Quantifying direct and indirect impacts of COVID-19 vaccination program in Slovenia in 2021: a retrospective modelling study

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Mathematical modelling offers tools to estimate both direct and indirect impacts on mortality and healthcare burden, thereby informing vaccination strategies. This study retrospectively assesses the impact of COVID-19 vaccination on hospitalizations, intensive care units (ICU) admissions, and deaths in Slovenia in 2021, employing an extended Susceptible-Exposed-Infected-Recovered model (SEIR). Methods: We utilized Slovenian COVID-19 national surveillance data from January to December 2021, encompassing cases, vaccination coverage, hospitalizations, ICU admissions, and deaths, to fit an age-stratified extended SEIR model. This model incorporates compartments for both vaccinated and unvaccinated populations and comprises five models, linked by a mixing matrix, collectively modelling the epidemic dynamics of 5 age groups: 0-24, 25-44, 45-64, 65-74, and over 75. This allows for exploration of vaccine impact across scenarios with varying vaccination coverage by age groups. Results: With 56% overall coverage in 2021, vaccination alone led to a 70% decrease in hospitalizations (2,507 vs. 752 per 100,000 population), a 74% decrease in ICU admissions (538 vs. 138 per 100,000 population) and a 70% decrease in COVID-19-related deaths (461 vs. 140 per 100,000 population). A population-wide vaccination strategy led to an additional 29% decrease in deaths in the over 65 years old group compared to a scenario vaccinating this high-risk group only. Conclusions: The COVID-19 vaccination program in Slovenia significantly reduced the burden of the pandemic in terms of preventable deaths. Support for national and EU surveillance systems is needed to improve modelling capacities, which are essential to guide public health interventions. When relevant, modelling the indirect effects of vaccination on the unvaccinated population regarding disease transmission is relevant for prioritizing age groups for vaccination. Further research on social mixing patterns is warranted to improve such estimates. air-borne infections coronavirus disease (COVID-19) epidemiology modelling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Slovenia vaccines and immunisation viral infections Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease (COVID-19) pandemic, has led to 776 million confirmed infections and more than 7 million deaths worldwide as of October 31, 2024 [ 1 ]. Approved vaccines against SARS-CoV-2 have significantly changed the course of the pandemic and saved tens of millions of lives worldwide [ 2 – 5 ]. The COVID-19 vaccination campaign in Slovenia began on December 27, 2020, shortly after the first vaccines were approved. By June of the following year, over 50% of Slovenians aged 65 years and older had received a full primary vaccination, and by the end of October 2021, the same vaccination coverage rate was achieved for the population aged over 18 years [ 6 ]. Figure 1 shows the number of confirmed SARS-CoV-2 infections and the spread of vaccination in Slovenia during the COVID-19 pandemic in 2021, also indicating periods with non-pharmaceutical interventions and dominant SARS-CoV-2 variants. Genomic surveillance of SARS-CoV-2 in Slovenia in 2021 showed an increase in the Alpha variant, which became predominant in March, while Delta variant became predominant in July [ 7 ]. Despite the success of the COVID-19 vaccination campaigns, vaccine confidence has suffered a significant decline since the onset of the pandemic [ 8 ]. The age gap in vaccination confidence widened between 2018 and 2022 in almost every European Union (EU) member state with over 65s becoming more confident and 18-34-year-olds growing less confident on the safety and importance of the measles, mumps and rubella vaccines [ 9 ]. In the 2023/2024 season, the median COVID-19 vaccination coverage in the EU among people aged 60 years and above was 14% (range: 0.02–66.1%) [ 10 ], while in Slovenia it reached 6.3% among those aged over 65 years [ 11 ]. Transparent and valid information on the risks and benefits of vaccines is important to increase confidence and tackle vaccine hesitancy [ 12 ]. Estimating the isolated direct and indirect impact of COVID-19 vaccination is challenging due to the complex disease dynamics associated with the simultaneous implementation of non-pharmaceutical interventions, the emergence of new virus variants and the varying vaccine effectiveness. Epidemiological mathematical modeling provides valuable tools to address such complexities and better understand infectious disease transmission dynamics and the impact of public health interventions [ 13 – 16 ]. Modeling has gained in relevance during the last pandemic, providing forecasting scenarios to guide the response, exploring the impact of different hypothetical vaccination rollout scenarios, and helping to identify parameters that affect epidemics, such as vaccine equity [ 5 , 14 , 17 , 18 ]. Compartmental models were found to be ideal for long-term analysis, data fitting, and identifying key parameters that affect epidemics [ 19 ]. Recent well-conducted cross-national surveillance studies and modeling of the impact of vaccination provide valuable insights into quantifying vaccination effects in terms of averted deaths [ 3 , 4 , 20 ]. Severe cases of COVID-19 continue to place a major burden on healthcare capacities and are often driving public health response and population risk perception, as the availability of hospital and intensive care units (ICU) beds are often the bottleneck that necessitates the implementation of more stringent non-pharmaceutical epidemic control measures in the population. Severe acute respiratory infection (SARI) surveillance studies provide valuable insights into the efficacy of vaccines and their direct impact on severe disease [ 21 , 22 ]. When data are available, well fitted compartmental modeling approaches can estimate both mortality and healthcare burden. By accounting for disease transmission dynamics, such modeling approaches can provide estimates for direct vaccination effects on the vaccinated individuals and also indirect effects of vaccination on the broader, unvaccinated community. The aim of our study was to retrospectively assess the direct and indirect impact of COVID-19 vaccination on hospitalizations, ICU admissions and deaths in Slovenia in 2021 using an extended Susceptible-Exposed-Infected-Recovered (SEIR) model, in order to provide detailed country estimates and a modeling approach that can serve as a basis for strategic planning. Methods Input data and study period In our study, we used Slovenian anonymized, open-access daily data on laboratory-confirmed COVID-19 cases, COVID-19-related deaths and vaccination coverage from the Slovenian National Institute of Public Health, published on the open-data portal OPSI [23]. Data on hospitalizations and ICU admissions were collected as part of a comprehensive ad-hoc surveillance system to monitor national healthcare capacities during the pandemic, set up by the Ministry of Health with the support of COVID Tracker Slovenia [24]. The study period was the entire year 2021. Vaccination scenarios We estimated the impact of vaccination in the time periods studied by considering six vaccination rollout scenarios with varying vaccination coverage in age groups, as described below and illustrated in Figure 2. In scenario S0, vaccination coverage followed the actual rollout in 2021 in Slovenia and reached 56% population coverage by the end of the year. Scenario S1 assumes that no vaccination has taken place. Scenario S2 assumes vaccination coverage 0.7 times lower than the actual vaccination coverage (S0) in the entire population and throughout the year, reaching population coverage rate of 39% by the end of 2021. Scenario 3 (S3) assumes a vaccination coverage 1.3 times higher than in S0 in the entire population and throughout the year and reaches 71% population coverage by the end of 2021. Scenarios 4 and 5 (S4 and S5) assume that only people over the age of 