Non-linear models in epidemiology often involve differential equations that describe how disease states (such as susceptible, infected, and recovered populations) change over time. These models can incorporate various factors such as contact rates, transmission probabilities, and recovery rates. Some common non-linear models include: - SIR (Susceptible-Infected-Recovered): This classic model divides the population into three compartments and uses differential equations to describe the flow between these states. - SEIR (Susceptible-Exposed-Infected-Recovered): This model adds an "exposed" state to capture the incubation period of a disease. - Agent-Based Models: These models simulate individual agents with specific rules, allowing for the emergence of non-linear patterns.
