Coronavirus disease 2019 (COVID-19) is an infectious disease which has started spreading in Wuhan, China in November 2019. In 11 March 2020, the World Health Organization announced that
the disease could be characterized as a pandemic.

On both clinical and non-clinical aspects, a large amount of scientific research has been conducted on different aspects of the new coronavirus disease. Our interest focuses on the non-clinical research, in particular, the mathematical modeling of the disease spread taking into account network effects and related operational problems. Although classical approaches such as Susceptible-Infectious-Recovered models are very helpful for understanding the vital characteristics of an epidemic, they usually ignore the network structure of the underlying population. However, the network topology may effect the spread significantly. For example, it is known that disease spreads faster in small-world networks than in many other network structures. Moreover, taking into account the underlying network structure can be crucial for effective setting of preventive measures such as isolation and vaccination, as the could help to identify potential superspreaders of a pandemic.

The main goal of this project is to develop mathematical programming-based models for preventing disease spread taking into account network effects and proposing efficient exact solution approaches to solve them. Since our developed solution approaches will provide solutions together with performance guarantees they can help to improve the acceptance of and the trust on such software systems by decision makers in the sense of explainable AI.