65 are vaccinated, in S4 with the actual vaccination rates of 2021, and in S5 with vaccination rates 0.7 times lower than the actual vaccination rates of 2021. Vaccination coverage was modeled according to five age groups: 0-24, 25-44, 45-64, 65-74, and over 75 years old. Figure 2 shows the vaccination course for all six scenarios, divided into age groups, and Table 1 shows the vaccination rates at the end of 2021. Table 1 : Cumulative vaccine uptake by individual age groups as a percentage of the respective group according to scenarios S0 to S5 at the end of the year 2021. Age Groups S0 [%] S1 [%] S2 [%] S3 [%] S4 [%] S5[%] 0-24 21.1 0 14.8 30.6 0.0 0.0 25-44 52.3 0 36.7 73.3 0.0 0.0 45-64 68.3 0 47.8 85.3 0.0 0.0 65-74 83.3 0 58.3 95.7 83.3 58.3 over 75 89.0 0 62.4 95.8 89.0 62.4 Overall 56.1 0 39.3 70.9 17.7 12.5 Scenario modeling The vaccination scenarios were modeled using an extended age-stratified SEIR compartmental model developed to predict the epidemiological situation in Slovenia during the COVID-19 pandemic. The model is illustrated in Figure 3, while fully described and extensively evaluated in Fošnarič et al. [25]. The model was also part of the European COVID-19 Forecast Hub (26). The structure of the base model includes compartments for modeling vaccinated and unvaccinated populations. Vaccination uptake (VU) is integrated into the model as a function of time (indicated with blue color on Figure 3) to divide the model compartments into vaccinated and unvaccinated sub-compartments. Vaccine effectiveness (VE) is included in the model (indicated with green color on Figure 3) to reduce the rates of infection, hospitalizations, ICU admissions, and deaths based on published estimates [27,28]. The base model is extended to five models linked by a mixing matrix W(t) (indicated with orange color on Figure 3), all together modeling the epidemic dynamics of 5 age groups in the population: 0-24, 25-44, 45-64, 65-74, and over 75. Assessment of the model We calibrated the model using actual data for the year 2021, which corresponds to scenario S0. The model parameters were estimated using actual data on the number of cases, hospitalizations and ICU admissions, COVID-19-related deaths and actual vaccination rates for five age groups separately over the entire duration of the epidemic in Slovenia. The calibration process is described in detail in Fošnarič et al. [25]. The calibrated model was then used to simulate the other five hypothetical scenarios by replacing the time series functions for vaccine uptake according to the described scenarios, while leaving all other parameters of the model unchanged. The resulting simulations were then compared in terms of hospital and ICU admissions, number of hospitalized persons, and COVID-19-related deaths in both the total population and the population over 65 years of age. Results The estimates of the six different vaccination scenarios in terms of hospital and ICU stays and deaths related to COVID-19 are shown as functions of time in Figure 4. The total numbers of hospitalizations, ICU admissions and deaths in the six scenarios are presented in Table 2. In the scenario without vaccination (S1), the number of ICU stays would have peaked in September 2021 with almost 3,000 people requiring intensive care (143 per 100,000 population). The number of deaths would also have peaked in September 2021 at almost 200 deaths per day. Vaccination alone, when compared to scenario S0, prevented 36,909 hospitalizations (1,755 per 100,000 population), 8,388 ICU admissions (400 per 100,000 population), and 6,741 deaths (321 per 100,000 population). Since the majority of the total burden is expected on the population over 65 years of age, the total numbers of hospitalizations, ICU admissions and deaths are shown in Table 2 for the total population and separately for the age groups over 65 years old. Table 2 : The total numbers of hospitalizations, ICU admissions and deaths due to COVID-19 in Slovenia in 2021 for six different vaccination scenarios: S0 - actual vaccination; S1 - no vaccination; S2 – 0.7 times actual vaccination; S3 – 1.3 times actual vaccination; S4 - actual vaccination of the population over 65, all younger unvaccinated; S5 – 0.7 times actual vaccination of the population over 65, all younger unvaccinated. The numbers per 100,000 are calculated by using the population of 2.1 million in Slovenia in 2021. Total S0 S1 S2 S3 S4 S5 Hospital admissions (all) 15,803 52,712 26,088 8,770 29,350 37,343 Hospital admissions (over 65) 10,308 37,998 17,847 6,681 15,393 22,994 ICU admissions (all) 2,901 11,289 5,472 1,492 6,028 7,813 ICU admissions (over 65) 2,008 8,259 3,724 1,204 3,136 5,124 Deaths (all) 2,945 9,686 5,291 1,766 4,685 6,375 Deaths (over 65) 2,548 8,342 4,466 1,692 3,589 5,222 Per 100,000 people Hospital admissions (all) 752 2,507 1,241 417 1,397 1,777 Hospital admissions (over 65) 490 1,808 849 318 732 1,094 ICU admissions (all) 138 538 261 71 287 372 ICU admissions (over 65) 96 393 177 57 149 244 Deaths (all) 140 461 252 84 223 303 Deaths (over 65) 121 397 212 80 171 248 Discussion In our study, we presented the first country-specific estimates of the impact of COVID-19 vaccination in Slovenia. We used a well-fitted extended SEIR model to assess averted hospitalizations, ICU admissions and deaths and to quantify the role of COVID-19 vaccination programs in reducing the burden of disease and safeguarding limited healthcare capacity. With such a data-intensive, well-fitted modelling approach, we were able to retrospectively model the complex country epidemic dynamics and explore different vaccination scenarios in a quantifiable way by changing a single model parameter, in our case the vaccination coverage rate. Our comparison of the actual vaccination scenario in Slovenia (S0) with a scenario without vaccination (S1) showed that the COVID-19 vaccination program in Slovenia led to a percentage decrease in hospital admissions by 70% (2,507 vs. 752 per 100,000 population), ICU admissions by 74% (538 vs. 138 per 100,000 population) and COVID-19-related deaths by 70% (461 vs. 140 per 100,000 population). If the scenario without vaccination (S1) had occurred, the Slovenian healthcare system would have exhausted all its available resources. The primary bottleneck of the Slovenian healthcare system was the capacity of intensive care units, which in times of pandemic were able to provide care for around 300 people requiring intensive care at once. It is important to note that in this scenario, stricter non-pharmaceutical disease control measures (e.g. lockdowns) would have been necessary to prevent the collapse of the healthcare system and the associated casualties. Without additional public health measures in S1, there would have been almost 200 deaths per day at the height of the epidemic. A 30% lower vaccination coverage rate than the actual (scenario S2), with a maximum vaccination coverage rate of 39%, which is in line with the global World Health Organization (WHO) vaccination target for 2021, would have led to a prolonged epidemic peak, with ICU admissions continuing to exceed maximum capacity, resulting in 261 ICU admissions per 100,000 population. Scenarios S2 and S3 examined the impact of a 30% positive and negative shift from the actual vaccination coverage (i.e., 71% and 39% of the overall population coverage, respectively) and provided evidence of the importance of activities and resources invested in improving population trust and coverage during the pandemic and beyond. It can be assumed that the number of cases (delta wave) seen in Slovenia at the end of 2021 could have been avoided if Slovenia had achieved vaccination coverage as in scenario S3, which was achieved by other EU countries such as Germany, France, or the Netherlands (26). As far as we are aware, this is the first published assessment of the impact of vaccination in Slovenia using a modeling approach that takes into account direct and indirect effects, different disease courses in terms of infections, hospitalizations, ICU admissions and deaths, and differentiation of the population by age and vaccination status. A similar model