title: Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions.
abstract: Theoretical models of disease dynamics on networks can aid our understanding of how infectious diseases spread through a population. Models that incorporate decision-making mechanisms can furthermore capture how behaviour-driven aspects of transmission such as vaccination choices and the use of non-pharmaceutical interventions (NPIs) interact with disease dynamics. However, these two interventions are usually modelled separately. Here, we construct a simulation model of influenza transmission through a contact network, where individuals can choose whether to become vaccinated and/or practice NPIs. These decisions are based on previous experience with the disease, the current state of infection amongst one's contacts, and the personal and social impacts of the choices they make. We find that the interventions interfere with one another: because of negative feedback between intervention uptake and infection prevalence, it is difficult to simultaneously increase uptake of all interventions by changing utilities or perceived risks. However, on account of vaccine efficacy being higher than NPI efficacy, measures to expand NPI practice have only a small net impact on influenza incidence due to strongly mitigating feedback from vaccinating behaviour, whereas expanding vaccine uptake causes a significant net reduction in influenza incidence, despite the reduction of NPI practice in response. As a result, measures that support expansion of only vaccination (such as reducing vaccine cost), or measures that simultaneously support vaccination and NPIs (such as emphasizing harms of influenza infection, or satisfaction from preventing infection in others through both interventions) can significantly reduce influenza incidence, whereas measures that only support expansion of NPI practice (such as making hand sanitizers more available) have little net impact on influenza incidence. (However, measures that improve NPI efficacy may fare better.) We conclude that the impact of interference on programs relying on multiple interventions should be more carefully studied, for both influenza and other infectious diseases.
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Andrews
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada; Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Chris T
authors.initials: CT
authors.lastname: Bauch
mesh_terms: D019295:Computational Biology; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data; D015438:Health Behavior; D006801:Humans; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission; D008954:Models, Biological; D015233:Models, Statistical; D014611:Vaccination / Q000706:statistics & numerical data*
chemical_list: D007252:Influenza Vaccines