Citations & re-uses

To cite the European Covid-19 Forecast Hub in project in publications, please use the following references:

Methodology and evaluation

Sherratt, K., Gruson, H., Johnson, H., Niehus, R., Prasse, B., Sandman, F., … & Funk, S. (2022). Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife. DOI: https://doi.org/10.7554/eLife.81916

Data

Katharine Sherratt, Hugo Gruson, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandman, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, … Sebastian Funk. (2022). European Covid-19 Forecast Hub (v2022.10.20)
Dataset
. Zenodo. https://doi.org/10.5281/zenodo.7229300

Please get in touch if you know or published an article using data from the European Covid-19 Forecast Hub or mentioning it as a public health tool.

• Morel J, Morel J, Alvarez L (2023). “Learning from the past: a short term forecast method for the COVID-19 incidence curve.” medRxiv. doi:10.1101/2022.11.05.22281904, https://www.medrxiv.org/content/early/2023/04/25/2022.11.05.22281904.full.pdf, https://www.medrxiv.org/content/early/2023/04/25/2022.11.05.22281904.

• Külah E, Çetinkaya YM, Özer AG, Alemdar H (2023). “COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.” Expert Systems with Applications, 214, 119034. ISSN 0957-4174, doi:10.1016/j.eswa.2022.119034, https://www.sciencedirect.com/science/article/pii/S0957417422020528.

• Giudici P, Tarantino B, Roy A (2023). “Bayesian time-varying autoregressive models of COVID-19 epidemics.” Biometrical Journal, 65(1), 2200054. doi:10.1002/bimj.202200054, https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202200054, https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202200054.

• Rodiah I, Vanella P, Kuhlmann A, Jaeger VK, Harries M, Krause G, Karch A, Bock W, Lange B (2023). “Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany.” European Journal of Epidemiology, 38(1), 39–58. ISSN 1573-7284, doi:10.1007/s10654-022-00938-6, http://dx.doi.org/10.1007/s10654-022-00938-6.

• Müller SA, Paltra S, Rehmann J, Nagel K, Conrad TO (2023). “Explicit Modelling of Antibody Levels for Infectious Disease Simulations in the Context of SARS-CoV-2.” bioRxiv. doi:10.1101/2023.03.31.535072, https://www.biorxiv.org/content/early/2023/03/31/2023.03.31.535072.full.pdf, https://www.biorxiv.org/content/early/2023/03/31/2023.03.31.535072.

• Antulov-Fantulin N, Böttcher L (2022). “On the Accuracy of Short-Term COVID-19 Fatality Forecasts.” BMC Infectious Diseases, 22(1), 1–7. ISSN 1471-2334, doi:10.1186/s12879-022-07205-9.

• Parolini N, Dede L, Ardenghi G, Quarteroni A (2022). “Modelling the COVID-19 Epidemic and the Vaccination Campaign in Italy by the SUIHTER Model.” Infectious Disease Modelling. ISSN 2468-0427, doi:10.1016/j.idm.2022.03.002.

• Rodríguez A, Kamarthi H, Agarwal P, Ho J, Patel M, Sapre S, Prakash BA (2022). “Data-Centric Epidemic Forecasting: A Survey.” arXiv. doi:10.48550/ARXIV.2207.09370.

• Fošnarič M, Kamenšek T, Gros JŽ, Žibert J (2022). “Extended compartmental model for modeling COVID-19 epidemic in Slovenia.” medRxiv. doi:10.1101/2022.07.16.22277702.

• Srivastava A (2022). “The Variations of SIkJalpha Model for COVID-19 Forecasting and Scenario Projections.” arXiv. doi:10.48550/ARXIV.2207.02919.

• Abbott S, Sherratt K, Bosse N, Gruson H, Bracher J, Funk S (2022). “Evaluating an epidemiologically motivated surrogate model of a multi-model ensemble.” medRxiv. doi:10.1101/2022.10.12.22280917, https://www.medrxiv.org/content/early/2022/10/13/2022.10.12.22280917.full.pdf, https://www.medrxiv.org/content/early/2022/10/13/2022.10.12.22280917.

• Bosse NI, Abbott S, Bracher J, Hain H, Quilty BJ, Jit M, for the Mathematical Modelling C, van Leeuwen E, Cori A, Funk S (2022). “Comparing human and model-based forecasts of COVID-19 in Germany and Poland.” PLOS Computational Biology, 18(9), 1-24. doi:10.1371/journal.pcbi.1010405, https://doi.org/10.1371/journal.pcbi.1010405.

• Krymova E, Béjar B, Thanou D, Sun T, Manetti E, Lee G, Namigai K, Choirat C, Flahault A, Obozinski G (2022). “Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.” Proceedings of the National Academy of Sciences, 119(32), e2112656119. doi:10.1073/pnas.2112656119, https://www.pnas.org/doi/pdf/10.1073/pnas.2112656119, https://www.pnas.org/doi/abs/10.1073/pnas.2112656119.

• Kucharski AJ, Hodcroft EB, Kraemer MUG (2021). “Sharing, Synthesis and Sustainability of Data Analysis for Epidemic Preparedness in Europe.” The Lancet Regional Health Europe, 9. ISSN 2666-7762, doi:10.1016/j.lanepe.2021.100215.

• Marcos Alarcón P (2021). Estudio de Modelos de Aprendizaje Automático Probabilístico para la Predicción de casos de Covid-19 en España. Ph.D. thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial. http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-ICD-Pmarcos.