Simulating non-pharmaceutical inventions against the spread of a viral disease from a social science perspective.
The spreading of viruses depends not only on its biological properties or the availability of effective pharmaceutical remedies but also on human behaviour. Since there are currently no pharmaceutical measures available against CoVID-19, much of the current discussion is on non-pharmaceutical measures such as social distancing. Whether and to what extent these approaches are effective critically depends on human behaviour: In order to contain the spreading of CoViD-19, a sufficient number of individuals must change their behaviour. The authors Dominik Klein, Johannes Marx, Daniel Mayerhoffer & Jürgen Sirsch offer an interactive computer simulation that compares different policy measures, such as social distancing and quarantine, in terms of their potential for virus containment. In the simulation, social distancing with high compliance is sufficient for containing the spread of CoViD-19. Alternatively, putting the infected into strict quarantine reaches similar results, albeit requiring even higher levels of compliance. The model also offers insights regarding the effectiveness of other measures, such as tracing contacts of infected persons.
Die Verbreitung von Viren hängt nicht nur von ihren biologischen Eigenschaften oder der Verfügbarkeit wirksamer pharmazeutischer Mittel ab, sondern auch vom Verhalten des Menschen. Da derzeit keine pharmazeutischen Maßnahmen gegen CoVID-19 zur Verfügung stehen, wird ein Großteil der aktuellen Diskussion über nicht-pharmazeutische Maßnahmen wie z.B. soziale Distanzierung geführt. Ob und inwieweit diese Ansätze wirksam sind, hängt entscheidend vom menschlichen Verhalten ab: Um die Verbreitung von CoViD-19 einzudämmen, muss eine ausreichende Anzahl von Individuen ihr Verhalten ändern. Die Autoren Dominik Klein, Johannes Marx, Daniel Mayerhoffer & Jürgen Sirsch bieten eine interaktive Computersimulation an, die verschiedene politische Massnahmen wie soziale Distanzierung und Quarantäne hinsichtlich ihres Potenzials zur Eindämmung des Virus vergleicht. In der Simulation ist soziale Distanzierung mit hoher Compliance ausreichend, um die Ausbreitung von CoViD-19 einzudämmen. Alternativ dazu kann eine strikte Quarantäne der Infizierten zu ähnlichen Ergebnissen führen, wenn auch mit einem noch höheren Grad an Bereitschaft zur Einhaltung. Das Modell bietet auch Einblicke in die Wirksamkeit anderer Maßnahmen, wie z.B. das Aufspüren von Kontakten infizierter Personen.
Viral infections and the treatment of resulting diseases are medical problems. What do social scientists have to contribute to these issues? Since, in the case of CoViD-19, no pharmaceutical interventions are available at the moment, the focus of the current debate is primarily on non-pharmaceutical measures. As these depend on the behaviour and interaction of humans, CoViD-19 containment also has a social science dimension. In order to adequately assess the chances and limitations of such measures, it is necessary to assess which dynamics individual actions create on the collective macro-level. An adequate tool for assessing complex aggregation effects of individual actions are simulation models.
Just as with every model, our simulation rests on several assumptions about parameters, mechanisms and processes. Our model simulates the spreading of a virus within a population of 2000 people. This small population guarantees swift performance in a web environment while creating results that proved robust in tests with larger populations.
In the model, individuals move within their immediate environment. Every encounter between an infectious and a not yet infected person results in the latter becoming infected. Broadly, the model can simulate two approaches to contain the epidemic, namely social distancing and quarantine. Social distancing reduces the mobility of individuals and thus reduces the number of social contacts. However, in our model world, individuals practising social distancing can still come into contact with people who do not distance themselves. Therefore, infections will still occur. The second approach to contain the epidemic is called quarantine. Unlike social distancing, this measure can never lead to infection.
This simulation does not intend to license concrete policy recommendations. For such an objective, additional factors and more precise calibration of the characteristics of CoViD-19 would have been needed.. Instead, the simulation wants to inform research on social mechanisms underlying the spread of viral diseases. Furthermore, it can also be used for illustration in academic teaching and beyond.
Simulations without any countermeasures display a rapid spread of CoViD-19 that quickly pushes the health system above its capacity limit (see Figure 1).
