Mislav Kerner is a Business Intelligence Consultant at Koios Consulting, based in Zagreb, Croatia. He gained experience by working in Risk Management industry as well as working on Data Science and Product Development projects at Koios. The main interest is related to the fintech industry and applications of Data Science and Big Data technologies in modeling and predicting as well as automation and optimization of business processes in large corporations. He has a master’s degree in Financial Mathematics and Business, obtained at the Faculty of Science, University of Zagreb. Prior to joining Koios at the beginning of 2020, Mislav has been working as Market and Liquidity Risk Analyst at Croatia’s leading bank, Zagrebačka banka, part of UniCredit Group.
Modelling the COVID19 Pandemic Cases and Mobility Data
In this presentation we will show a Data Science project in which we researched and modelled the Coronavirus spreading in society during March and April. Due to a significant amount of asymptomatic or mild conditions cases which haven’t been officially registered, we were interested in the true number of COVID19 cases as well as predicted epidemic outcomes. It was mainly performed by implementing and extending the epidemiological model SEIR as well as using some other, simpler methods.
The main part of modelling was the estimation of the virus reproduction rate which plays the key role in epidemic development. Also, we tried to find a statistical connection between the decrease of people’s mobility (measured by Google mobility data) and decrease of coronavirus reproduction rate.