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Table 3 Result summaries for the outcomes measures from 1000 probabilistic simulations for three best calibrations

From: A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

Total number of:Optimal CalibrationCalibration #2Calibration #3
Calibrated Real life1 (Scenario 1)No WGS (Scenario 2)Early WGS (Scenario 3)Calibrated Real life1No WGSEarly WGSCalibrated Real life1No WGSEarly WGS
Colonized patients (SD)197352 (170)3 (0)136219 (90)3 (0)137448 (320)7 (9)
Detected patients (SD)75152 (75)1 (0)75118 (48)1 (0)73217 (136)4 (7)
Sequencing tests (SD)79 2 (0)77 2 (0)74 4 (7)
Bed closures (SD)419902 (486)11 (2)5221145 (489)5 (1)4281206 (767)19 (28)
Total costs $US (SD)318,654 (8406)531,109 (234,315)45,273 (3155)349,160 (8889)488,962 (178,224)45,637 (2005)322,981 (8441)705,474 (360,758)62,426 (38,507)
Whole genome sequencing costs19,469 (393) 501 (51)18,975 (388) 493 (10)18,221 (385) 916 (1788)
Microbiology testing costs180,933 (7804)292,342 (110,451)40,397 (2775)193,338 (8169)303,916 (104,391)42,248 (1996)186,432 (7862)380,751 (160,304)54,187 (28,663)
Cleaning costs27,811 (1346)58,075 (31,402)733 (205)33,125 (1488)46,446 (20,638)233 (43)29,128 (1362)78,749 (49,321)1406 (1927)
Nursing costs3091 (183)6291 (3229)44 (8)3129 (184)4818 (2068)43 (2)3000 (179)8910 (5843)157 (302)
Infection control executive meetings costs24,366 (439)38,873 (20,044)1946 (124)22,133 (401)29,270 (12,413)1925 (34)21,803 (391)55,779 (35,168)2850 (2224)
Bed closure costs62,984 (2481)135,528 (72,750)1652 (372)78,460 (3012)104,511 (47,699)696 (104)64,397 (2523)181,286 (115,707)2911 (4131)
  1. NB: Empty cells denote where the outcome measures which were not modelled as part of the scenario
  2. SD standard deviation, US United States, WGS whole genome sequencing
  3. 1No standard deviations were reported for non-cost outcome summaries in ‘Calibrated Real life’ as the model was calibrated using this scenario with a fixed outbreak signaling condition. Variation observed in ‘Calibrated Real life’ cost outcomes was due to the stochasticity of the cost parameters solely