Took me a minute to figure out what 'stabilizes' actually meant since immune cells block further spread.
Model the problem as a multi-source BFS where each infected cell has a state (infection day, recovery day) and spreads to neighbors daily. Simulate day by day, tracking active infections and immunity, until no infected cells remain; return the total days elapsed. Clarify edge cases like initial immunity, simultaneous recovery and spread, and grid boundaries.
Pro tip: Explicitly state your assumptions about the spread and recovery order (e.g., recovery happens before spread each day) and mention that you'd validate with small test cases to avoid off-by-one errors.
Ask about initial grid state (infected, healthy, immune), spread direction (4 or 8 neighbors), and whether recovery happens before or after spread on a given day. Confirm what 'fully stabilizes' means (no infected cells).
Use a queue for BFS or a set of active infections. Track for each cell its infection day and recovery day, or maintain day counters. Consider using a 2D array for grid states and a queue for newly infected cells.
At each day, process recoveries (remove cells whose recovery day is today), then spread from currently infected cells to healthy neighbors, marking them infected with recovery day = current day + D. Increment day counter.
Stop when there are no infected cells (active or newly infected). Return the number of days elapsed. Handle the case where the grid starts with no infections (return 0).
Discuss time complexity O(N*M*D) in worst case if simulating day by day, but can be optimized to O(N*M) using BFS with time stamps. Mention space complexity O(N*M).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.