Age-Specific Analysis of Influenza Transmission Patterns to Inform Prevention Strategies

Influenza infections pose a persistent global health concern, highlighting the need for robust methods to predict and prevent their spread. We aimed to understand the nuanced impact of different age groups on influenza transmission. Our methodology included cross-correlation and Granger causality analyses, revealing that age groups at the spectrum's extremes, particularly the younger demographic, play a pivotal role in initiating and intensifying epidemics. Through an age-structured reproduction number model, the study identifies the benefits of focusing vaccination efforts precisely on these influential age groups to enhance the effectiveness and cost-efficiency of influenza prevention strategies. The findings emphasize the necessity of age-specific analysis in crafting informed vaccination guidelines and managing responses to respiratory infectious diseases. By collaborating with disease management agencies, these insights can significantly improve policy-making processes, aiding in the reduction of societal burdens associated with the control and prevention of acute respiratory infections. Thus, the research supports the development of targeted, data-driven public health interventions that can better mitigate the impact of future influenza outbreaks.