The model offers a wide range of political measures to slow down and mitigate the wave of infection (“flattening of the curve”). A first instrument is social distancing by the entire population. Sufficiently reducing stress on the healthcare system requires compliance of 80% of the population (Figure 2). To what extent such high levels of compliance are possible is difficult to assess: While individual motivation is certainly crucial, some may be unable to avoid contacts, for instance, in system-relevant professions.
Alternatively, one may require only those with symptoms to practice strict distancing. Taken alone, this measure only has negligible influence on virus spreading, as patients can still infect others who move around unrestrictedly.
The model can also simulate strict quarantines that are enforced not only on hospitalized patients but also on persons with merely mild symptoms (Figure 3) and possibly also their contacts. In contrast to simple distancing, quarantines fully prevent infections, as patients no longer engage in potentially contagious encounters. In this scenario, the risk of infection is exclusively posed by the symptom-free sick, as they still move around uninhibitedly with the potential to infect others. Surprisingly, in the model, this quarantine policy is about as effective as social distancing with 80% compliance (Figure 3).
However, our model might arguably underestimate the effect of social distancing, as practitioners of distancing can continue to be infected by those who move normally. In the real world, this should probably not occur to the same extent. Here, it seems plausible that self-distancing goes along with a lower risk of being infected by others. Furthermore, the model assumes everybody to fully respect quarantine, which may be unachievable in practice. Lastly, to effectively quarantine the mildly symptomatic, these individuals must attribute their symptoms to CoViD-19. This turns out crucial, as the effectiveness of measures drops significantly if less than 90% of this group complies with quarantines.
Increasingly, the debate focuses on replacing social distancing with alternative, less restrictive measures (Smart Distancing): Variations of the model combine different policies, including general social distancing (applied to larger or smaller subgroups) as well as targeted distancing or quarantining of sick people and possibly their contacts. However, in comparison with the most disruptive approach (general social distancing combined with strict quarantines of everybody infected), any relaxation increases the epidemic’s spread. Naturally, which containment measures are judged as being sufficient will depend on what level of spread is considered acceptable, but also on the social, health or economic costs of these measures. In the end, various sets of measures may be deemed sufficient (Figure 4 depicts one example). However, the model suggests that targeted measures focussing on isolating ill people and their contacts should always be accompanied by broad approaches, e.g., social distancing among parts of the general population.
Furthermore, tracing chains of infection has played an essential role in combating CoVid-19. In the model, it turns out advantageous to quarantine not only sick people but also their contacts. However, for effective containment, identifying only some of the patients’ contacts seems sufficient. For this, current ideas on using mobile phone data seem superfluous, as targeted interviews suffice.
Our simulation suggests that measures currently imposed around the world might be suitable for containing the CoViD-19 pandemic. High individual compliance with social distancing could slow down the spread of CoViD-19 to a level that existing intensive care capacities can cope with. Relaxing strict social distancing measures and a shift to smart distancing inevitably increases the spread of CoViD-19. Nonetheless, depending on which levels of containment are deemed acceptable, some sets ofsmart distancing measures prove sufficient in the model.
A key advantage of simulation models is that they can be adapted to new situations quickly and thus currently discussed proposals to contain the epidemic can be evaluated. However, when interpreting results, one should keep in mind that simulations are based on assumptions and their empirical adequacy. This limitation applies to the medical and epidemiological parameters employed, but also to assumptions about the behaviour of individuals and their interaction.
For the longer version and the interactive computer simulation, see
If you are interested, the following literature reference will give you an insight into further application possibilities and limitations of simulations:
- Klein, Dominik, Marx, Johannes, and Fischbach, Kai (2018): Agent-Based Modeling in Social Science, History, and Philosophy. Historical Social Research. Vol. 43: 1, pp. 234-258. Doi: 10.12759/hsr.43.2018.1.7-27
There are already isolated agent-based models that deal with the dynamic development of CoViD-19.
- A well prepared and easily accessible simulation on this topic can be found on the website of the Bavarian Broadcasting Corporation: https://web.br.de/interaktiv/corona-simulation/
- The Washington Post article on this topic and the simulation presented in it also offers a good introduction: https://www.washingtonpost.com/graphics/2020/world/corona-simulator/