Malnutrition Tutorial

This tutorial demonstrates how to use the malnutrition comorbidity module in the tbsim package.

Module Location: tbsim/comorbidities/malnutrition/

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Overview

The malnutrition module in TBSim allows users to simulate the impact of malnutrition on TB disease dynamics. Malnutrition is a significant risk factor for TB, affecting both susceptibility to infection and disease progression.

This tutorial shows how to: - Set up a basic malnutrition simulation - Configure malnutrition parameters - Run the simulation and visualize results

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Malnutrition Model

The malnutrition model simulates nutritional status and its impact on health outcomes. Key features include:

• Nutritional Status Tracking: Monitors individual nutritional states
• Health Impact: Affects susceptibility to diseases and recovery rates
• Demographic Integration: Works with birth and death processes

Key Parameters

Parameter	Description
init_prev	Initial prevalence of malnutrition in the population
recovery_rate	Rate at which individuals recover from malnutrition
susceptibility_multiplier	Multiplier for disease susceptibility when malnourished

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Setup and Imports

First, import the required modules:

import tbsim
import starsim as ss
import matplotlib.pyplot as plt
import numpy as np

Building the Malnutrition Simulation

The make_malnutrition() function creates a complete simulation with malnutrition as the primary disease model:

def make_malnutrition():
    # --------- Disease ----------
    nut_pars = dict()  # Use default malnutrition parameters
    nut = tbsim.Malnutrition(nut_pars)
    
    # --------- People ----------
    n_agents = 200  # Small population for demonstration
    pop = ss.People(n_agents=n_agents)
    
    # -------- Simulation Parameters -------
    sim_pars = dict(
        dt=ss.days(7),                    # Weekly time steps
        start=ss.date('1990'),      # Simulation start date
        stop=ss.date('2020'),       # Simulation end date (30 years)
    )
    
    # -------- Contact Network ----------
    net = ss.RandomNet(dict(
        n_contacts=ss.poisson(lam=5),     # Average 5 contacts per agent
        dur=0                             # Instantaneous contacts
    ))
    
    # -------- Demographics ------------
    births = ss.Births(pars=dict(birth_rate=5))    # 5 births per 1000 per year
    deaths = ss.Deaths(pars=dict(death_rate=5))    # 5 deaths per 1000 per year
    
    # -------- Assemble Simulation -----
    sim = ss.Sim(
        people=pop, 
        diseases=nut, 
        demographics=[deaths, births],
        networks=net,
        pars=sim_pars,
        verbose=0,
    )
    return sim

Running the Simulation

Execute the simulation and visualize the results:

# Create and run the malnutrition simulation
sim_n = make_malnutrition()
sim_n.run()

# Process results for visualization
results = {'malnutrition': sim_n.results.flatten()}

# Plot results with 3 columns in dark mode
tbsim.plot(results, n_cols=3)
plt.show()

/opt/hostedtoolcache/Python/3.13.13/x64/lib/python3.13/site-packages/scipy/stats/_distn_infrastructure.py:2334: RuntimeWarning: invalid value encountered in multiply
  lower_bound = _a * scale + loc
/opt/hostedtoolcache/Python/3.13.13/x64/lib/python3.13/site-packages/scipy/stats/_distn_infrastructure.py:2335: RuntimeWarning: invalid value encountered in multiply
  upper_bound = _b * scale + loc

Key Parameters Explained

• dt: Time step duration (7 days = weekly updates)
• n_agents: Population size (200 agents for demonstration)
• n_contacts: Number of contacts per agent (Poisson distribution with mean 5)
• birth_rate/death_rate: Demographic rates per 1000 population per year
• Simulation period: 30 years (1990-2020)

Expected Output

The simulation will generate plots showing: - Malnutrition prevalence over time - Population demographics (births, deaths) - Contact network statistics - Nutritional status transitions

Extending the Model

This basic malnutrition model can be extended to: - Include TB as a comorbidity - Add nutritional interventions - Model seasonal variations in nutrition - Include age-specific nutritional requirements

For more complex scenarios, see the TB-malnutrition comorbidity examples in the interventions tutorials.