Search

Different extensions of the classical single-strain SIR model for the host population, motivated by modeling dengue fever epidemiology, have reported a rich dynamic structure including deterministic chaos which was able to explain the large fluctuations of disease incidences. A comparison between the basic two-strain dengue model, which already captures differences between primary and secondary infections, with the four-strain dengue model, that introduces the idea of competition of multiple strains in dengue epidemics shows that the difference between first and secondary infections drives the rich dynamics more than the detailed number of strains to be considered in the model structure. Chaotic dynamics were found to happen at the same parameter region of interest, for both the two and the four-strain models, able to explain the fluctuations observed in empirical data and showing a qualitatively good agreement between empirical data and model simulation. Since the law of parsimony favors the simplest of two competing models, the two-strain model would be the better candidate to be analyzed, giving the expected complex behavior to explain the fluctuations observed in empirical data, and indeed the better option for estimating all initial conditions as well as the few model parameters based on the available incidence data., Comment: Letter format of the manuscript which is submitted to PLA, 13 pages, 3 figures

We analyse an epidemiological model of competing strains of pathogens and hence differences in transmission for first versus secondary infection due to interaction of the strains with previously aquired immunities, as has been described for dengue fever (in dengue known as antibody dependent enhancement, ADE). Such models show a rich variety of dynamics through bifurcations up to deterministic chaos. Including temporary cross-immunity even enlarges the parameter range of such chaotic attractors, and also gives rise to various coexisting attractors, which are difficult to identify by standard numerical bifurcation programs using continuation methods. A combination of techniques, including classical bifurcation plots and Lyapunov exponent spectra has to be applied in comparison to get further insight into such dynamical structures. Here we present for the first time multi-parameter studies in a range of biologically plausible values for dengue. The multi-strain interaction with the immune system is expected to also have implications for the epidemiology of other diseases., Comment: 13 pages, 4 fugures; proceedings of CMMSE 2008, ISBN 978-84-612-1982-7

We generalize the well known formulation of the susceptibles, infected, susceptibles (SIS) spatial epidemics with creation and annihilation operators to the reinfection model including recovered which can be reinfected, the SIRI model, using ladder operators constructed from the Gell-Mann matrices known in quantum chromodynamics., Comment: 10 pages

An epidemic multi-strain model with temporary cross-immunity shows chaos, even in a previously unexpected parameter region. Especially dengue fever models with strong enhanced infectivity on secondary infection have previously shown deterministic chaos motivated by experimental findings of antibody-dependent-enhancement (ADE). Including temporary cross-immunity in such models, which is common knowledge among field researchers in dengue, we find a deterministically chaotic attractor in the more realistic parameter region of reduced infectivity on secondary infection (''inverse ADE'' parameter region). This is realistic for dengue fever since on second infection people are more likely to be hospitalized, hence do not contribute to the force of infection as much as people with first infection. Our finding has wider implications beyond dengue in any multi-strain epidemiological systems with altered infectivity upon secondary infection, since we can relax the condition of rather high infectivity on secondary infection previously required for deterministic chaos. For dengue the finding of wide ranges of chaotic attractors open new ways to analysis of existing data sets., Comment: 16 pages, 15 figures