Parameterizing a dynamic inﬂuenza model using longitudinal versus age-stratiﬁed case notiﬁcations yields diﬀerent predictions of vaccine impacts
MetadataShow full item record
Dynamic transmission models of inﬂuenza are often used in decision-making to identify which vaccination strategies might best reduce inﬂuenza-associated health and economic burdens. Our goal was to use laboratory conﬁrmed inﬂuenza cases to ﬁt model parameters in an age-structured, two-type (inﬂuenza A/B) dynamic model of inﬂuenza. We compared the ﬁtted model under two diﬀerent types of ﬁtting methodologies: using longitudinal weekly case notiﬁcation data versus using cross-sectional age-stratiﬁed cumulative case notiﬁcation data. These two approaches allow us to compare model predictions when using two diﬀerent types of model ﬁtting procedures, according to data availability. We ﬁnd that the longitudinal ﬁtting method provides best ﬁtting parameter sets that have a higher variance between the respective parameters in each set than the cross-sectional cumulative case method. Also, model predictions–particularly for inﬂuenza A–are very diﬀerent for the two ﬁtting approaches under hypothetical vaccination scenarios that expand coverage in either younger age classes or older age classes. The cross-sectional method predicts much larger decreases in total cases from baseline vaccination coverage than the longitudinal method. Also, the longitudinal method predicts that vaccinating younger age groups yields greater declines in total cases than vaccinating older age groups, whereas the cross-sectional method predicts the opposite. These results show that the type of data used to ﬁt a dynamic transmission model can produce very diﬀerent outcomes, hence multiple ﬁtting methods should be used whenever possible.
Cite this version of the work
Michael A. Andrews, Chris T. Bauch (2018). Parameterizing a dynamic inﬂuenza model using longitudinal versus age-stratiﬁed case notiﬁcations yields diﬀerent predictions of vaccine impacts. UWSpace. http://hdl.handle.net/10012/13756