title: Modeling optimal treatment strategies in a heterogeneous mixing model.
abstract: BACKGROUND
  Many mathematical models assume random or homogeneous mixing for various infectious diseases. Homogeneous mixing can be generalized to mathematical models with multi-patches or age structure by incorporating contact matrices to capture the dynamics of the heterogeneously mixing populations. Contact or mixing patterns are difficult to measure in many infectious diseases including influenza. Mixing patterns are considered to be one of the critical factors for infectious disease modeling.
  
  
  METHODS
  A two-group influenza model is considered to evaluate the impact of heterogeneous mixing on the influenza transmission dynamics. Heterogeneous mixing between two groups with two different activity levels includes proportionate mixing, preferred mixing and like-with-like mixing. Furthermore, the optimal control problem is formulated in this two-group influenza model to identify the group-specific optimal treatment strategies at a minimal cost. We investigate group-specific optimal treatment strategies under various mixing scenarios.
  
  
  RESULTS
  The characteristics of the two-group influenza dynamics have been investigated in terms of the basic reproduction number and the final epidemic size under various mixing scenarios. As the mixing patterns become proportionate mixing, the basic reproduction number becomes smaller; however, the final epidemic size becomes larger. This is due to the fact that the number of infected people increases only slightly in the higher activity level group, while the number of infected people increases more significantly in the lower activity level group. Our results indicate that more intensive treatment of both groups at the early stage is the most effective treatment regardless of the mixing scenario. However, proportionate mixing requires more treated cases for all combinations of different group activity levels and group population sizes.
  
  
  CONCLUSIONS
  Mixing patterns can play a critical role in the effectiveness of optimal treatments. As the mixing becomes more like-with-like mixing, treating the higher activity group in the population is almost as effective as treating the entire populations since it reduces the number of disease cases effectively but only requires similar treatments. The gain becomes more pronounced as the basic reproduction number increases. This can be a critical issue which must be considered for future pandemic influenza interventions, especially when there are limited resources available.
authors.affiliation: Department of Mathematics, Graduate School, Kyung Hee University, Seoul, 02447, Korea. seoyunchoe@khu.ac.kr.
authors.forename: Seoyun
authors.initials: S
authors.lastname: Choe
authors.affiliation: Department of Applied Mathematics, Kyung Hee University, Yongin-si, 446-701, Korea. mathever@gmail.com.
authors.forename: Sunmi
authors.initials: S
authors.lastname: Lee
mesh_terms: D000367:Age Factors; D050936:Basic Reproduction Number; D003198:Computer Simulation; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology / Q000628:therapy*; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted