Epidemiological COVID-19 model LeipzigIMISE-SECIR Version 1
A principled approach to parametrize SIR-type epidemiologic models of different complexities by embedding the model structure as a hidden layer into a general Input-Output Non-Linear Dynamical System (IO-NLDS). Non-explicitly modelled impacts on the system are imposed as inputs of the system. Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a SIR-type model and data of Germany and Saxony demonstrating good prediction performances.
By our approach, we can estimate and compare for example the relative effectiveness of non-pharmaceutical interventions and can provide predictions regarding the further course of the epidemic under specified scenarios. Our method of parameter estimation can be translated to other data sets, i.e. other countries and other SIR-type models even for other disease contexts.
LHA ID: 88Q2HXKTJF-0
0 items (and an image) are associated with this Model:Human Disease: Covid-19
Model type: Ordinary differential equations (ODE)
Model format: R package
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Created: 18th Sep 2021 at 09:41
Last updated: 18th Sep 2021 at 09:41
Last used: 19th Dec 2024 at 09:28
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Version 1 Created 18th Sep 2021 at 09:41 by Holger Kirsten
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This project serves as a container for all COVID-19 related assets, developed at the University of Leipzig and its partners.
Programme: NFDI - German National Research Data Infrastructure
Public web page: https://www.nfdi4health.de/task-force-covid-19
Start date: 20th May 2020
NFDI4Health - the National Research Data Infrastructure for Personal Health Data - deals with data generated in clinical trials, epidemiological and public health studies. The collection and analysis of these data on health and disease status and important factors influencing it are an essential component for the development of new therapies, comprehensive care approaches and preventive measures in a modern health care system. Although these data already meet high content quality standards, they ...
Programme: NFDI - German National Research Data Infrastructure
Public web page: https://www.nfdi4health.de/en
The group is interested in omic-level studies including the genome, methylome, transcriptome, and metabolome of the general population as well as of various diseases including cardiovascular diseases, pneumonia, sepsis, obesity, and brain cancers. Additionally, we aim at transfering results of biomathematical model simulations into clinical practice e.g. by haematopoietic growth-factor opimization during cytotoxic chemotherapy and optimization of EPO applications in chronic kidney disease.
The ...
Programme: This Project is not associated with a Programme
Public web page: http://www.imise.uni-leipzig.de/en/Groups/GenStat/
Start date: 1st Jan 2013
https://orcid.org/0000-0002-3126-7950
Projects: LIFE Heart, LIFE - Leipzig Research Center for Civilization Diseases, HaematoOpt - Individualized model-based managing of the next-cycle thrombopenia of CHOEP/CHOP treated patients based on platelets dynamics during the previous cycles, e:Med, Genetical Statistics and Systems Biology, Task Force COVID-19 Leipzig, NFDI4Health
Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE)
Markus Scholz Scientific Speaker Institution Institut für Medizinische Informatik, Statistik und Epidemiologie Universität Leipzig Härtelstraße 16-18 04107 Leipzig
In the German National Research Data Infrastructure (NFDI), valuable data from science and research are systematically accessed, networked and made usable in a sustainable and qualitative manner for the entire German science system. Up to now, they have mostly been available on a decentralised, project-related or temporary basis. The NFDI aims to create a permanent digital repository of knowledge as an indispensable prerequisite for new research questions, findings and innovations. Relevant data ...
Projects: NFDI4Health, Task Force COVID-19 Leipzig, NFDI4Health - TA3 Services, Clinical Trials Leipzig
Web page: https://www.nfdi.de/?lang=en
Taxonomy URI: http://purl.obolibrary.org/obo/DOID_0080600
Synonyms: 2019-nCoV infection, 2019 Novel Coronavirus (2019-nCoV), Wuhan seafood market pneumonia virus infection, Wuhan coronavirus infection
Definitions: A Coronavirus infection that is characterized by fever, cough and shortness of breath and that has_material_basis_in SARS-CoV-2.
