Model Overview

Note

NOTE: AI Generated. Still to be verified by Willy.

Disease Transmission Dynamics

The model simulates disease transmission using a modified SIS (Susceptible-Infected-Susceptible) framework:

Disease States

Each patient has an associated PersonDisease object that tracks:

  • Susceptible: Patient is not colonized
  • Colonized: Patient is asymptomatic carrier of the disease
  • Clinically Infected: Patient shows symptoms (detected or not)

State Transitions

Susceptible
    ↓ (transmission event)
Colonized
    ├→ Detected By Surveillance
    ├→ Clinically Detected
    └→ Decolonized (natural clearance)
  ↓
Susceptible

Disease Importation

Admission-Based Importation

When a new patient is admitted:

  1. Importation probability (importationRate, default: 0.206) determines if they arrive colonized
  2. If colonized, they immediately enter the Colonized state
  3. They begin shedding and can transmit to other patients

Configuration

<parameter name="importationRate" value="0.206"/>
<!-- Probability (0-1) that new admits arrive colonized -->

Transmission

Beta-Based Transmission

Note

TODO: Find a link to the mathematical explanation of this.

Disease transmission within the facility follows a contact transmission model:

  • Beta coefficient (longTermAcuteCareBeta, default: 0.0615)
    • Probability of transmission per unit time between colonized and susceptible patient
    • Accounts for contact rates, pathogen viability, and inoculum size
  • Transmission rate is updated when:
    • Patient is admitted
    • Patient is discharged
    • Isolation status changes
    • Surveillance testing changes

Isolation Effectiveness

When a patient is isolated:

  • Isolation effectiveness (isolationEffectiveness, default: 0.5)
    • Multiplier applied to beta when patient is isolated
    • 0.5 = 50% reduction in transmission probability
    • Represents practice like hand hygiene, PPE, contact precautions

Surveillance & Detection

Active Surveillance Testing

Admission Surveillance

  • Triggered: When patient admitted (if onActiveSurveillance = true)
  • Probability: probSurveillanceDetection per disease
  • Adherence: admissionSurveillanceAdherence (default: 0.911)
  • Outcome: If positive, patient is isolated

Periodic Surveillance

  • Interval: daysBetweenMidstaySurveillanceTests (default: 14 days)
  • Adherence: midstaySurveillanceAdherence (default: 0.954)
  • Scheduling: Starts when patient is admitted

Clinical Detection

  • Natural detection: Colonized patients may develop symptoms and be detected
  • Detection rate: Exponentially-distributed with mean avgTimeToClinicDetection
  • Isolation: Clinically detected patients are immediately isolated

Decolonization

Spontaneous Clearance

  • Duration: Exponentially-distributed with mean avgDecolonizationTime (default: 387 days)
  • Outcome: Patient transitions from Colonized → Susceptible
  • No intervention: Does not require treatment in the baseline model

Population Dynamics

Patient Flow

  1. Admission
    • Continuous process with rate newPatientAdmissionRate
    • Length of stay (LOS) sampled from facility-specific distribution
  2. Length of Stay
    • Type 0 (LTAC): Mixture of two gamma distributions
      • 62.5% probability: Gamma(shape=7.6, scale=3.4) days
      • 37.5% probability: Gamma(shape=1.2, scale=23.5) days
  3. Discharge
    • Patient is removed when LOS expires
    • Transmission contribution is updated
    • Patient statistics (patient-days) are recorded

Simulation Timeline

Burn-In Period (10 years)

  • Allows system to reach equilibrium
  • No statistics collected
  • No patient tracking
  • Sets up realistic initial conditions

Measurement Period (5 years)

  • All statistics are collected
  • Event logs are recorded
  • Output files are generated

Key Time Representations

  • 1.0 tick = 1 day
  • 1.5 tick = midday (0.5 × 24 hours into the day)
  • Total duration = 5475 ticks = 15 years (10 burn-in + 5 measurement)

Facility Types

Currently supports one facility type:

Type 0: Long-Term Acute Care (LTAC)

  • High prevalence of comorbidities
  • Longer stays than acute care
  • Higher disease transmission risk
  • Default for single-facility model

Output Statistics

Summary Statistics

  • Total admissions and patient-days
  • Prevalence of colonization
  • Incidence of disease
  • Detection rates
  • Transmission events

Time Series Data

  • Daily population size
  • Daily prevalence
  • Daily incidence
  • Cumulative outcomes

Event Logs

  • Each admission with importation status
  • Each transmission event
  • Each clinical/surveillance detection
  • Each decolonization event

Key Assumptions

  1. Homogeneous mixing: All patients have equal contact probability (modified by isolation)
  2. Independent diseases: Multiple diseases tracked independently
  3. Perfect detection: Detected patients are immediately and perfectly isolated
  4. No treatment: Decolonization only through natural clearance
  5. No reinfection: Once decolonized, patients are not immediately re-infected
  6. Exponential distributions: Natural processes follow exponential distributions

Model Limitations

  • Single facility (no multi-facility transmission)
  • No spatial structure (everyone can contact everyone)
  • No seasonality effects
  • No staff agents (only patient populations)
  • Simplified detection and isolation mechanics
  • No cost/economic considerations

Next Steps