The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach.

Quirine A Ten Bosch,Edwin Michael,Dave D Chadee,Muhammad R A Hassan,Brajendra K Singh,Samuel V Scarpino

doi:10.1371/journal.pntd.0004680

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.

Highlights

With a 30-fold increase in incidence over the last five decades, dengue poses an increasing threat to about two thirds of the world population [1]
We demonstrate the ability of pattern-oriented modelling (POM) to model dynamical drivers that have gone unnoticed in single pattern or synthetic likelihood approaches
Dengue, caused by a group of viruses belonging to the Flavivirus genera, circulates in four major serotypes (DENV 1–4) [2], and manifests in a wide spectrum of clinical forms, from subclinical to classic dengue fever to the more serious forms of the disease, namely, dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS)

Summary

Introduction

With a 30-fold increase in incidence over the last five decades, dengue poses an increasing threat to about two thirds of the world population [1]. Dengue dynamics are characterized by highly seasonal, multi-annual fluctuations, with replacement of serotypes occurring at varying intervals An example of these patterns arising in a newly emerging dengue setting is illustrated in (Fig 1) [5,6]. Cross-enhancement is believed to be caused by antibody-dependent enhancement (ADE), where heterotypic antibodies facilitate cell entry through the formation of virion-antibody complexes, leading to increased viral titers upon secondary infection [9,10]. This is thought to result in increased susceptibility to a secondary heterologous infection and, upon these secondary infections, in a more serious form of disease and increased infectiousness. To mimic the distinct seasonal signature of dengue dynamics, the incorporation of seasonal forcing into the vector population dynamics or transmission rate has been found to be essential [19,22,23]

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