Understand the spread of infectious diseases
Scientists around the world are working on research on infectious diseases after the global epidemic COVID-19 disease caused by a new coronavirus SARS-CoV-2. This applies not only to virologists, but also to physicists who develop mathematical models to describe the spread of epidemics. Such models are important for testing the effects of various measures designed to prevent the disease – face masks, closing public buildings and businesses, and as one of the acquaintances. These models often play a key role in political decisions and emphasize the basis of all measures taken.
Physicists Michael te Vrugt, Jens Bickmann and Prof. from the Institute of Theoretical Physics and the Center for Soft Nanology Sciences. Rafael Wittkowski University of Münster developed a new model showing the spread of infectious diseases. A working group led by Rafael Wittkowski is studying Statistical Physics, a description of systems consisting of a large number of particles. Physicists also use Dynamic Density Functional Theory (DDFT), a method developed in the 1990s that allows them to describe interacting particles.
At the beginning of the corona pandemic, they realized that the same method was useful for explaining the spread of disease. “In principle, people observing social distance can, for example, be modeled as particles pushing each other because they have the same electric charge,” explains author Michael te Vrugt. “Thus, theories describing particles pushing each other can be applied to people who maintain their distances from each other,” he said. Based on this idea, they developed the SIR-DDFT model, which combines the SIR model (a popular theory describing the spread of infectious diseases) with DDFT. Outcome theory describes people who can infect each other but keep their distance. “The theory also allows us to describe hotspots with infected people, which improves our understanding of the dynamics of super-spreading events such as the Heinsberg carnival celebrations earlier this year or the apres-ski in Ischgl,” he says. – author Jens Bickmann. The results of the study were published in the journal Nature Communication.
The degree of social alienation applied is then determined by the strength of the pushing interactions. “As a result,” explains research leader Rafael Wittkowski, “this theory can also be used to test the effects of social alienation by simulating an epidemic and changing values for parameters that determine the strength of the interaction.” Simulations show that infection rates actually decrease significantly as a result of social alienation. The model thus reflects the familiar “curve correction” effect, in which the curve, which reflects the development of the number of infected people over time, becomes more flattering as a result of moving away from social distance. Compared to existing theories, the new model has the advantage that it is possible to openly model social interactions.
Reference: “The effect of social distance and isolation on epidemic prevalence modeled by the functional theory of dynamic density” Michael te Vrugt, Jens Bickmann and Rafael Wittkowski, 4 November 2020, Nature Communication.
DOI: 10.1038 / s41467-020-19024-0
Funding: The Wittkowski Working Group is funded by the German Research Foundation (DFG, WI 4170 / 3-1).