HIV Transmission Dynamics Agent-Based Model

• HIV Transmission Dynamics Agent-Based Model
  • Project Overview
  • Running the Simulation
    • 1. Calibration Phase
    • 2. Main Forward Simulation Phase
    • Output Structure
  • Intervention Scenarios Summary
  • Note

HIV Transmission Dynamics Agent-Based Model

This MATLAB project simulates HIV transmission dynamics within a population of men who have sex with men (MSM) in the Netherlands using an agent-based model (ABM). The model includes two phases: calibration and main forward simulation, with multiple scenarios that allow exploring various intervention simulating increase in diagnosis rates.

Project Overview

1. Calibration Phase: This phase calibrates the model by to fit observed data in 2017-2022. Each calibration run is initialized by specifying the pars_num and traj_num parameters.
2. Main Forward Simulation Phase: After calibration, this phase simulates intervention scenarios by altering diagnosis rates, allowing for comparisons between baseline and intervention scenarios. The simulation starts in 2023 and runs for 10 years.

Running the Simulation

1. Calibration Phase

To start the calibration, use the following function:

Main_Traj(pars_num, traj_num)

• pars_num: Specifies the parameter number (runs from 1 to 100) used to load the configuration file.
• traj_num: Specifies the trajectory number (runs from 1 to 20), representing different trajectories to capture model variability.

Example:

Main_Traj(1, 1)

2. Main Forward Simulation Phase

Once calibration is complete, initiate the forward simulation with various intervention scenarios using:

Main_Traj_Load(pars_num, traj_num, inter_str)

• pars_num: Same parameter number used in the calibration phase.
• traj_num: Same trajectory number used in the calibration phase.
• inter_str: A string specifying the intervention scenario. Available values:
  • 'base': Baseline scenario with no increase in diagnosis rate.
  • 'max': Maximum impact scenario.
  • 'mult_XX_3m': Scenario where the diagnosis rate within the first 3 months of infection is increased by a factor of XX (values: 2, 4, 8, 16, 32).
  • 'mult_XX': Scenario where the diagnosis rate within the first 6 months of infection is increased by a factor of XX (values: 2, 4, 8, 16, 32).

Example:

Main_Traj_Load(1, 1, 'mult_8_3m')

Output Structure

Simulation outputs for calibration are saved in folders structured as follows:

Output_par_num_pars_snum_batch_traj_num

Simulation outputs for mainscenarios are saved in folders structured as follows:

Output_par_num_pars_snum_batch_traj_num_inter_str

Each folder name reflects the parameter settings, trajectory number, and scenario used in that particular run, storing output data for each specific intervention scenario.

Intervention Scenarios Summary

• base: Baseline scenario, no additional diagnosis interventions.
• max: Maximum intervention impact scenario.
• mult_XX_3m: Increased diagnosis rate within the first 3 months of infection by a factor of XX.
• mult_XX: Increased diagnosis rate within the first 6 months of infection by a factor of XX.

By running various scenarios, this model provides insights into the potential impact of increased diagnosis rates on HIV transmission dynamics within the MSM population.

Note

Calibration rounds creates a folder State with subfolders State_pars_XX_batch_YY:

• XX: Parameter number used in the calibration phase.
• YY: Trajectory number used in the calibration phase.

These folders contain snapshot of the population at the end of the calibration round, which is used to initialize the model in the main scenario simulation.

Note: Ensure that all dependencies are correctly installed and configured in MATLAB before running the simulations.