Mathematical models of disease spread, and the controls that change them.
A mathematical biology research initiative at Telkom University's School of Computing: compartmental epidemic models, optimal control, and an open-source simulation studio that runs in the browser.
R returns to S as immunity wanes
The simulation studio
Python (NumPy, SciPy, SymPy) compiled to WebAssembly runs inside your browser, so the studio opens at any hour and nothing you type leaves your machine.
- Step 1
Write the model as equations
Type each dX/dt in plain notation, N included. Parameters are picked up from the equations as you type, and the system is shown back as typeset maths.
- Step 2
Compare scenarios side by side
Each scenario overrides only the parameters that change. Peaks, the day of the peak and R₀ are tabulated for every run.
- Step 3
Solve the optimal control problem
The Hamiltonian, adjoint system and control law are derived symbolically, then solved by the forward–backward sweep for every control combination.
Current research
All topics and peopleOptimal control of mpox transmission
An SEIQR model with waning immunity and two interventions, transmission reduction and case detection with isolation. We derive the optimality system with Pontryagin's maximum principle and compare intervention strategies numerically. Manuscript in preparation with collaborators at Universitas Padjadjaran.
OngoingS→E→I→Q→RModelling hantavirus and dengue transmission
A systematic literature review of mathematical models for hantavirus and dengue, mapping which mechanisms, data and control strategies the field has covered so far.
OngoingS→I→RMechanistic models meet machine learning
Few studies combine mechanistic dynamics, optimal control, real surveillance data and machine learning in one framework. We are building towards that combination: fitting compartmental models to data and using learned components where the mechanism is uncertain.
Open for collaboration