title: The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach.
abstract: The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control.
authors.affiliation: Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America.
authors.forename: Quirine A
authors.initials: QA
authors.lastname: Ten Bosch
authors.affiliation: Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America.
authors.forename: Brajendra K
authors.identifier: 0000-0001-5847-8750
authors.initials: BK
authors.lastname: Singh
authors.affiliation: Hospital Sultanah Bahiyah, Alor Setar, Kedah, Malaysia.
authors.forename: Muhammad R A
authors.initials: MR
authors.lastname: Hassan
authors.affiliation: Department of Life Sciences, University of the West Indies, Saint Augustine, Trinidad and Tobago.
authors.forename: Dave D
authors.initials: DD
authors.lastname: Chadee
authors.affiliation: Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America.
authors.forename: Edwin
authors.initials: E
authors.lastname: Michael
mesh_terms: D003198:Computer Simulation; D003715:Dengue / Q000453:epidemiology* / Q000276:immunology / Q000517:prevention & control*; D003716:Dengue Virus / Q000145:classification*; D006801:Humans; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D012621:Seasons; D065288:Serogroup; D014300:Trinidad and Tobago / Q000453:epidemiology