Individual variation in infectiousness significantly influences disease emergence and outbreak dynamics. The basic reproductive number, R₀, represents the average number of secondary infections caused by an infected individual, but it may obscure individual differences in infectiousness. This study shows that infectiousness varies widely among individuals, with some causing many more infections than others, leading to 'superspreading events'. These events are not exceptional but are part of the distribution of infectiousness. The study uses contact tracing data to analyze infectiousness variation and shows that the distribution of infectiousness is often highly skewed. Models that account for this variation differ from average-based models, leading to different predictions about disease extinction and outbreak patterns. The study proposes a rigorous definition for superspreading events and a method to predict their frequency. It also highlights the importance of identifying predictive correlates of higher infectiousness for effective control measures. The findings suggest that superspreading is a normal feature of disease spread, and that targeted control measures are more effective than population-wide measures. The study also shows that individual variation in infectiousness is crucial for understanding disease dynamics, and that neglecting this variation can lead to inaccurate predictions. The study provides a framework for analyzing infectiousness variation and its impact on disease outbreaks, and emphasizes the need for detailed transmission tracing data to better understand disease dynamics. The study also highlights the importance of considering individual variation in infectiousness when developing control strategies, as this can significantly improve the effectiveness of interventions. The study concludes that individual variation in infectiousness is a key factor in disease emergence and that further research is needed to better understand and manage this variation.Individual variation in infectiousness significantly influences disease emergence and outbreak dynamics. The basic reproductive number, R₀, represents the average number of secondary infections caused by an infected individual, but it may obscure individual differences in infectiousness. This study shows that infectiousness varies widely among individuals, with some causing many more infections than others, leading to 'superspreading events'. These events are not exceptional but are part of the distribution of infectiousness. The study uses contact tracing data to analyze infectiousness variation and shows that the distribution of infectiousness is often highly skewed. Models that account for this variation differ from average-based models, leading to different predictions about disease extinction and outbreak patterns. The study proposes a rigorous definition for superspreading events and a method to predict their frequency. It also highlights the importance of identifying predictive correlates of higher infectiousness for effective control measures. The findings suggest that superspreading is a normal feature of disease spread, and that targeted control measures are more effective than population-wide measures. The study also shows that individual variation in infectiousness is crucial for understanding disease dynamics, and that neglecting this variation can lead to inaccurate predictions. The study provides a framework for analyzing infectiousness variation and its impact on disease outbreaks, and emphasizes the need for detailed transmission tracing data to better understand disease dynamics. The study also highlights the importance of considering individual variation in infectiousness when developing control strategies, as this can significantly improve the effectiveness of interventions. The study concludes that individual variation in infectiousness is a key factor in disease emergence and that further research is needed to better understand and manage this variation.