{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T01:08:33Z","timestamp":1783818513206,"version":"3.55.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:00Z","timestamp":1783728000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002835","name":"Chalmers University of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002835","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Health Serv Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The benefits of computational forecasting in the later phase of the COVID-19 pandemic when vaccines and clinical pharmaceutical interventions were available have seldom been assessed. We aimed to evaluate computational forecasting research applied to Swedish populations in the vaccination phase of the pandemic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search was performed on March 8, 2024 in the electronic databases PubMed, Scopus, Cochrane library, Embase, Love platform and Epistemikos. An updated version of the Risk of bias Opinion Tool (ROBOT) was used to assess the quality of evidence reported in the papers identified in the search. The articles fulfilling the quality criteria were assessed for suitability for meta-analysis. Data were extracted from the selected articles for synthesis of characteristics, and a thematic analysis was used for a qualitative synthesis of the contents.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 2034 unique publications identified in the database search, 6 articles satisfied the selection and quality criteria. Variability in the reporting of forecasting performance results was found to make a quantitative meta-analysis of forecast performance infeasible. The data synthesis showed that statistical modeling using Bayesian calibration was the most common methodological approach. No external model validation was reported, but 5\/6 articles included internal model corroboration data. The primary theme resulting from the qualitative synthesis of article content was design or refinement of computational models with demonstration of model use in health service practice as a secondary theme. None of the articles referred to health service policymaking as the primary research context.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Computational forecasting research using Swedish population data from the vaccination phase of the COVID-19 pandemic was deployed in a model design context. While methodological knowledge was developed, most of the research was not initiated to solve the public health and healthcare problems at hand. Our results indicate that the alignment between computational forecasting research and policymaking needs in the vaccination phase of pandemics can be enhanced.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12913-026-15076-y","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:35:32Z","timestamp":1783665332000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computational forecasting using Swedish data in the vaccination phase of the COVID-19 pandemic: a systematic literature review deliberating modelling relevance for public health and healthcare"],"prefix":"10.1186","volume":"26","author":[{"given":"Anna Saxne","family":"J\u00f6ud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik","family":"Thor\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Spreco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Torbj\u00f6rn","family":"Lundh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toomas","family":"Timpka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8503-0177","authenticated-orcid":false,"given":"Philip","family":"Gerlee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"15076_CR1","volume-title":"Calibrating long-term non-pharmaceutical interventions for COVID-19. Principles and facilitation tools Republished without changes on 07 June 2022. Originally published on 16 May 2020","author":"WHO","year":"2020","unstructured":"World Health Organization. Calibrating long-term non-pharmaceutical interventions for COVID-19. Principles and facilitation tools Republished without changes on 07 June 2022. Originally published on 16 May 2020. Geneva: World Health Organization Pacfic Region; 2020."},{"key":"15076_CR2","volume-title":"Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand","author":"NM Ferguson","year":"2020","unstructured":"Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Ghani AC. