Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
DOI
PMC
 |
|
Open Access Green as soon as Postprint is submitted to ZB.
Parameter estimation for contact tracing in graph-based models.
J. R. Soc. Interface 20:20230409 (2023)
We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is R0. The estimator is tested in a simulation study and is furthermore applied to COVID-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical COVID-19 data, we compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution fit the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency of the estimates on the reproduction number. Finally, we discuss the relevance of our findings.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Branching Process ; Contact Tracing ; Epidemiology ; Parameter Inference ; Stochastic Susceptible–infected–recovered Model On Graph
ISSN (print) / ISBN
1742-5689
e-ISSN
1742-5662
Quellenangaben
Volume: 20,
Issue: 208,
Article Number: 20230409
Publisher
Royal Society of London
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Horizon 2020 research and innovation funding programme
project GENOMIE_QADOP
Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE)
German Academic Exchange Service (DAAD)
project GENOMIE_QADOP
Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE)
German Academic Exchange Service (DAAD)