Understanding how infectious diseases spread

Scientists around the world have been working hard on infectious disease research in the wake of the global outbreak of COVID-19, caused by the novel SARS-CoV-2 coronavirus. This concerns not only virologists, but physicists as well, who are developing mathematical models to describe the spread of epidemics. Such models are important for testing the effects of various measures designed to contain the disease – such as face masks, closure of public buildings and businesses, and family social distancing. These models often serve as a basis for political decisions and underline the justification of the measures taken.

Physicists Michael te Vrugt, Jens Bickmann and Prof. Raphael Wittkowski of the Institute of Theoretical Physics and the Center for Soft Nanoscience of the University of Münster have developed a new model showing the spread of infectious diseases. The working group led by Raphael Wittkowski is studying Statistical Physics, or the description of systems made up of a large number of particles. In their work, physicists also use Dynamic Density Functional Theory (DDFT), a method developed in the 1990s that allows you to describe interacting particles.

At the start of the corona pandemic, they realized that the same method is useful for describing the spread of disease. “In principle, people who observe social distances can be modeled as particles that repel each other because they have, for example, the same electrical charge,” explains lead author Michael te Vrugt. “So maybe the theories describing particles repelling each other could be applicable to people keeping their distance from each other,” he adds. Based on this idea, they developed the so-called SIR-DDFT model, which combines the SIR model (a well-known theory describing the spread of infectious diseases) with DDFT. The resulting theory describes people who can infect each other but who keep their distance. “The theory also makes it possible to describe hotspots with infected people, which improves our understanding of the dynamics of so-called super-spreader events earlier this year such as carnival celebrations in Heinsberg or après-ski in Ischgl,” adds co-author Jens Bickmann. The results of the study were published in the journal Nature Communications.

The extent of social distancing practiced is therefore defined by the strength of the repulsive interactions. “Consequently,” explains Raphael Wittkowski, the study leader, “this theory can also be used to test the effects of social distancing by simulating an epidemic and varying the values ​​for the parameters that define the strength of interactions.” Simulations show that infection rates actually show a marked decrease which is the result of social detachment. The model thus reproduces the familiar “flattening of the curve” effect, in which the curve representing the trend in the number of infected people over time becomes much flatter due to social distancing. Compared to existing theories, the new model has the advantage that the effects of social interactions can be modeled explicitly.

Reference
te Vrugt M, Bickmann J, Wittkowski R. Effects of social distancing and isolation on epidemic spread modeled by functional theory of dynamic density. Nature Communications. 2020; 11 (1): 5576. doi: 10.1038 / s41467-020-19024-0

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