was used to plan the vaccination strategy against COVID-19 in the USA [ 14 ]. The results of a large multi-country study estimating deaths directly prevented by COVID-19 vaccination for the period December 2020 to March 2023 showed a percentage decrease of 45% for Slovenia (from 602 to 333 deaths per 100,000 population) [ 4 ]. Our estimates for 2021 alone show a percentage decrease of 70% (from 461 to 140 deaths per 100,000 population). Another large multi-country study by Mesle et al., which estimated the number of deaths directly prevented by COVID-19 vaccination in the WHO European Region from December 2020 to November 2021 in people aged 60 years and older [ 3 ], found a percentage decrease of 38% in deaths in Slovenia due to vaccination. In comparison, our results for the population aged 65 years and older showed a percentage decrease of 69% in 2021 (from 397 to 121 deaths per 100,000 population). Although a direct comparison of these results is not possible mainly due to the different time periods, age groups, varying SARS-CoV-2 epidemiology and vaccine effectiveness, we believe that our modelled estimates of the vaccine impact add value as they use additional and more detailed input data to describe the epidemic in Slovenia. Overall, our extended SEIR modelling approach also complements surveillance-based approaches with estimates of hospital burden and the ability to explore different scenarios, which can provide valuable insights when healthcare capacity determines the implementation of public health interventions. The higher impact of vaccination on prevented deaths observed in our study may be due in part to the fact that our estimates also include indirect vaccination effects. As mentioned above, compared to other approaches, our model takes into account the indirect effects of vaccination on unvaccinated individuals with regard to disease transmission [ 29 ]. Theoretically, there are a number of situations in which the indirect benefit of vaccination outweighs the direct benefit, particularly at low vaccination rates (e.g., 20%) and intermediate values of the basic reproduction number (1–1.5), which in some cases are more than 400% of the direct benefit [ 30 ]. We can estimate the indirect impact of vaccinating the general population on the higher risk group of over 65-year-olds by comparing the actual vaccination in Slovenia (scenario S0), where the general population was by end of the year 2021 vaccinated at an overall coverage rate of 56%, with scenario S4, in which it is assumed that only the over 65-year-olds are vaccinated (at the actual age-specific coverage rates of around 80%). A comparison between scenarios S4 and S0 shows that the actual vaccination rates for the under-65 population indirectly impacted the over-65 population by additionally reducing their hospital admissions by 33% (731 to 490 per 100,000), ICU admissions by 35% (149 to 96 per 100,000 population) and deaths by 29% (171 to 121 per 100,000). Given the evolution of SARS-CoV-2 variants and the reduced impact of current vaccines on disease transmission, vaccination strategies focus on maximising the benefit of COVID-19 vaccination for the most vulnerable individuals and older adults who are vulnerable (e.g., > 60 years old) [ 31 ]. Scenario S5 explores such a vaccination rollout, targeting only the population over 65 years of age, with a relatively high vaccination coverage rate of around 60%. Nevertheless, it remains important to better understand and quantify the indirect effects of vaccination on disease transmission, and when relevant to consider these findings when developing vaccination strategies. Our model estimates could be improved by better modelling of population mixing behavior [ 32 ]. and could be used in the future to explore different vaccination strategies for different age groups, which may become relevant again with the advent of new vaccines. Country-specific studies such as the one conducted by Backer et al. provide insights into social mixing patterns that would allow better quantification of the indirect effects of vaccination in different age groups and should be conducted regularly [ 33 ]. Our study has limitations that need to be considered. The data-intensive requirements of the model need to be considered in terms of post-pandemic surveillance capacity. After the pandemic, priorities in Slovenia have shifted and some ad hoc surveillance systems for monitoring hospitalizations and bed capacities have been discontinued. As SARI surveillance plays a more important role, we advocate for the continued support of surveillance capacities at national and EU level that are able to provide open data for complex and granular modeling approaches, which are essential for guidance and evaluation during the next public health crisis. Although in 2021 the alpha variant of SARS-CoV-2 was replaced by the delta variant, we present results for the entire year. The model parameters were tuned to account for the changes in epidemiology caused by the new variant. Another limitation of our model is that it only partially quantifies the complex indirect effects of vaccination. As we have already discussed the behavioral patterns of the population, it is important to point out that the effects related to protecting healthcare capacity by limiting the impact of overcrowded ICUs or, in extreme cases, the need for triage, have not been considered, which likely leads to an underestimation of the impact. To further assess the validity of our model estimates, we compared our model results for COVID-19 hospitalizations and ICU admissions in S0 with available data from the Slovenian surveillance systems for SARI, which were not part of the model input data [ 34 ]. These comparisons showed that our model results were consistent with the independent surveillance data from the SARI surveillance system: modeled COVID-19 hospitalizations 15,803 vs. 11,750 and modeled ICU admissions 2901 vs. 2456. The COVID-19 vaccination program in Slovenia has significantly reduced the burden of the pandemic in terms of deaths and healthcare access needs. Our data-driven approach, using a SEIR-type model with remarkable data adherence, provides a complementary way to retrospectively assess key public health interventions such as vaccination programs and provide scenarios to inform future vaccination strategies. Modelling indirect vaccine effects in terms of transmission dynamics is relevant for planning future vaccination strategies and prioritizing age groups for vaccination. Additional research on social mixing patterns is warranted to improve such model estimates. Continuous support and enhancement of national and EU surveillance systems is needed to support and improve modeling estimates. Conclusions The COVID-19 vaccination program in Slovenia has significantly reduced the burden of the pandemic in terms of preventable deaths. Support for national and EU surveillance systems is needed to improve modelling capacities needed to guide public health interventions. When relevant, modelling the indirect effects of vaccination on the unvaccinated population in terms of disease transmission is relevant for prioritizing age groups for vaccination. Further research on social mixing patterns is warranted to improve such estimates. Abbreviations SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 COVID-19 Coronavirus disease EU European Union ICU Intensive care units SARI Severe acute respiratory infection SEIR model Susceptible-Exposed-Infected-Recovered model WHO World Health Organization Declarations Ethics approval and consent to participate Ethical approval was not obtained for this study since only open-access anonymized routinely collected national surveillance data was used. Consent for publication Not applicable. Availability of data and materials Input data used for the model is available on the Slovenian open data portal OPSI (https://podatki.gov.si/). Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Competing interests The authors declare that they have no competing interests. Authors' contributions Mario Fafangel interpreted the data and provided the epidemiological and broader public health context while writing the discussion. Janez Žibert developed the model, ran the simulations, and interpreted the data. Miha Fošnarič co-developed the model and interpreted the data. All authors participated in the conceptualization of the research and were actively involved in writing the manuscript and provided continuous revisions throughout the entire process. Acknowledgements We wish to acknowledge all the employees at the Slovenian National Institute for Public Health whose continuous work on improving the infectious disease surveillance system in the country makes this kind of research possible. References World Health Organization. WHO Coronavirus (COVID-19) Dashboard. [Internet]. 2022 [cited 2022 Mar 26]. Available from: https://covid19.who.int/ Tregoning JS, Flight KE, Higham SL, Wang Z, Pierce BF. 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European Centre for Disease Prevention and Control. Overview of the implementation of COVID-19 vaccination strategies and deployment plans in the EU/EEA. Stockholm: ECDC; 2023 Mar. LaJoie Z, Usherwood T, Sampath S, Srivastava V. A COVID-19 model incorporating variants, vaccination, waning immunity, and population behavior. Sci Rep. 2022 Nov 27;12(1):20377. Backer JA, Vos ERA, Hartog G den, Hagen CCE van, Melker HE de, Klis FRM van der, et al. Contact behaviour before, during and after the COVID-19 pandemic in the Netherlands: evidence from contact surveys, 2016 to 2017 and 2020 to 2023. Eurosurveillance. 2024 Oct 24;29(43):2400143. Klavs I, Serdt M, Berlot L. Epidemiološko spremljanje resnih akutnih okužb dihal, potrjenih covid-19 v Sloveniji v letu 2021. Epidemiološko spremljanje resnih akutnih okužb dihal, potrjenih covid-19 v Sloveniji. [Internet]. 2023. Available from: https://nijz.si/nalezljive-bolezni/epidemiolosko-spremljanje-resnih-akutnih-okuzb-dihal-potrjenih covid- 19-v-sloveniji/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6829583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470429098,"identity":"a50722d0-3042-480e-93b9-82382151b469","order_by":0,"name":"Mario Fafangel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie2Ru2rDMBSGf2FIFlGvnponKJwiEBR6eZWYgLuYEOiS0VO76AG89VEqY2iWXNZAlppC6JBBowcPlZ0lSxSPhepbjgT6zvklAR7PH0UD1xhm7XJ2j0G7B11UBHh7EJT0U3CilJdD3bytKm1AIz5clT+GNtMrtvhi9ey8IpfPVOSgW8WnyV1OuxcbbBxwRzCpE5QcDftAKgWnXfwKrQPXXeRm3yr0pMKDFA2trVJkrHYp224KxSpKxTdIW8U+gTPYdo8iJ5qo6CCZoolVPm0TZ7AkMGZODypMhannj/F7vqiqujmvHDn2HERdiXT3mb0ITFfCrK/g8Xg8/4RfrdVS60eI2v0AAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Public Health (NIJZ)","correspondingAuthor":true,"prefix":"","firstName":"Mario","middleName":"","lastName":"Fafangel","suffix":""},{"id":470429099,"identity":"92d18e03-6dce-440d-9ca1-86323363e7e8","order_by":1,"name":"Janez Žibert","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Janez","middleName":"","lastName":"Žibert","suffix":""},{"id":470429100,"identity":"ff0cff66-c82f-4cb0-bd80-aa7d15f90d28","order_by":2,"name":"Miha Fošnarič","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Miha","middleName":"","lastName":"Fošnarič","suffix":""}],"badges":[],"createdAt":"2025-06-05 13:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6829583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6829583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84703647,"identity":"5b23286d-9cbc-41d2-9156-946183dff041","added_by":"auto","created_at":"2025-06-16 11:59:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe course of the COVID-19 pandemic in Slovenia in 2021 in terms of weekly incidence of confirmed COVID-19 cases (black) and the proportion of population vaccinated against COVID-19 (blue line). The phases of the epidemic, together with prevailing viral variants and periods of non-pharmaceutical interventions, are shown in the timeline on top.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6829583/v1/58a07699142976e9d7edabf2.png"},{"id":84701426,"identity":"dfa1de22-4bb9-4446-b496-96a034453ca8","added_by":"auto","created_at":"2025-06-16 11:35:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCOVID-19 vaccination rate in Slovenia in 2021 as a function of time for the scenarios considered in the analysis. The contribution of different age groups to the population vaccination rate is color-coded: 0-24 (green), 25-44 (orange), 45-64 (red), 65-74 (blue), and more than 75 years old (gray).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6829583/v1/042026a557daf95ddf6d0843.png"},{"id":84702670,"identity":"af7bda49-fdb0-44c0-9f9d-2cb6c5b4c380","added_by":"auto","created_at":"2025-06-16 11:51:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":291946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExtended age-stratified SEIR model for modeling vaccination scenarios.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6829583/v1/4b1351c9afda3c7dc930a656.png"},{"id":84702446,"identity":"70aab729-ce93-49fa-8681-94937ade4d3d","added_by":"auto","created_at":"2025-06-16 11:43:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":370455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of hospital stays (a), number of intensice care units (ICU stays (b) and seven-day averages of the number of deaths (c) due to COVID-19 in Slovenia in 2021, presented for six different vaccination scenarios: S0 - actual vaccination (BLUE); S1 - no vaccination (RED); S2 – 0.7 times actual vaccination (orange); S3 – 1.3 times actual vaccination (GREEN); S4 - actual vaccination of the population over 65, all younger unvaccinated (VIOLET); S5 - 0.7-times actual vaccination of the population over 65, all younger unvaccinated (GRAY).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6829583/v1/6108dd528e3347840ee1eca7.png"},{"id":109102176,"identity":"33e8adb1-2f20-479d-84be-6f2f8b5c2cce","added_by":"auto","created_at":"2026-05-12 14:31:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6829583/v1/120b40be-0405-4562-bae9-1b254337a07a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying direct and indirect impacts of COVID-19 vaccination program in Slovenia in 2021: a retrospective modelling study","fulltext":[{"header":"Background","content":"\u003cp\u003eSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease (COVID-19) pandemic, has led to 776\u0026nbsp;million confirmed infections and more than 7\u0026nbsp;million deaths worldwide as of October 31, 2024 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approved vaccines against SARS-CoV-2 have significantly changed the course of the pandemic and saved tens of millions of lives worldwide [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe COVID-19 vaccination campaign in Slovenia began on December 27, 2020, shortly after the first vaccines were approved. By June of the following year, over 50% of Slovenians aged 65 years and older had received a full primary vaccination, and by the end of October 2021, the same vaccination coverage rate was achieved for the population aged over 18 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the number of confirmed SARS-CoV-2 infections and the spread of vaccination in Slovenia during the COVID-19 pandemic in 2021, also indicating periods with non-pharmaceutical interventions and dominant SARS-CoV-2 variants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGenomic surveillance of SARS-CoV-2 in Slovenia in 2021 showed an increase in the Alpha variant, which became predominant in March, while Delta variant became predominant in July [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite the success of the COVID-19 vaccination campaigns, vaccine confidence has suffered a significant decline since the onset of the pandemic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The age gap in vaccination confidence widened between 2018 and 2022 in almost every European Union (EU) member state with over 65s becoming more confident and 18-34-year-olds growing less confident on the safety and importance of the measles, mumps and rubella vaccines [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the 2023/2024 season, the median COVID-19 vaccination coverage in the EU among people aged 60 years and above was 14% (range: 