No. 43




Data-Based Support for Corona Crisis Management in Estonia

09 June 2021


RiTo No. 43, 2021

  • Krista Fischer

    Krista Fischer

    Member of the Academy; University of Tartu, Professor of Mathematical Statistics, Associate Professor of Biostatistics

  • Mario Kadastik

    Mario Kadastik

    National Institute of Chemical Physics and Biophysics, Senior Researcher, Deputy Director

The global COVID-19 pandemic truly started to affect Estonia in the beginning of March 2020, when the first local cases of the virus were diagnosed. To support the Government of the Republic in managing the crisis, the Prime Minister formed the COVID-19 Scientific Advisory Board, whose key task is to regularly analyse the data on the spreading of the virus, and compile data-based reports and forecasts.

The article summarises the used data analysis methods and outlines the development of the epidemic in Estonia from March 2020 until April 2021. First we describe the data visualisation method that allows us to follow the spreading of the virus in time, its regional differences, and spreading dynamics in different age groups. We would like to point out that the multidimensionality of the data requires particular care in differentiating between relative and absolute indicators. For example, the number of infections per 100,000 individuals over a 14-day period is suitable for a rough estimate, but we must remember that its value of 200 corresponds to less than 20 infections in the smallest Estonian county, Hiiu County, over the same period.

When describing the infection dynamics, it is important to understand the concept of infection rate R, which describes whether the infection trend is on the rise or falling, and which is very suitable for describing the effect of the measures on the spreading of the infection. Considering different scenarios for rate R dynamics, we can forecast the level of infections in the near future. However, one of the key tasks in data analysis has been the prediction of hospital and intensive care patient load, and the possible mortality rate. In order to fulfil this task, we need the most precise estimations possible for a number of input parameters, including the likelihood of need for hospital and intensive care, the length of hospital and intensive care stay (and the probability distribution of the relevant time periods), mortality rate, and death timeline. We have assessed these parameters and also modified these according to the need, specifically using Estonian data because international data might not describe our situation adequately.

The described analysis was led by the two authors of the article: a mathematician-biostatistician and a particle physicist. We learned first-hand that it was our fundamental STEM background that enabled us to quickly identify suitable approaches in a new situation that neither of us had encountered before. We have therefore concluded that supporting STEM education should be an important priority for the government, as it provides the necessary competence that could come useful in a variety of crisis situations which are still difficult to foresee.