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Volume 16. London: Imperial College London; 2020."},{"key":"15076_CR3","doi-asserted-by":"publisher","unstructured":"Soltesz K, Gustafsson F, Timpka T, Jald\u00e9n J, Jidling C, Heimerson A, Sch\u00f6n TB, Spreco A, Ekberg J, Dahlstr\u00f6m \u00d6, Bagge Carlson F, J\u00f6ud A, Bernhardsson B. The effect of interventions on COVID-19. Nature. 2020;588(7839). https:\/\/doi.org\/10.1038\/s41586-020-3025-y.","DOI":"10.1038\/s41586-020-3025-y"},{"key":"15076_CR4","first-page":"88","volume":"56","author":"M Marathe","year":"2013","unstructured":"Marathe M, Vullikanti KAS. Comput Epidemiol CACM. 2013;56:88\u201396.","journal-title":"Comput Epidemiol CACM"},{"issue":"4","key":"15076_CR5","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/s41577-021-00522-1","volume":"21","author":"T Carvalho","year":"2021","unstructured":"Carvalho T, Krammer F, Iwasaki A. The first 12 months of COVID-19: a timeline of immunological insights. Nat Rev Immunol. 2021;21(4):245\u201356. https:\/\/doi.org\/10.1038\/s41577-021-00522-1.","journal-title":"Nat Rev Immunol"},{"key":"15076_CR6","doi-asserted-by":"publisher","first-page":"778037","DOI":"10.3389\/fpubh.2022.778037","volume":"10","author":"S Siddiqui","year":"2022","unstructured":"Siddiqui S, Alhamdi HWS, Alghamdi HA. Recent Chronology of COVID-19 Pandemic. Front Public Health. 2022;10:778037. https:\/\/doi.org\/10.3389\/fpubh.2022.778037.","journal-title":"Front Public Health"},{"issue":"7","key":"15076_CR7","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1007\/s10238-023-01168-0","volume":"23","author":"E Pitsillou","year":"2023","unstructured":"Pitsillou E, Yu Y, Beh RC, Liang JJ, Hung A, Karagiannis TC. Chronicling the 3-year evolution of the COVID-19 pandemic: analysis of disease management, characteristics of major variants, and impacts on pathogenicity. Clin Exp Med. 2023;23(7):3277\u201398. https:\/\/doi.org\/10.1007\/s10238-023-01168-0.","journal-title":"Clin Exp Med"},{"key":"15076_CR8","doi-asserted-by":"publisher","unstructured":"COVID research. a year of scientific milestones. Nature. 2021 May;5. https:\/\/doi.org\/10.1038\/d41586-020-00502-w.","DOI":"10.1038\/d41586-020-00502-w"},{"issue":"7","key":"15076_CR9","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1111\/apm.13133","volume":"129","author":"H Mens","year":"2021","unstructured":"Mens H, Koch A, Chaine M, Bengaard Andersen A. The Hammer vs Mitigation-A comparative retrospective register study of the Swedish and Danish national responses to the COVID-19 pandemic in 2020. APMIS. 2021;129(7):384\u201392. https:\/\/doi.org\/10.1111\/apm.13133.","journal-title":"APMIS"},{"issue":"1","key":"15076_CR10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1177\/1403494820980264","volume":"49","author":"EA Yarmol-Matusiak","year":"2021","unstructured":"Yarmol-Matusiak EA, Cipriano LE, Stranges S. A comparison of COVID-19 epidemiological indicators in Sweden, Norway, Denmark, and Finland. Scand J Public Health. 2021;49(1):69\u201378. https:\/\/doi.org\/10.1177\/1403494820980264.","journal-title":"Scand J Public Health"},{"key":"15076_CR11","doi-asserted-by":"crossref","unstructured":"Gerlee P, J\u00f6ud A, Spreco A, Timpka T. Computational models predicting the early development of the COVID-19 pandemic in Sweden: Systematic review, data synthesis, and secondary validation of accuracy. Sci Rep. 12(1):13256.","DOI":"10.1038\/s41598-022-16159-6"},{"key":"15076_CR12","doi-asserted-by":"crossref","unstructured":"Schroeder SA. How to interpret COVID-19 predictions: reassessing the IHME\u2019s model. Philos Med. 2(1).","DOI":"10.5195\/pom.2021.43"},{"key":"15076_CR13","doi-asserted-by":"publisher","first-page":"5928","DOI":"10.1136\/bmj.d5928","volume":"343","author":"JP Higgins","year":"2011","unstructured":"Higgins JP, Altman DG, G\u00f8tzsche PC, J\u00fcni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA. The Cochrane Collaboration\u2019s tool for assessing risk of bias in randomised trials. BMJ. 2011;343:5928. https:\/\/doi.org\/10.1136\/bmj.d5928.","journal-title":"BMJ"},{"key":"15076_CR14","doi-asserted-by":"publisher","unstructured":"Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med. 2019;170(1):W33. https:\/\/doi.org\/10.7326\/M18-1377.","DOI":"10.7326\/M18-1377"},{"issue":"1","key":"15076_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","volume":"14","author":"CL Ramspek","year":"2021","unstructured":"Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clin kidney J. 2021;14(1):49\u201358.","journal-title":"Clin kidney J"},{"key":"15076_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-69909-7_3470-2","volume-title":"Thematic Analysis: A Practical Guide","author":"V Braun","year":"2022","unstructured":"Braun V, Clarke V. Thematic Analysis: A Practical Guide. London: SAGE Publications Ltd; 2022."