will have disappeared also without intervention and the surviving inhabitants will have developed immunity to the virus. Many deaths are to be lamented.

In our model town, you are able to design health politics in different ways: for instance, you can send infected people into domestic quarantine, you can improve general conditions of hygiene by health education, you can lock down schools or invest in the capacity of beds in hospitals. The resulting changes of the situation will directly be visible on the monitor. All measures will influence the number of seriously ill and dying people as well as the duration and course of the epidemic. You can experience in our policy lab what big differences can be achieved by the various instruments and come then to your own – better informed – conclusions.

Two final remark are necessary: It is important to point out that the policy lab prioritizes social interactions. Epidemiological and medical contexts are very simply modelled and make use of publicly available sources of knowledge. This gives rise to various opportunities of interdisciplinary cooperation. Our model offers many possibilities of expansion. We are working on it. And please keep in mind: our policy laboratory does not provide any forecasts but it is supposed to strengthen our understanding of complex systems.

Please cite as:
Vermeulen, Ben, Müller, Matthias and Pyka, Andreas (2021) 'Social Network Metric-Based Interventions? Experiments with an Agent-Based Model of the COVID-19 Pandemic in a Metropolitan Region' Journal of Artificial Societies and Social Simulation 24 (3) 6 <http://jasss.soc.surrey.ac.uk/24/3/6.html>. doi: 10.18564/jasss.4571