title: Optimizing treatment regimes to hinder antiviral resistance in influenza across time scales.
abstract: The large-scale use of antivirals during influenza pandemics poses a significant selection pressure for drug-resistant pathogens to emerge and spread in a population. This requires treatment strategies to minimize total infections as well as the emergence of resistance. Here we propose a mathematical model in which individuals infected with wild-type influenza, if treated, can develop de novo resistance and further spread the resistant pathogen. Our main purpose is to explore the impact of two important factors influencing treatment effectiveness: i) the relative transmissibility of the drug-resistant strain to wild-type, and ii) the frequency of de novo resistance. For the endemic scenario, we find a condition between these two parameters that indicates whether treatment regimes will be most beneficial at intermediate or more extreme values (e.g., the fraction of infected that are treated). Moreover, we present analytical expressions for effective treatment regimes and provide evidence of its applicability across a range of modeling scenarios: endemic behavior with deterministic homogeneous mixing, and single-epidemic behavior with deterministic homogeneous mixing and stochastic heterogeneous mixing. Therefore, our results provide insights for the control of drug-resistance in influenza across time scales.
authors.affiliation: Mathematical, Computational, and Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America. opatters@asu.edu
authors.forename: Oscar
authors.initials: O
authors.lastname: Patterson-Lomba
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Althouse
authors.forename: Georg M
authors.initials: GM
authors.lastname: Goerg
authors.forename: Laurent
authors.initials: L
authors.lastname: Hébert-Dufresne
mesh_terms: D000998:Antiviral Agents / Q000494:pharmacology* / Q000627:therapeutic use; D024882:Drug Resistance, Viral / Q000187:drug effects*; D019353:Endemic Diseases / Q000517:prevention & control; D019143:Evolution, Molecular; D006801:Humans; D007251:Influenza, Human / Q000188:drug therapy* / Q000453:epidemiology* / Q000635:transmission; D015233:Models, Statistical; D009975:Orthomyxoviridae / Q000187:drug effects / Q000502:physiology; D058873:Pandemics / Q000517:prevention & control; D013997:Time Factors
chemical_list: D000998:Antiviral Agents
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