20.500.12556/RUL-136271 COVID-19 in Slovenia, from a success story to disaster what lessons can be learned? 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. COVID-19 modeling epidemics Bayesian inference discrete renewal process nonpharmaceutical interventions reproduction number COVID-19 modeliranje epidemij Bajesovo sklepanje true false true Angleški jezik Slovenski jezik Članek v reviji 2022-04-21 14:43:16 2022-04-21 14:43:19 2022-09-15 04:13:40 0000-00-00 00:00:00 2021 0 0 23 str. iss. 10, art. 1045 Vol. 11 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616.9 2075-1729 10.3390/life11101045 79132675 life-11-01045-v4.pdf life-11-01045-v4.pdf 1 D72E8C28367A4A148D37F77308A0CD64 2bcc1736ba16e10d0d1b0f97ccd9964d6084a342f75f64486abfb85ed2174feb c1b3604a-c170-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=155824 https://www.mdpi.com/2075-1729/11/10/1045 1 8e12653f-c170-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=155823 Medicinska fakulteta Fakulteta za šport 0 0 0