TY  - JOUR
AB  - 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.
AU  - Okolie, A.*
AU  - Müller, J.
AU  - Kretzschmar, M.*
C1  - 68913
C2  - 53765
CY  - 6-9 Carlton House Terrace, London Sw1y 5ag, England
TI  - Parameter estimation for contact tracing in graph-based models.
JO  - J. R. Soc. Interface
VL  - 20
IS  - 208
PB  - Royal Soc
PY  - 2023
SN  - 1742-5689
ER  - 

TY  - JOUR
AB  - In the field of reproductive biology, there is a strong need for a suitable tool capable of non-destructive evaluation of oocyte viability and function. We studied the application of brilliant cresyl blue (BCB) as an intra-vital exogenous contrast agent using multispectral optoacoustic tomography (MSOT) for visualization of porcine ovarian follicles. The technique provided excellent molecular sensitivity, enabling the selection of competent oocytes without disrupting the follicles. We further conducted in vitro embryo culture, molecular analysis (real-time and reverse transcriptase polymerase chain reaction) and DNA fragmentation analysis to comprehensively establish the safety of BCB-enhanced MSOT imaging in monitoring oocyte viability. Overall, the experimental results suggest that the method offers a significant advance in the use of contrast agents and molecular imaging for reproductive studies. Our technique improves the accurate prediction of ovarian reserve significantly and, once standardized for in vivo imaging, could provide an effective tool for clinical infertility management.
AU  - Dutta, R.*
AU  - Mandal, S.
AU  - Lin, H.-C.
AU  - Raz, T.*
AU  - Kind, A.*
AU  - Schnieke, A.*
AU  - Razansky, D.
C1  - 60495
C2  - 49356
CY  - 6-9 Carlton House Terrace, London Sw1y 5ag, England
TI  - Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction.
JO  - J. R. Soc. Interface
VL  - 17
IS  - 172
PB  - Royal Soc
PY  - 2020
SN  - 1742-5689
ER  - 

TY  - JOUR
AB  - Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.
AU  - Hross, S.
AU  - Theis, F.J.
AU  - Sixt, M.*
AU  - Hasenauer, J.
C1  - 55201
C2  - 46089
TI  - Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data.
JO  - J. R. Soc. Interface
VL  - 15
IS  - 149
PY  - 2018
SN  - 1742-5689
ER  -