Several methods and models are employed in epidemic prediction, such as: - Statistical Models: These models use historical data to identify trends and correlations. Common techniques include time-series analysis and regression models. - Compartmental Models: These models, like the SIR (Susceptible-Infectious-Recovered) model, divide the population into compartments based on disease status. They help in understanding the dynamics of disease spread. - Agent-Based Models: These simulate the interactions of individuals within a population to predict the spread of disease. They consider individual behaviors and their impact on transmission. - Machine Learning Models: With the rise of big data, machine learning algorithms are increasingly used to predict outbreaks. These models can analyze vast amounts of data and identify complex patterns that traditional models may miss.
