Transforming the World
The Berlin Mathematics Research Center MATH+ and its network of collaborators support the science community with COVID-19-related research activities from across all fields. Thus responding to the current pandemic and its scientific challenges. We set up this website to provide an overview of all research projects concerning the coronavirus that include MATH+ members. Most of the research projects presented here also reflect the approach of MATH+ of working in interdisciplinary and trans-institutional groups.
COVID-19 caused by the novel virus SARS-CoV-2 (severe acute respiratory syndrome Coronavirus 2) is currently spreading across the globe. The first confirmed COVID-19 case hit Berlin on 1 March 2020 and was treated at Charité Berlin Campus Virchow. Our objective is to track and monitor infections for Berlin, Germany as well as model predictions using data-based and model-based approaches.
In early 2020 COVID-19 turned into a global pandemic. Non-pharmaceutical interventions (NPIs), including the isolation of infected individuals, tracing and quarantine of exposed individuals are decisive tools to prevent onwards transmission and curb fatalities. Strategies that combine NPIs with SARS-CoV-2 testing may help to shorten quarantine durations while being non-inferior with respect to infection prevention. Thus, combined strategies can help reducing the socio-economic burden of SARS-CoV-2 and increase public acceptance. We developed a software that enables policy makers to calculate the reduction in transmissibility through quarantine or isolation in combination with arbitrary testing strategies. This tool is freely available from: https://github.com/CovidStrategyCalculator/CovidStrategyCalculator
We build an integrated geolocalized and demographically referenced spatio-temporal stochastic network- and agent-based model of COVID-19 dynamics for individual human encounters in real-world communities.
It enables analysis of different intervention scenarios such as school closures or physical distancing in European communities. Based on the developing network, we study the impact of stochasticity on infection spreading and identify the importance of early introduction of test-trace measures.
We identify and design drug molecules for preventing cell entry of the novel Coronavirus SARS-Cov-2 into human cells.
The project MODUS-Covid investigates the infection dynamics of the SARS-CoV-2 in Germany. Using data-based movement patterns it is possible to simulate the infection dynamic for certain areas under different restrictions. Also the developing of new mathematical methods for fast and accurate calculations of the possible epidemic spread and the estimation of the potential success of introduced measures in suppressing the virus are an important part of the project.
Within this BMBF project, together with our collaborators, we are studying human mobility models for COVID-19 spreading in Berlin. The research groups at Zuse Institute Berlin (Prof. Schütte, Prof. T. Conrad, Dr. N. Conrad) are working on developing new mathematical methods for fast and accurate calculations of the possible epidemic spread and estimate the potential success of introduced measures in suppressing the virus.
In our MATH+ Project EF5-1 “Spreading of copper technology in ancient times”, we are developing new mathematical frameworks for analyzing the spatio-temporal process of innovation spreading in ancient Europe and modelling possible scenarios inferring which influences had an effect on the spreading dynamics. We have extended these approaches to studying the spreading of COVID-19 in Germany, by introducing the infection dynamics on different spatio-temporal scales.
We applied optimal control theory to an epidemic compartmental model tailored to specific aspects of COVID-19.
The model has been calibrated to reproduce the early phase of the COVID-19 pandemics in Germany. We computed the optimal mitigation strategy (temporal course of mean contact reduction) to control the disease with purely non-pharmaceutical interventions. Our numerical solution indicates that achieving herd immunity via natural infections is either extremely dangerous or expensive.
The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. All over Europe, governments discuss and decide about far-reaching counter-measures like shutdowns of economic activity and public life.
These decisions are taken facing conflicting objectives: some objectives, like the minimization of disease-related deaths, demand for strong counter-measures, while others, such as social and economic costs, require less restrictive interventions. Finding the optimal compromise boils down to solving a multi-objective optimization problem. We demonstrate how to find a mathematical description of this problem based on real-word data, and how to solve it numerically. The solution consists of a set of several optimal strategies, from which political decisions makers should select. The theory is complemented by application to counter-measures against covid-19 spreading in Berlin.
The analysis and prediction of the spread of infections such as the coronavirus are characterized by numerous uncertainties, for example, regarding spatial clustering or the individual transmission rate of individuals. Within the MATH+ Project EF4-1: Influence of Mobility on Connectivity, researchers of the group Interacting Random Systems at Weierstrass Institute Berlin are investigating corresponding stochastic epidemic models on random networks.
In a short video, we explain the math behind exponential growth and social distancing.