I remember it being a grid traversal type problem but I can't recall the exact constraints.
Model the population as a grid or graph where each node has a state (susceptible, infected, recovered). Simulate time steps, updating states based on infection and recovery probabilities, and track the spread. Discuss trade-offs between different models and optimizations for large populations.
Pro tip: Clarify assumptions early (e.g., homogeneous mixing, fixed infection rate) and mention that the model can be extended to include vaccination, immunity, or network effects. This shows you think beyond the basic simulation.
Ask about the population structure (grid, graph, or well-mixed), parameters (infection rate, recovery rate), and time granularity. State your assumptions clearly.
Select a model like SIR (Susceptible-Infected-Recovered) or SEIR, and decide on the spatial representation (e.g., 2D grid with Moore neighborhood).
Outline the step-by-step process: initialize states, iterate over time steps, compute new states based on neighbors and probabilities, and handle recoveries.
Write pseudocode or code, using efficient data structures (e.g., arrays for grids, adjacency lists for graphs). Discuss optimizations like parallelization or sparse updates.
Test with small cases, visualize the spread, and compute metrics like peak infection rate or total infected. Discuss how to validate against real-world data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.