Irregularity in dengue fever epidemics: difference between first and secondary infections drives the rich dynamics more than the detailed number of strains

TL;DR

This study shows that in dengue fever epidemics, the difference between first and secondary infections causes more complex dynamics than the number of strains, with simpler two-strain models effectively capturing observed fluctuations.

Contribution

The paper demonstrates that the difference between primary and secondary infections drives complex epidemic dynamics more than the number of strains modeled, favoring simpler two-strain models.

Findings

Chaotic dynamics occur in both two- and four-strain models.

Two-strain model effectively explains empirical fluctuations.

Simpler models are preferable for analysis and parameter estimation.

Abstract

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.