0.02\u0026ndash;66.1%) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while in Slovenia it reached 6.3% among those aged over 65 years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTransparent and valid information on the risks and benefits of vaccines is important to increase confidence and tackle vaccine hesitancy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Estimating the isolated direct and indirect impact of COVID-19 vaccination is challenging due to the complex disease dynamics associated with the simultaneous implementation of non-pharmaceutical interventions, the emergence of new virus variants and the varying vaccine effectiveness. Epidemiological mathematical modeling provides valuable tools to address such complexities and better understand infectious disease transmission dynamics and the impact of public health interventions [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Modeling has gained in relevance during the last pandemic, providing forecasting scenarios to guide the response, exploring the impact of different hypothetical vaccination rollout scenarios, and helping to identify parameters that affect epidemics, such as vaccine equity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Compartmental models were found to be ideal for long-term analysis, data fitting, and identifying key parameters that affect epidemics [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent well-conducted cross-national surveillance studies and modeling of the impact of vaccination provide valuable insights into quantifying vaccination effects in terms of averted deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Severe cases of COVID-19 continue to place a major burden on healthcare capacities and are often driving public health response and population risk perception, as the availability of hospital and intensive care units (ICU) beds are often the bottleneck that necessitates the implementation of more stringent non-pharmaceutical epidemic control measures in the population. Severe acute respiratory infection (SARI) surveillance studies provide valuable insights into the efficacy of vaccines and their direct impact on severe disease [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When data are available, well fitted compartmental modeling approaches can estimate both mortality and healthcare burden. By accounting for disease transmission dynamics, such modeling approaches can provide estimates for direct vaccination effects on the vaccinated individuals and also indirect effects of vaccination on the broader, unvaccinated community.\u003c/p\u003e \u003cp\u003eThe aim of our study was to retrospectively assess the direct and indirect impact of COVID-19 vaccination on hospitalizations, ICU admissions and deaths in Slovenia in 2021 using an extended Susceptible-Exposed-Infected-Recovered (SEIR) model, in order to provide detailed country estimates and a modeling approach that can serve as a basis for strategic planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eInput data and study period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we used Slovenian anonymized, open-access daily data on laboratory-confirmed COVID-19 cases, COVID-19-related deaths and vaccination coverage from the Slovenian National Institute of Public Health, published on the open-data portal OPSI [23]. Data on hospitalizations and ICU admissions were collected as part of a comprehensive ad-hoc surveillance system to monitor national healthcare capacities during the pandemic, set up by the Ministry of Health with the support of COVID Tracker Slovenia [24]. The study period was the entire year 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVaccination scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated the impact of vaccination in the time periods studied by considering six vaccination rollout scenarios with varying vaccination coverage in age groups, as described below and illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003eIn scenario S0, vaccination coverage followed the actual rollout in 2021 in Slovenia and reached 56% population coverage by the end of the year. Scenario S1 assumes that no vaccination has taken place. Scenario S2 assumes vaccination coverage 0.7 times lower than the actual vaccination coverage (S0) in the entire population and throughout the year, reaching population coverage rate of 39% by the end of 2021. Scenario 3 (S3) assumes a vaccination coverage 1.3 times higher than in S0 in the entire population and throughout the year and reaches 71% population coverage by the end of 2021. Scenarios 4 and 5 (S4 and S5) assume that only people over the age of 65 are vaccinated, in S4 with the actual vaccination rates of 2021, and in S5 with vaccination rates 0.7 times lower than the actual vaccination rates of 2021.\u003c/p\u003e\n\u003cp\u003eVaccination coverage was modeled according to five age groups: 0-24, 25-44, 45-64, 65-74, and over 75 years old. Figure 2 shows the vaccination course for all six scenarios, divided into age groups, and Table 1 shows the vaccination rates at the end of 2021.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Cumulative vaccine uptake by individual age groups as a percentage of the respective group according to scenarios S0 to S5 at the end of the year 2021.\u003c/em\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAge Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eS0 [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eS1 [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eS2 [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eS3 [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eS4 [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eS5[%]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e25-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e73.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e45-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e47.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e85.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eover 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e70.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eScenario modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vaccination scenarios were modeled using an extended age-stratified SEIR compartmental model developed to predict the epidemiological situation in Slovenia during the COVID-19 pandemic. The model is illustrated in Figure 3, while fully described and extensively evaluated in Fo\u0026scaron;narič et al. [25]. The model was also part of the European COVID-19 Forecast Hub (26).\u003c/p\u003e\n\u003cp\u003eThe structure of the base model includes compartments for modeling vaccinated and unvaccinated populations. Vaccination uptake (VU) is integrated into the model as a function of time (indicated with blue color on Figure 3) to divide the model compartments into vaccinated and unvaccinated sub-compartments. Vaccine effectiveness (VE) is included in the model (indicated with green color on Figure 3) to reduce the rates of infection, hospitalizations, ICU admissions, and deaths based on published estimates [27,28]. The base model is extended to five models linked by a mixing matrix W(t) (indicated with orange color on Figure 3), all together modeling the epidemic dynamics of 5 age groups in the population: 0-24, 25-44, 45-64, 65-74, and over 75.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of the model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calibrated the model using actual data for the year 2021, which corresponds to scenario S0. The model parameters were estimated using actual data on the number of cases, hospitalizations and ICU admissions, COVID-19-related deaths and actual vaccination rates for five age groups separately over the entire duration of the epidemic in Slovenia. The calibration process is described in detail in Fo\u0026scaron;narič et al. [25].\u003c/p\u003e\n\u003cp\u003eThe calibrated model was then used to simulate the other five hypothetical scenarios by replacing the time series functions for vaccine uptake according to the described scenarios, while leaving all other parameters of the model unchanged.