Several methods and models are employed in influenza prediction:
Statistical Models: These models use historical data to identify patterns and trends in flu activity. Machine Learning: Advanced algorithms analyze vast datasets to improve prediction accuracy. Syndromic Surveillance: Monitoring symptoms reported by individuals to detect early signs of an outbreak. Virological Surveillance: Tracking virus strains circulating in the population to anticipate future trends. Environmental Data: Incorporating weather and climate data to understand their impact on flu transmission.
