Table 1.
A reference for the notation used throughout the paper.
Fig 1.
A visualisation of the output of the model applied to the full CUH Ward A dataset.
Each row corresponds to a candidate individual, each column, except the last, to a potential infection source. Sources starting with “CAMP" are identified patients within the hospital. The “Hospital" source represents all unidentified sources within the hospital. The “Community" source is all sources outside the hospital. Cells are coloured by the posterior probability of that infection source. The last column shows the posterior nosocomiality probability, which is 1–P(CommunityData).
Fig 2.
The impact of adding data sources on the assessed probability of each infection source.
Panel a. shows the posterior probabilities of each infection source when only symptom onsets and admission times are provided to NOSTRA. The other panels explore the impact on the posterior probabilities as different data is added. Panel b. shows the change in posterior probability from the estimates in panel a. when location information is added. Panel c. shows the change in posterior probability from the estimates in panel a. when genetic information about the pathogen is added. Panel d. shows the change in posterior probability from the model including onset times, admission times and patient locations, when genetic information about the pathogen is added. Panel e. shows the change in posterior probability from the model including onset times, admission times and genetic information about the pathogen, when patient locations are added.
Fig 3.
The calibration of NOSTRA versus references as measured by Brier score.
The calibration of NOSTRA versus references as measured by Brier score for nosocomiality assessment (left), source identification (middle), and transmission chain identification (right). Low scores indicate better calibration. Points are coloured by the prevalence used in that simulation (see methods). The large black points correspond to the mean across simulations. NOSTRA, Candidates is the NOSTRA model run with a full set of candidate individuals and all data. NOSTRA, No Candidates is the NOSTRA model run with no candidate individuals using Eq 17. Prevalence Prior sets the prior probability of nosocomiality to the true probability of nosocomiality in the dataset. Na ve Prior sets the prior probability of community infection to 0.5 and the probability of every source within the hospital to , where n is the number of candidate individuals in the hospital. 96hr Categorisation assigns a nosocomiality probability of 0 to anything detected in the first 96 hours post-admission and a nosocomiality probability of 1 to everything else. The backets show the Bonferroni-corrected p-values of the one tailed paired Wilcoxon signed rank test that the Brier score of the NOSTRA model run with candidates is lower than each reference.
Table 2.
The arithmetic mean, and 0.025 and 0.975 quantiles of the Brier scores of the different models for each of the different estimation targets NOSTRA, Candidates is the NOSTRA model run with a full set of candidate individuals and all data. NOSTRA, No Candidates is the NOSTRA model run with no candidate individuals using Eq 17. Prevalence Prior sets the prior probability of nosocomiality to the true probability of nosocomiality in the dataset. Na ve prior sets the prior probability of community infection to 0.5 and the probability of every source within the hospital to , where n is the number of candidate individuals in the hospital. 96hr Categorisation assigns a nosocomiality probability of 0 to anything detected in the first 96 hours post-admission and a nosocomiality probability of 1 to everything else.
Fig 4.
The calibration of NOSTRA versus references as measured by Brier score by the between admission and detection of infection.
The calibration of NOSTRA versus references as measured by Brier score for nosocomiality assessment (left), source identification (middle), and transmission chain identification (right) by the time between admission and detection of infection. Low scores indicate better calibration. The large red points correspond to the mean across simulations. NOSTRA - Candidates is the NOSTRA model run with a full set of candidate individuals and all data. NOSTRA - No Candidates is the NOSTRA model run with without any candidate individuals, using Eq 17. Prevalence Prior sets the prior probability of nosocomiality to the true probability of nosocomiality in that simulation run. Na ve prior sets the prior probability of nosocomiality to 0.5. 96hr Categorisation assigns a nosocomiality probability of 0 to anything detected in the first 96 hours post-admission and a nosocomiality probability of 1 to everything else.