COVID-19 in Slovenia, from a success story to disaster : what lessons can be learned?
ID Ružić Gorenjec, Nina (Author), ID Kejžar, Nataša (Author), ID Manevski, Damjan (Author), ID Pohar Perme, Maja (Author), ID Vratanar, Bor (Author), ID Blagus, Rok (Author)

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During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the ‘dance’ strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15–20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs.

Keywords:COVID-19, modeling epidemics, Bayesian inference, discrete renewal process, nonpharmaceutical interventions, reproduction number
Work type:Article (dk_c)
Typology:1.01 - Original Scientific Article
Organization:MF - Faculty of Medicine
FŠ - Faculty of Sport
Publication status in journal:Published
Article version:Publisher's version of article
Number of pages:23 str.
Numbering:Vol. 11, iss. 10, art. 1045
PID:20.500.12556/RUL-136271 This link opens in a new window
ISSN on article:2075-1729
DOI:10.3390/life11101045 This link opens in a new window
COBISS.SI-ID:79132675 This link opens in a new window
Publication date in RUL:21.04.2022
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Record is a part of a journal

Shortened title:Life
COBISS.SI-ID:519982617 This link opens in a new window


License:CC BY 4.0, Creative Commons Attribution 4.0 International
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:04.10.2021

Secondary language

Keywords:COVID-19, modeliranje epidemij, Bajesovo sklepanje

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project number:N1-0035
Name:Izboljšanje napovedovanja redkih dogodkov

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project number:P3-0154
Name:Metodologija za analizo podatkov v medicini

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project number:J3-1761
Name:Število izgubljenih let kot mera bremena bolezni

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