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diff --git a/documentation/amici_refs.bib b/documentation/amici_refs.bib
@@ -594,34 +594,6 @@ @Article{WangSta2019
   url      = {http://www.sciencedirect.com/science/article/pii/S2405896319321433},
 }
 
-@Article{Staedter2020.09.03.268276,
-  author       = {St{\"a}dter, Philipp and Sch{\"a}lte, Yannik and Schmiester, Leonard and Hasenauer, Jan and Stapor, Paul L.},
-  journal      = {bioRxiv},
-  title        = {Benchmarking of numerical integration methods for ODE models of biological systems},
-  year         = {2020},
-  abstract     = {Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 167 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.Competing Interest StatementThe authors have declared no competing interest.},
-  doi          = {10.1101/2020.09.03.268276},
-  elocation-id = {2020.09.03.268276},
-  eprint       = {https://www.biorxiv.org/content/early/2020/09/04/2020.09.03.268276.full.pdf},
-  publisher    = {Cold Spring Harbor Laboratory},
-  timestamp    = {2020-09-30},
-  url          = {https://www.biorxiv.org/content/early/2020/09/04/2020.09.03.268276},
-}
-
-@Article{RaimundezDud2020,
-  author       = {Raim{\'u}ndez, Elba and Dudkin, Erika and Vanhoefer, Jakob and Alamoudi, Emad and Merkt, Simon and Fuhrmann, Lara and Bai, Fan and Hasenauer, Jan},
-  journal      = {medRxiv},
-  title        = {COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modelling.},
-  year         = {2020},
-  abstract     = {Epidemiological models are widely used to analyse the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations by performing a study of the COVID-19 outbreak in Wuhan, China. We perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence / credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that several models are over-simplistic and that the reported case numbers provide often insufficient information.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the European Union{\textquoteright}s Horizon 2020 research and innovation program (CanPathPro; Grant no. 686282; E.D., J.H., S.M.), the Federal Ministry of Education and Research of Germany (Grant no. 031L0159C; E.A. \&amp; Grant no. 01ZX1705; J.H.), the Federal Ministry of Economic Affairs and Energy (Grant no. 16KN074236; J.V.), and the German Research Foundation (Clusters of Excellence EXC 2047 \&amp; EXC 2151; E.R., F.B., J.H.).Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe complete implementation (including the respective version of the used toolboxes) and data are available on ZENODO (https://doi.org/10.5281/zenodo.3757227). This includes the MATLAB code as well as the the specification of the parameter estimation problems as PEtab files (with the model in SBML format).https://doi.org/10.5281/zenodo.3757227},
-  doi          = {10.1101/2020.04.19.20071597},
-  elocation-id = {2020.04.19.20071597},
-  eprint       = {https://www.medrxiv.org/content/early/2020/04/22/2020.04.19.20071597.full.pdf},
-  publisher    = {Cold Spring Harbor Laboratory Press},
-  timestamp    = {2020-11-09},
-  url          = {https://www.medrxiv.org/content/early/2020/04/22/2020.04.19.20071597},
-}
-
 @Article{GerosaChi2020,
   author    = {Luca Gerosa and Christopher Chidley and Fabian Fröhlich and Gabriela Sanchez and Sang Kyun Lim and Jeremy Muhlich and Jia-Yun Chen and Sreeram Vallabhaneni and Gregory J. Baker and Denis Schapiro and Mariya I. Atanasova and Lily A. Chylek and Tujin Shi and Lian Yi and Carrie D. Nicora and Allison Claas and Thomas S.C. Ng and Rainer H. Kohler and Douglas A. Lauffenburger and Ralph Weissleder and Miles A. Miller and Wei-Jun Qian and H. Steven Wiley and Peter K. Sorger},
   journal   = {Cell Systems},
@@ -794,6 +766,51 @@ @Article{Erdem2020.11.09.373407
   url          = {https://www.biorxiv.org/content/early/2020/11/10/2020.11.09.373407},
 }
 
+@Article{StaedterSch2021,
+  author    = {Städter, Philipp and Schälte, Yannik and Schmiester, Leonard and Hasenauer, Jan and Stapor, Paul L.},
+  journal   = {Scientific Reports},
+  title     = {Benchmarking of numerical integration methods for ODE models of biological systems},
+  year      = {2021},
+  issn      = {2045-2322},
+  number    = {1},
+  pages     = {2696},
+  volume    = {11},
+  abstract  = {Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.},
+  doi       = {10.1038/s41598-021-82196-2},
+  refid     = {Städter2021},
+  timestamp = {2021-02-19},
+  url       = {https://doi.org/10.1038/s41598-021-82196-2},
+}
+
+@Article{Schmiester2021.02.06.430039,
+  author       = {Schmiester, Leonard and Weindl, Daniel and Hasenauer, Jan},
+  journal      = {bioRxiv},
+  title        = {Efficient gradient-based parameter estimation for dynamic models using qualitative data},
+  year         = {2021},
+  abstract     = {Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence.Results Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. Additionally, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data.Availability The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.Competing Interest StatementThe authors have declared no competing interest.},
+  doi          = {10.1101/2021.02.06.430039},
+  elocation-id = {2021.02.06.430039},
+  eprint       = {https://www.biorxiv.org/content/early/2021/02/08/2021.02.06.430039.full.pdf},
+  publisher    = {Cold Spring Harbor Laboratory},
+  timestamp    = {2021-02-19},
+  url          = {https://www.biorxiv.org/content/early/2021/02/08/2021.02.06.430039},
+}
+
+@Article{RaimundezDud2021,
+  author    = {Elba Raimúndez and Erika Dudkin and Jakob Vanhoefer and Emad Alamoudi and Simon Merkt and Lara Fuhrmann and Fan Bai and Jan Hasenauer},
+  journal   = {Epidemics},
+  title     = {COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling},
+  year      = {2021},
+  issn      = {1755-4365},
+  pages     = {100439},
+  volume    = {34},
+  abstract  = {Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.},
+  doi       = {https://doi.org/10.1016/j.epidem.2021.100439},
+  keywords  = {Compartment model, SEIRD, Parameter estimation, Model selection, Uncertainty analysis},
+  timestamp = {2021-02-19},
+  url       = {https://www.sciencedirect.com/science/article/pii/S1755436521000037},
+}
+
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