Lucija Mihalj is a Junior Data Scientist at Koios Consulting, based in Zagreb, Croatia. She earned a Bachelor of Science in Mathematics and a Master of Mathematical Statistics at the Faculty of Science, University of Zagreb. After graduation, she joined Koios Consulting where she has gained experience by working on various Data Science and Product Development projects. She has a strong interest in data science and machine learning, as well as fintech industry.
Modelling the COVID19 Pandemic Cases and Mobility Data
In this presentation we will present 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.