COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data

Reza S. Abhari; Marcello Marini; Ndaona Chokani

Utilizing publicly available data, a holistic bottom-up agent-based simulation of the current COVID-19 pandemic in Switzerland is presented as a reference case. The initiation, growth and containment of the COVID-19 spread in presented, and quantified in terms of the infected (symptomatic and asymptomatic), recovered and deaths. Using the same simulation tool, it is shown that without social adaptation and governmental intervention, an explosive spread of the COVID-19 virus would have resulted in an infection of 42.7% of the entire population by 25 April 2020; on the otherhand the government's timely intervention resulted in less than 1% of the population being infected for the examined time period. This shows that it is critical for goverments to step in, at an early stage to contain and manage pandemics and minimize mortality rates in the coming months. As restrictions become less prevelant, the infection rate and the associated mortality will undoubtedly increase.