Analysis Seminar - 19/2/2014

Wednesday, 19 February 2014, 2:00 p.m.

Lecturer: Maíra Aguiar, Centro de Matemática e Aplicações Fundamentais da Universidade de Lisboa

Title: "Modelling dengue fever epidemiology: complex dynamics and its implication for data analysis".

Local: Room 1.5, Edifício VII
Faculdade de Ciências e Tecnologia, Quinta da Torre, Caparica

Abstract: Dengue fever dynamics is well known to be particularly complex with large fluctuations of disease incidences. Mathematical models describing the irregular behaviour of dengue epidemics are parametrized on data referring to incidence and ultimately aim to be used as a predictive tool that can be used by the public health authorities of disease control.

In dengue fever epidemiology there are four antigenically related but distinct serotypes, raising many complications in the analysis and interpretation of the available incidence data. Antibodies generated by exposure to any one strain are known to be cross-reactive for other strains, but they are believed only to provide strain-speci fic lifelong immunity to reinfection, whereas subsequent infections by other serotypes (one of the three heterologous serotypes) increase the risk of developing severe dengue. The high antibody titers attained after primary infection appear to generate a degree of cross-protection for a while, but if secondary exposure occurs after antibody levels begin to decline, cross-reactivity appears to act to enhance the growth rate of the new invading viral strain. This is called antibody-dependent enhancement and its occurrence in dengue has been used to explain the etiology of severe disease.

Multi-strain dengue models are often modelled with SIR-type models where the SIR classes are labelled for the hosts that have seen the individual strains. In this talk, we present a set of models motivated by dengue fever epidemiology and compare diff erent dynamical behaviours originated when increasing complexity into model framework, anticipating that temporary cross-immunity and di fference between primary and secondary infections appear to be the key factors determining disease transmission, outcome of infection and epidemics. These models are parametrized on the official notifi cation dengue data from Bureau of Epidemiology, Ministry of Public Health in Thailand. The extended models show complex dynamics and qualitatively a very good result when comparing empirical data and model simulations.