COVID-19 Epidemic Modelling (SIRD)
Mar 1, 2025
·
1 min read
An epidemiological modelling project simulating the spread of COVID-19 in Morocco using the SIRD compartmental model — applied mathematics meeting a real-world public health problem.
The model
The SIRD model divides a population into four compartments evolving over time:
| Compartment | Meaning |
|---|---|
| S | Susceptible — not yet infected |
| I | Infected — currently infectious |
| R | Recovered |
| D | Deceased |
The dynamics are governed by a system of ordinary differential equations:
dS/dt = -β·S·I/N
dI/dt = β·S·I/N - γ·I - μ·I
dR/dt = γ·I
dD/dt = μ·I
Where β is the transmission rate, γ the recovery rate, and μ the mortality rate.
My contribution
- Analysed and implemented the SIRD ODE system
- Solved numerically using SciPy to simulate population evolution over time
- Tuned β, γ, and μ parameters against observed Moroccan COVID-19 data
- Visualised S, I, R, D curves and interpreted dynamics — peak infection timing, convergence behaviour, and sensitivity to β changes
Key finding
A small change in the transmission rate β shifts the infection peak by weeks — a concrete illustration of why epidemic modelling matters for policy decisions, and why early intervention has outsized impact.
Tech stack
- Language: Python
- Libraries: NumPy, SciPy (ODE solving), Matplotlib (visualisation)
- Focus: Applied mathematics, numerical methods, data visualisation