},{"key":"15076_CR17","unstructured":"J\u00f6ud A, Gerlee P, Spreco A, Timpka T. Sammanst\u00e4llning och utv\u00e4rdering av modeller f\u00f6r pandemiprediktion I Sverige under 2020. Link\u00f6ping University Electronic."},{"key":"15076_CR18","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.puhe.2023.06.007","volume":"221","author":"A Spreco","year":"2023","unstructured":"Spreco A, Andersson C, Sj\u00f6dahl R, Timpka T. Concordance between COVID-19 mortality statistics derived from clinical audit and death certificates in \u00d6sterg\u00f6tland county, Sweden. Public Health. 2023;221:46\u20139. https:\/\/doi.org\/10.1016\/j.puhe.2023.06.007.","journal-title":"Public Health"},{"key":"15076_CR19","doi-asserted-by":"crossref","unstructured":"Nixon K, Jindal S, Parker F, Marshall M, Reich NG, Ghobadi K et al. Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation. Lancet Digital Health. 4(10):e699-e701.","DOI":"10.1016\/S2589-7500(22)00167-4"},{"key":"15076_CR20","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1016\/j.envsoft.2004.10.005","volume":"20","author":"P Reichert","year":"2005","unstructured":"Reichert P, Borsu ME. Does high forecast uncertainty preclude effective decision support? Environ Modelling Softw. 2005;20:991\u20131001.","journal-title":"Environ Modelling Softw"},{"key":"15076_CR21","doi-asserted-by":"crossref","unstructured":"Woelmer WM, Moore TN, Lofton ME, Thomas RQ, Carey CC. Embedding communication concepts in forecasting training increases students\u2019 understanding of ecological uncertainty. Ecosphere. 2023;14.","DOI":"10.1002\/ecs2.4628"},{"issue":"8","key":"15076_CR22","doi-asserted-by":"publisher","first-page":"e543","DOI":"10.1016\/S2589-7500(24)00144-4","volume":"6","author":"P Gerlee","year":"2024","unstructured":"Gerlee P, Thor\u00e9n H, J\u00f6ud AS, Lundh T, Spreco A, Nordlund A, Brezicka T, Britton T, Kjellberg M, K\u00e4llberg H, Tegnell A. Evaluation and communication of pandemic scenarios. Lancet Digit Health. 2024;6(8):e543\u20134.","journal-title":"Lancet Digit Health"},{"issue":"1","key":"15076_CR23","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s13643-023-02411-1","volume":"13","author":"O Espinosa","year":"2024","unstructured":"Espinosa O, Mora L, Sanabria C, Ramos A, Rinc\u00f3n D, Bejarano V, Rodr\u00edguez J, Barrera N, \u00c1lvarez-Moreno C, Cort\u00e9s J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev. 2024;13(1):30. https:\/\/doi.org\/10.1186\/s13643-023-02411-1.","journal-title":"Syst Rev"},{"issue":"6","key":"15076_CR24","doi-asserted-by":"publisher","first-page":"e0004675","DOI":"10.1371\/journal.pgph.0004675","volume":"5","author":"L Hadley","year":"2025","unstructured":"Hadley L, Rich C, Tasker A, Restif O, Funk S. How does policy modelling work in practice? A global analysis on the use of epidemiological modelling in health crises. PLOS global public health. 2025;5(6):e0004675.","journal-title":"PLOS global public health"}],"container-title":["BMC Health Services Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12913-026-15076-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12913-026-15076-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12913-026-15076-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T01:02:17Z","timestamp":1783818137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12913-026-15076-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,10]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["15076"],"URL":"https:\/\/doi.org\/10.1186\/s12913-026-15076-y","relation":{},"ISSN":["1472-6963"],"issn-type":[{"value":"1472-6963","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,10]]},"assertion":[{"value":"4 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"N\/A.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"947"}}