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Okolie, A.* ; Müller, J. ; Kretzschmar, M.*

Parameter estimation for contact tracing in graph-based models.

J. R. Soc. Interface 20:20230409 (2023)
Postprint DOI PMC
Open Access Green
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Branching Process ; Contact Tracing ; Epidemiology ; Parameter Inference ; Stochastic Susceptible–infected–recovered Model On Graph
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1742-5689
e-ISSN 1742-5662
Zeitschrift Interface : journal of the Royal Society
Quellenangaben Band: 20, Heft: 208, Seiten: , Artikelnummer: 20230409 Supplement: ,
Verlag Royal Society of London
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen 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)
DOI 10.1098/rsif.2023.0409
WOS ID 001108046500001
PubMed ID 37989228
Erfassungsdatum 2023-12-19
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