The contribution of epidemiological models to the description of the outbreak of the COVID-19 pandemic

After the global outbreak of the COVID-19 pandemic, an infection dynamic of immense extent developed. Since then, numerous measures have been taken to bring the infection under control. This was very successful in the spring of 2020, while the number of infections rose sharply the following autumn. To predict the occurrence of infections, epidemiological models are used. These are in principle a very valuable tool in pandemic management. However, they still partly need to be based on assumptions regarding the transmission routes and possible drivers of the infection dynamics. Despite numerous individual approaches, systematic epidemiological data are still lacking with which, for example, the effectiveness of individual measures could be quantified. Such information generated in studies is needed to enable reliable predictions regarding the further course of the pandemic. Thereby, the complexity of the models could develop hand in hand with the complexity of the available data. In this article, after delineating two basic classes of models, the contribution of epidemiological models to the assessment of various central aspects of the pandemic, such as the reproduction rate, the number of unreported cases, infection fatality rate, and the consideration of regionality, is shown. Subsequently, the use of the models to quantify the impact of measures and the effects of the test-trace-isolate strategy is described. In the concluding discussion, the limitations of such modelling approaches are juxtaposed with their advantages.

Automated summary

Epidemiological models play an important role in global pandemic management, contributing to the assessment of key parameters and the impact of measures, but they still have limitations such as lack of systematic epidemiological data and reliance on assumptions for some model inferences. Despite this, these models remain valuable for evaluating the pandemic's important aspects.