\u003c/p\u003e\n\u003cp\u003eThe resulting simulations were then compared in terms of hospital and ICU admissions, number of hospitalized persons, and COVID-19-related deaths in both the total population and the population over 65 years of age.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe estimates of the six different vaccination scenarios in terms of hospital and ICU stays and deaths related to COVID-19 are shown as functions of time in Figure 4. The total numbers of hospitalizations, ICU admissions and deaths in the six scenarios are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eIn the scenario without vaccination (S1), the number of ICU stays would have peaked in September 2021 with almost 3,000 people requiring intensive care (143 per 100,000 population). The number of deaths would also have peaked in September 2021 at almost 200 deaths per day. Vaccination alone, when compared to scenario S0, prevented 36,909 hospitalizations (1,755 per 100,000 population), 8,388 ICU admissions (400 per 100,000 population), and 6,741 deaths (321 per 100,000 population). Since the majority of the total burden is expected on the population over 65 years of age, the total numbers of hospitalizations, ICU admissions and deaths are shown in Table 2 for the total population and separately for the age groups over 65 years old.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: The total numbers of hospitalizations, ICU admissions and deaths due to COVID-19 in Slovenia in 2021 for six different vaccination scenarios: S0 - actual vaccination; S1 - no vaccination; S2 \u0026ndash; 0.7 times actual vaccination; S3 \u0026ndash; 1.3 times actual vaccination; S4 - actual vaccination of the population over 65, all younger unvaccinated; S5 \u0026ndash; 0.7 times actual vaccination of the population over 65, all younger unvaccinated. The numbers per 100,000 are calculated by using the population of 2.1 million in Slovenia in 2021.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eS0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eHospital admissions (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e15,803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e52,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e26,088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8,770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e29,350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e37,343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eHospital admissions (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e10,308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e37,998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e15,393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22,994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eICU admissions (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2,901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e11,289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5,472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1,492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7,813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eICU admissions (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2,008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e8,259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3,724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1,204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5,124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDeaths (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2,945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e9,686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5,291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e6,375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDeaths (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2,548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e8,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1,692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3,589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5,222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003ePer 100,000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eHospital admissions (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2,507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1,397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1,777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eHospital admissions (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1,094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eICU admissions (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eICU admissions (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDeaths (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDeaths (over 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we presented the first country-specific estimates of the impact of COVID-19 vaccination in Slovenia. We used a well-fitted extended SEIR model to assess averted hospitalizations, ICU admissions and deaths and to quantify the role of COVID-19 vaccination programs in reducing the burden of disease and safeguarding limited healthcare capacity. With such a data-intensive, well-fitted modelling approach, we were able to retrospectively model the complex country epidemic dynamics and explore different vaccination scenarios in a quantifiable way by changing a single model parameter, in our case the vaccination coverage rate.\u003c/p\u003e \u003cp\u003eOur comparison of the actual vaccination scenario in Slovenia (S0) with a scenario without vaccination (S1) showed that the COVID-19 vaccination program in Slovenia led to a percentage decrease in hospital admissions by 70% (2,507 vs. 752 per 100,000 population), ICU admissions by 74% (538 vs. 138 per 100,000 population) and COVID-19-related deaths by 70% (461 vs. 140 per 100,000 population). If the scenario without vaccination (S1) had occurred, the Slovenian healthcare system would have exhausted all its available resources. The primary bottleneck of the Slovenian healthcare system was the capacity of intensive care units, which in times of pandemic were able to provide care for around 300 people requiring intensive care at once. It is important to note that in this scenario, stricter non-pharmaceutical disease control measures (e.g. lockdowns) would have been necessary to prevent the collapse of the healthcare system and the associated casualties. Without additional public health measures in S1, there would have been almost 200 deaths per day at the height of the epidemic. A 30% lower vaccination coverage rate than the actual (scenario S2), with a maximum vaccination coverage rate of 39%, which is in line with the global World Health Organization (WHO) vaccination target for 2021, would have led to a prolonged epidemic peak, with ICU admissions continuing to exceed maximum capacity, resulting in 261 ICU admissions per 100,000 population.\u003c/p\u003e \u003cp\u003eScenarios S2 and S3 examined the impact of a 30% positive and negative shift from the actual vaccination coverage (i.e., 71% and 39% of the overall population coverage, respectively) and provided evidence of the importance of activities and resources invested in improving population trust and coverage during the pandemic and beyond. It can be assumed that the number of cases (delta wave) seen in Slovenia at the end of 2021 could have been avoided if Slovenia had achieved vaccination coverage as in scenario S3, which was achieved by other EU countries such as Germany, France, or the Netherlands (26).