Eyvind Wærsted Axelsen

About

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and dis

Selected publications

• Scientific articles and book chapters
• Andersen, Thea A; Bjerner, Lars Johan; Tjade, Trygve; Ranheim, Trond Egil; Axelsen, Eyvind Wærsted & Sovershaev, Michael [Show all 8 contributors for this article] (2025). The COVID-19 pandemic and critical laboratory functions. Can fast-track molecular testing reduce work absence in the laboratory? Journal of Infection Prevention. ISSN 1757-1774. doi: 10.1177/17571774251330455. Full text in Research Archive
• Elfert, Eike; Kaminski, Wolfgang E.; Matek, Christian; Hoermann, Gregor; Axelsen, Eyvind Wærsted & Marr, Carsten [Show all 7 contributors for this article] (2024). Expert-level detection of M-proteins in serum protein electrophoresis using machine learning. Clinical Chemistry and Laboratory Medicine. ISSN 1434-6621. doi: 10.1515/cclm-2024-0222. Full text in Research Archive
• Garcia-Gallo, Esteban; Merson, Laura; Kennon, Kalynn; Kelly, Sadie; Citarella, Barbara Wanjiru & Fryer, Daniel Vidali [Show all 1,775 contributors for this article] (2022). ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19. Scientific Data. 9(1). doi: 10.1038/s41597-022-01534-9. Full text in Research Archive Show summary The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use.
• Søraas, Arne Vasli; Kalleberg, Karl Trygve; Dahl, John Arne; Søraas, Camilla Lund; Myklebust, Tor Åge & Axelsen, Eyvind Wærsted [Show all 12 contributors for this article] (2021). Persisting symptoms three to eight months after non-hospitalized COVID-19, a prospective cohort study. PLOS ONE. 16(8), p. 1–13. doi: 10.1371/journal.pone.0256142. Full text in Research Archive Show summary Long-COVID-19 is a proposed syndrome negatively affecting the health of COVID-19 patients. We present data on self-rated health three to eight months after laboratory confirmed COVID-19 disease compared to a control group of SARS-CoV-2 negative patients. We followed a cohort of 8786 non-hospitalized patients who were invited after SARS-CoV-2 testing between February 1 and April 15, 2020 (794 positive, 7229 negative). Participants answered online surveys at baseline and follow-up including questions on demographics, symptoms, risk factors for SARS-CoV-2, and self-rated health compared to one year ago. Determinants for a worsening of self-rated health as compared to one year ago among the SARS-CoV-2 positive group were analyzed using multivariate logistic regression and also compared to the population norm. The follow-up questionnaire was completed by 85% of the SARS-CoV-2 positive and 75% of the SARS-CoV-2 negative participants on average 132 days after the SARS-CoV-2 test. At follow-up, 36% of the SARS-CoV-2 positive participants rated their health “somewhat” or “much” worse than one year ago. In contrast, 18% of the SARS-CoV-2 negative participants reported a similar deterioration of health while the population norm is 12%. Sore throat and cough were more frequently reported by the control group at follow-up. Neither gender nor follow-up time was associated with the multivariate odds of worsening of self-reported health compared to one year ago. Age had an inverted-U formed association with a worsening of health while being fit and being a health professional were associated with lower multivariate odds. A significant proportion of non-hospitalized COVID-19 patients, regardless of age, have not returned to their usual health three to eight months after infection.
• Axelsen, Eyvind Wærsted & Krogdahl, Stein (2013). Package Templates: A Definition by Semantics-Preserving Source-to-Source Transformations to Efficient Java Code. SIGPLAN notices. ISSN 0362-1340. 48(3), p. 50–59. doi: 10.1145/2371401.2371409. Full text in Research Archive
• Axelsen, Eyvind Wærsted; Sørensen, Fredrik; Krogdahl, Stein & Møller-Pedersen, Birger (2012). Challenges in the Design of the Package Template Mechanism. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 7271, p. 268–305. doi: 10.1007/978-3-642-35551-6_7. Full text in Research Archive
• Axelsen, Eyvind Wærsted & Krogdahl, Stein (2012). Package templates: a definition by semantics-preserving source-to-source transformations to efficient Java code. In Ostermann, Klaus (Eds.), GPCE '12: Proceedings of the 11th International Conference on Generative Programming and Component Engineering. Association for Computing Machinery (ACM). ISSN 9781450311298. p. 50–59. doi: 10.1145/2371401.2371409. Full text in Research Archive
• Axelsen, Eyvind Wærsted & Krogdahl, Stein (2012). Adaptable generic programming with required type specifications and package templates. In Haupt, Michael (Eds.), Proceedings of the 11th annual international conference on Aspect-oriented Software Development. Association for Computing Machinery (ACM). ISSN 9781450310925. p. 83–94. doi: 10.1145/2162049.2162060. Full text in Research Archive
• Axelsen, Eyvind Wærsted; Krogdahl, Stein & Møller-Pedersen, Birger (2010). Controlling Dynamic Module Composition through an Extensible Meta-Level API. SIGPLAN notices. ISSN 0362-1340. 45(12), p. 81–95. Full text in Research Archive