Information about number of flights deleted during the pre-processing of the trajectory data, for June 28<sup><i>th</i></sup>, 2019.

Information about number of flights deleted during the pre-processing of the trajectory data, for June 28<sup><i>th</i></sup>, 2019.

<p>Information about number of flights deleted during the pre-processing of the trajectory data, for June 28<sup><i>th</i></sup>, 2019.</p>

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Bibliographische Detailangaben
1. Verfasser:	Raúl López-Martín (21386066) (author)
Weitere Verfasser:	Massimiliano Zanin (340141) (author)
Veröffentlicht:	2025
Schlagworte:	Developmental Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified encompassing topics like develop new tools database may act 854 days spanning 708 network representations scale data set real data sets comparing aircraft positions data source scale structure many real world studies vast area study willing spreading processes processing trajectories pairwise fashion major enablers complex systems
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Graphical representation of the main components of the network of interactions in a single FIR for June 28<sup><i>th</i></sup>, 2019.
von: Raúl López-Martín (21386066)
Veröffentlicht: (2025)

Initial five rows from the interaction CSV file for executed trajectories of 2019-06-28. Distances are rounded to 10<sup>−3</sup>.
von: Raúl López-Martín (21386066)
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Initial five rows from the metadata CSV file for executed trajectories of 2019-06-28. Distances and entropies are rounded to 10<sup>−3</sup>.
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Structure of the CSV files with information about interactions.
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Flowchart of the trajectory pre-processing and interaction extraction.
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Structure of the metadata CSV files.
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Computational cost of the analyses.
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Evolution of daily network metrics as a function of the total distance flown.
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Topological properties of individual networks.
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Time series of complex networks measurements extracted from interactions for planned trajectories over the whole European airspace.
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Spatial and temporal distribution of interactions.
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Temporal snapshots of a simulated interaction network reconstruction.
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Transfer task (pre- and post-test) design and information alignment.
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Estimated total production value, economic value of pollination (EVP) for the entire country and within each biome domain. Values refer to the leading crops in the 2019 harvest. <sup>1</sup>Source: IBGE.
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Characteristics for Kamphaeng Phet cohort studies split by source (KPS1 and KFCS) as well as across the entire dataset. Information on age, hospitalization as well as serological assay results for RT-PCR, haemagglutination inhibition assay (HAI), and enzyme immunoassay (EIA) are presented.
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Acute and convalescent serological data from Thai cohorts used in this analyses, colored by true infections status and prediction of infection during model testing.
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Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.
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Theoretical relationship between time since symptom onset and titers in one infected (red) and three uninfected (blue) individuals.
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Comparison of accuracy metrics (sensitivity, specificity, and AUC ROC) of each model defined by what subset of data was used for training and testing.
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Acute and convalescent data for serological assays, colored by probability of infection found during model testing for Model 9. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent sera samples.
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Acute and convalescent samples from cohort study data for serological assays, colored by whether they are predicted as infections using Model 9.
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Relationship between estimated probability of a dengue virus infection (DENV) found during model testing for Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Acute and convalescent samples from cohort study data for serological assays, colored by the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent serum samples.
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Distribution of paired serological data that fall into probability buckets when using Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Model comparisons for simulated data with differing amounts of observational noise.
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Parameters found in Bayesian estimation framework, definition, and prior distribution.
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Relative observational noise by assay and dataset.
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Parameters for forward simulation using serosim [38].
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Acute and convalescent data for serological assays, colored by probability of.
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