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:

In [1]:

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

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:

In [2]:

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

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:

In [3]:

# 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_combined(results, n_cols=3, dark=True, cmap='Purples')
plt.show()

# 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_combined(results, n_cols=3, dark=True, cmap='Purples') plt.show()

/opt/hostedtoolcache/Python/3.13.11/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/3.13.11/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.