\u003c/p\u003e \u003cp\u003eAs far as we are aware, this is the first published assessment of the impact of vaccination in Slovenia using a modeling approach that takes into account direct and indirect effects, different disease courses in terms of infections, hospitalizations, ICU admissions and deaths, and differentiation of the population by age and vaccination status. A similar model was used to plan the vaccination strategy against COVID-19 in the USA [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The results of a large multi-country study estimating deaths directly prevented by COVID-19 vaccination for the period December 2020 to March 2023 showed a percentage decrease of 45% for Slovenia (from 602 to 333 deaths per 100,000 population) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our estimates for 2021 alone show a percentage decrease of 70% (from 461 to 140 deaths per 100,000 population). Another large multi-country study by Mesle et al., which estimated the number of deaths directly prevented by COVID-19 vaccination in the WHO European Region from December 2020 to November 2021 in people aged 60 years and older [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], found a percentage decrease of 38% in deaths in Slovenia due to vaccination. In comparison, our results for the population aged 65 years and older showed a percentage decrease of 69% in 2021 (from 397 to 121 deaths per 100,000 population). Although a direct comparison of these results is not possible mainly due to the different time periods, age groups, varying SARS-CoV-2 epidemiology and vaccine effectiveness, we believe that our modelled estimates of the vaccine impact add value as they use additional and more detailed input data to describe the epidemic in Slovenia. Overall, our extended SEIR modelling approach also complements surveillance-based approaches with estimates of hospital burden and the ability to explore different scenarios, which can provide valuable insights when healthcare capacity determines the implementation of public health interventions. The higher impact of vaccination on prevented deaths observed in our study may be due in part to the fact that our estimates also include indirect vaccination effects.\u003c/p\u003e \u003cp\u003eAs mentioned above, compared to other approaches, our model takes into account the indirect effects of vaccination on unvaccinated individuals with regard to disease transmission [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Theoretically, there are a number of situations in which the indirect benefit of vaccination outweighs the direct benefit, particularly at low vaccination rates (e.g., 20%) and intermediate values of the basic reproduction number (1\u0026ndash;1.5), which in some cases are more than 400% of the direct benefit [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We can estimate the indirect impact of vaccinating the general population on the higher risk group of over 65-year-olds by comparing the actual vaccination in Slovenia (scenario S0), where the general population was by end of the year 2021 vaccinated at an overall coverage rate of 56%, with scenario S4, in which it is assumed that only the over 65-year-olds are vaccinated (at the actual age-specific coverage rates of around 80%). A comparison between scenarios S4 and S0 shows that the actual vaccination rates for the under-65 population indirectly impacted the over-65 population by additionally reducing their hospital admissions by 33% (731 to 490 per 100,000), ICU admissions by 35% (149 to 96 per 100,000 population) and deaths by 29% (171 to 121 per 100,000).\u003c/p\u003e \u003cp\u003eGiven the evolution of SARS-CoV-2 variants and the reduced impact of current vaccines on disease transmission, vaccination strategies focus on maximising the benefit of COVID-19 vaccination for the most vulnerable individuals and older adults who are vulnerable (e.g., \u0026gt;\u0026thinsp;60 years old) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Scenario S5 explores such a vaccination rollout, targeting only the population over 65 years of age, with a relatively high vaccination coverage rate of around 60%. Nevertheless, it remains important to better understand and quantify the indirect effects of vaccination on disease transmission, and when relevant to consider these findings when developing vaccination strategies. Our model estimates could be improved by better modelling of population mixing behavior [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. and could be used in the future to explore different vaccination strategies for different age groups, which may become relevant again with the advent of new vaccines. Country-specific studies such as the one conducted by Backer et al. provide insights into social mixing patterns that would allow better quantification of the indirect effects of vaccination in different age groups and should be conducted regularly [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has limitations that need to be considered. The data-intensive requirements of the model need to be considered in terms of post-pandemic surveillance capacity. After the pandemic, priorities in Slovenia have shifted and some ad hoc surveillance systems for monitoring hospitalizations and bed capacities have been discontinued. As SARI surveillance plays a more important role, we advocate for the continued support of surveillance capacities at national and EU level that are able to provide open data for complex and granular modeling approaches, which are essential for guidance and evaluation during the next public health crisis. Although in 2021 the alpha variant of SARS-CoV-2 was replaced by the delta variant, we present results for the entire year. The model parameters were tuned to account for the changes in epidemiology caused by the new variant. Another limitation of our model is that it only partially quantifies the complex indirect effects of vaccination. As we have already discussed the behavioral patterns of the population, it is important to point out that the effects related to protecting healthcare capacity by limiting the impact of overcrowded ICUs or, in extreme cases, the need for triage, have not been considered, which likely leads to an underestimation of the impact. To further assess the validity of our model estimates, we compared our model results for COVID-19 hospitalizations and ICU admissions in S0 with available data from the Slovenian surveillance systems for SARI, which were not part of the model input data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These comparisons showed that our model results were consistent with the independent surveillance data from the SARI surveillance system: modeled COVID-19 hospitalizations 15,803 vs. 11,750 and modeled ICU admissions 2901 vs. 2456.\u003c/p\u003e \u003cp\u003eThe COVID-19 vaccination program in Slovenia has significantly reduced the burden of the pandemic in terms of deaths and healthcare access needs. Our data-driven approach, using a SEIR-type model with remarkable data adherence, provides a complementary way to retrospectively assess key public health interventions such as vaccination programs and provide scenarios to inform future vaccination strategies. Modelling indirect vaccine effects in terms of transmission dynamics is relevant for planning future vaccination strategies and prioritizing age groups for vaccination. Additional research on social mixing patterns is warranted to improve such model estimates. Continuous support and enhancement of national and EU surveillance systems is needed to support and improve modeling estimates.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe COVID-19 vaccination program in Slovenia has significantly reduced the burden of the pandemic in terms of preventable deaths. Support for national and EU surveillance systems is needed to improve modelling capacities needed to guide public health interventions. When relevant, modelling the indirect effects of vaccination on the unvaccinated population in terms of disease transmission is relevant for prioritizing age groups for vaccination. Further research on social mixing patterns is warranted to improve such estimates.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSARS-CoV-2 Severe acute respiratory syndrome coronavirus 2\u003c/p\u003e\n\u003cp\u003eCOVID-19 Coronavirus disease\u003c/p\u003e\n\u003cp\u003eEU European Union \u003c/p\u003e\n\u003cp\u003eICU Intensive care units\u003c/p\u003e\n\u003cp\u003eSARI Severe acute respiratory infection\u003c/p\u003e\n\u003cp\u003eSEIR model Susceptible-Exposed-Infected-Recovered model\u003c/p\u003e\n\u003cp\u003eWHO World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not obtained for this study since only open-access anonymized routinely collected national surveillance data was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInput data used for the model is available on the Slovenian open data portal OPSI (https://podatki.gov.si/). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMario Fafangel interpreted the data and provided the epidemiological and broader public health context while writing the discussion. Janez Žibert developed the model, ran the simulations, and interpreted the data. Miha Fo\u0026scaron;narič co-developed the model and interpreted the data. All authors participated in the conceptualization of the research and were actively involved in writing the manuscript and provided continuous revisions throughout the entire process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to acknowledge all the employees at the Slovenian National Institute for Public Health whose continuous work on improving the infectious disease surveillance system in the country makes this kind of research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. WHO Coronavirus (COVID-19) Dashboard. [Internet]. 2022 [cited 2022 Mar 26]. 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Eurosurveillance. 2022 Jan 6;27(1):2101110.\u003c/li\u003e\n\u003cli\u003eKatz MA, Cohuet S, Bino S, Tarkhan-Mouravi O, Kryeziu B, Otorbaeva D, et al. COVID-19 vaccine effectiveness against SARS-CoV-2-confirmed hospitalisation in the eastern part of the WHO European Region (2022\u0026ndash;2023): a test-negative case-control study from the EuroSAVE network. Lancet Reg Health \u0026ndash; Eur [Internet]. 2024 Dec 1 [cited 2025 Jan 17];47. Available from: https://www.thelancet.com/journals/lanepe/article/PIIS2666-7762(24)00262-X/fulltext\u003c/li\u003e\n\u003cli\u003eOPSI - Odprti podatki Slovenije (Slovenian open data): Data from the National Institute of Public health (NIJZ) [Internet]. [cited 2024 Nov 1]. Available from: https://podatki.gov.si/data/search?s=nijz\u003c/li\u003e\n\u003cli\u003eCOVID-19 Sledilnik [Internet]. [cited 2024 Dec 17]. Available from: https://covid-19.sledilnik.org/\u003c/li\u003e\n\u003cli\u003eFo\u0026scaron;narič M, Kamen\u0026scaron;ek T, Žganec Gros J, Žibert J. Extended compartmental model for modeling COVID-19 epidemic in Slovenia. Sci Rep. 2022 Oct 8;12(1):16916.\u003c/li\u003e\n\u003cli\u003eSherratt K, Gruson H, Grah R, Johnson H, Niehus R, Prasse B, et al. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. Wesolowski A, Ferguson NM, Shaman JL, Pei S, editors. eLife. 2023 Apr 21;12:e81916.\u003c/li\u003e\n\u003cli\u003eEyre DW, Taylor D, Purver M, Chapman D, Fowler T, Pouwels KB, et al. Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants. N Engl J Med. 2022 Feb 23;386(8):744\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eLink-Gelles R. Effectiveness of Bivalent mRNA Vaccines in Preventing Symptomatic SARS-CoV-2 Infection \u0026mdash; Increasing Community Access to Testing Program, United States, September\u0026ndash;November 2022. MMWR Morb Mortal Wkly Rep [Internet]. 2022 [cited 2025 Jan 12];71. Available from: https://www.cdc.gov/mmwr/volumes/71/wr/mm7148e1.htm\u003c/li\u003e\n\u003cli\u003eSalo J, H\u0026auml;gg M, Kortelainen M, Leino T, Saxell T, Siikanen M, et al. The indirect effect of mRNA-based COVID-19 vaccination on healthcare workers\u0026rsquo; unvaccinated household members. Nat Commun. 2022 Mar 4;13(1):1162.\u003c/li\u003e\n\u003cli\u003eScutt G, Cross M, Waxman D. Theoretically quantifying the direct and indirect benefits of vaccination against SARS-CoV-2 in terms of avoided deaths. Sci Rep. 2022 May 25;12(1):8833.\u003c/li\u003e\n\u003cli\u003eEuropean Centre for Disease Prevention and Control. Overview of the implementation of COVID-19 vaccination strategies and deployment plans in the EU/EEA. Stockholm: ECDC; 2023 Mar.\u003c/li\u003e\n\u003cli\u003eLaJoie Z, Usherwood T, Sampath S, Srivastava V. A COVID-19 model incorporating variants, vaccination, waning immunity, and population behavior. Sci Rep. 2022 Nov 27;12(1):20377.\u003c/li\u003e\n\u003cli\u003eBacker JA, Vos ERA, Hartog G den, Hagen CCE van, Melker HE de, Klis FRM van der, et al. Contact behaviour before, during and after the COVID-19 pandemic in the Netherlands: evidence from contact surveys, 2016 to 2017 and 2020 to 2023. Eurosurveillance. 2024 Oct 24;29(43):2400143.\u003c/li\u003e\n\u003cli\u003eKlavs I, Serdt M, Berlot L. Epidemiolo\u0026scaron;ko spremljanje resnih akutnih okužb dihal, potrjenih covid-19 v Sloveniji v letu 2021. Epidemiolo\u0026scaron;ko spremljanje resnih akutnih okužb dihal, potrjenih covid-19 v Sloveniji. [Internet]. 2023. Available from: https://nijz.si/nalezljive-bolezni/epidemiolosko-spremljanje-resnih-akutnih-okuzb-dihal-potrjenih covid- 19-v-sloveniji/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"air-borne infections, coronavirus disease (COVID-19), epidemiology, modelling, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Slovenia, vaccines and immunisation, viral infections","lastPublishedDoi":"10.21203/rs.3.rs-6829583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6829583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Declining vaccine confidence post-pandemic necessitates transparent estimates of vaccination impact to regain public trust. Mathematical modelling offers tools to estimate both direct and indirect impacts on mortality and healthcare burden, thereby informing vaccination strategies. This study retrospectively assesses the impact of COVID-19 vaccination on hospitalizations, intensive care units (ICU) admissions, and deaths in Slovenia in 2021, employing an extended Susceptible-Exposed-Infected-Recovered model (SEIR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We utilized Slovenian COVID-19 national surveillance data from January to December 2021, encompassing cases, vaccination coverage, hospitalizations, ICU admissions, and deaths, to fit an age-stratified extended SEIR model. This model incorporates compartments for both vaccinated and unvaccinated populations and comprises five models, linked by a mixing matrix, collectively modelling the epidemic dynamics of 5 age groups: 0-24, 25-44, 45-64, 65-74, and over 75. This allows for exploration of vaccine impact across scenarios with varying vaccination coverage by age groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e With 56% overall coverage in 2021, vaccination alone led to a 70% decrease in hospitalizations (2,507 vs. 752 per 100,000 population), a 74% decrease in ICU admissions (538 vs. 138 per 100,000 population) and a 70% decrease in COVID-19-related deaths (461 vs. 140 per 100,000 population). A population-wide vaccination strategy led to an additional 29% decrease in deaths in the over 65 years old group compared to a scenario vaccinating this high-risk group only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The COVID-19 vaccination program in Slovenia significantly reduced the burden of the pandemic in terms of preventable deaths. Support for national and EU surveillance systems is needed to improve modelling capacities, which are essential to guide public health interventions. When relevant, modelling the indirect effects of vaccination on the unvaccinated population regarding disease transmission is relevant for prioritizing age groups for vaccination. Further research on social mixing patterns is warranted to improve such estimates.\u003c/p\u003e","manuscriptTitle":"Quantifying direct and indirect impacts of COVID-19 vaccination program in Slovenia in 2021: a retrospective modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 11:34:59","doi":"10.21203/rs.3.rs-6829583/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36d45284-4bc9-4d99-bba1-eeac88e36b5a","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T14:29:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 11:34:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6829583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6829583","identity":"rs-6829583","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}