Does spatial proximity drive norovirus transmission during outbreaks in hospitals?

Harris, John P; Lopman, Ben A; Cooper, Ben S; O'Brien, Sarah J

Despite this limitation, the additional models indicated that that the results are robust to different assumptions about missing data which is evidenced by slightly higher probabilities obtained when using records with complete information only (Table 1 and figure_1_supplement ). As a check to demonstrate that the results were not an artefact of the statistical methods, we also ran the models on data where patient position was randomly assigned. This showed no pattern and the proximity measures were not significant for any of these models. In our analysis the estimation of Pk depends upon outbreak size. However we are not interested in the absolute values of P, only in how the value of P calculated with real proximity data compares with the value calculated with randomly generated proximity data (based on a permutation of the bay identities) which will be affected in the same way by outbreak sizes. We also performed a sensitivity analysis, normalising Pk by dividing it by the number of branches in the transmission tree for each network. This gives equal weight to 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r p e e r r e v i e w o n l y each outbreak and allows Pk to be interpreted as the probability that two linked cases were in the same bay. This did not change the results of the analysis; the P metric still fell well outside of the measure one would expect from the random simulations (p = 0.004, data not shown).