This article discusses strategies for mitigating the severity of an influenza pandemic. It presents a comprehensive analysis of various intervention options, including antiviral, vaccine, and non-pharmaceutical measures, using a large-scale epidemic simulation. The study focuses on the United States and Great Britain as examples, examining the effectiveness of different control policies under varying transmission scenarios. Key findings include the limited effectiveness of border restrictions in delaying the spread of a pandemic unless they are extremely effective. School closures can significantly reduce peak attack rates but have little impact on overall attack rates. Case isolation and household quarantine can be effective if feasible. Antiviral treatment is most effective when administered within a day of symptom onset. Household-based prophylaxis combined with reactive school closure can reduce clinical attack rates by 40–50%. More widespread prophylaxis would be more logistically challenging but could reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic can significantly reduce attack rates even if of low efficacy. The study uses an individual-based simulation model to estimate the reproduction number (R0) for pandemic influenza, finding values between 1.4 and 2.0. It also examines the impact of interventions on clinical attack rates rather than mortality, given the lack of data on the virulence of the next pandemic strain and the effects of improvements in medical care. The study highlights the importance of early intervention and the need for a combination of strategies to effectively control a pandemic. It also emphasizes the need for more quantitative data on transmission in different social contexts and the importance of real-time data analysis to adapt interventions to the virus the world faces. The study concludes that a combination of strategies, including vaccination, case isolation, and school closures, is likely to be the most effective in mitigating the impact of a pandemic.This article discusses strategies for mitigating the severity of an influenza pandemic. It presents a comprehensive analysis of various intervention options, including antiviral, vaccine, and non-pharmaceutical measures, using a large-scale epidemic simulation. The study focuses on the United States and Great Britain as examples, examining the effectiveness of different control policies under varying transmission scenarios. Key findings include the limited effectiveness of border restrictions in delaying the spread of a pandemic unless they are extremely effective. School closures can significantly reduce peak attack rates but have little impact on overall attack rates. Case isolation and household quarantine can be effective if feasible. Antiviral treatment is most effective when administered within a day of symptom onset. Household-based prophylaxis combined with reactive school closure can reduce clinical attack rates by 40–50%. More widespread prophylaxis would be more logistically challenging but could reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic can significantly reduce attack rates even if of low efficacy. The study uses an individual-based simulation model to estimate the reproduction number (R0) for pandemic influenza, finding values between 1.4 and 2.0. It also examines the impact of interventions on clinical attack rates rather than mortality, given the lack of data on the virulence of the next pandemic strain and the effects of improvements in medical care. The study highlights the importance of early intervention and the need for a combination of strategies to effectively control a pandemic. It also emphasizes the need for more quantitative data on transmission in different social contexts and the importance of real-time data analysis to adapt interventions to the virus the world faces. The study concludes that a combination of strategies, including vaccination, case isolation, and school closures, is likely to be the most effective in mitigating the impact of a pandemic.