Fig 1.
Multi state model formulation and defined settings.
(A)Default setting – Extended illness-death model, (B) Setting 1- Default setting + enhanced treatment (only Intervention 1), (C) Setting 2- Default setting + enhanced treatment and infection prevention (combination of interventions 1 and 2). Arrows denote direction of possible transitions; 0-Admission into hospital, 1- Hospital-acquired infections (HAI), 2-Discharged alive after Admission (and stay in hospital setting without acquiring HAI), 3-Death after Admission (and stay in hospital setting without acquiring HAI), 4-Discharged alive after admission followed by HAI, 5-Death after Admission followed by HAI, λij – Transition hazard rate from state i to another sate j, λTij – Transition hazard rate from state i to another sate j in the context of enhanced treatment, θ – Disease prevention factor.
Table 1.
Total transition-specific hazard rates for each setting.
Fig 2.
Overview of the interfaces of the HAISim Shiny App.
A-Interface of HAISim based on Setting 1(Enhanced treatment as the sole intervention); B-Interface of the HAISim based on Setting 2 (Combination of enhanced treatment and prevention strategies). HAISim simulates and evaluates the impact of various intervention strategies on hospital-acquired infections outcomes by modeling different intervention factors.
Fig 3.
Overview of the interfaces of the StaViC Shiny app.
Initial condition (Left plot) vs. After intervention (Right plot). StaViC helps to understand the dynamics of patients who acquire an infection after hospital admission, through the visualization of the probability of patients being in different health states of the model over time.
Table 2.
Estimated number of lives saved and patient-days reduced under different intervention scenarios in simulations using HAISim.
Fig 4.
Stacked probability plots over a 30-day period using StaViC for simulations.