Designing Better Allocation Policies for Influenza Vaccine
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Influenza has been one of the most infectious diseases for roughly 2400 years. The most effective way to prevent influenza outbreaks and eliminate their seasonal effects is vaccination. The distribution of influenza vaccine to various groups in the population becomes an important decision determining the effectiveness of vaccination for the entire population. We developed a simulation model using the Epifire C++ application program  to simulate influenza transmission under a given vaccination strategy. Our model can generate a network that can be configured with different degree distributions, transmission rates, number of nodes and edges, infection periods, and perform chain-binomial based simulation of SIR (Susceptible-Infectious-Recovered) disease transmission. Furthermore, we integrated NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) for optimizing vaccine allocation to various age groups. We calibrate our model according to age specific attack rates from the 1918 pandemic. In our simulation model, we evaluate three different vaccine policies for 36 different scenarios and 1000 individuals: The policy of the Advisory Committee on Immunization Practices (ACIP), former recommendations of the Centers for Disease Control and Prevention (CDC), and new suggestions of the CDC. We derive the number of infected people at the end of each run and calculated the corresponding cost and years of life lost. As a result, we observe that optimized vaccine distribution ensures less infected people and years of life lost compared to the fore-mentioned policies in almost all cases. On the other hand, total costs for the policies are close to each other. Former CDC policy ensures slightly lower cost than other policies and our proposed in some cases.