Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets.

In this paper we analyze the stochastic version of a minimalistic multi-strain model,which captures essential differences between primary and secondary infections in denguefever epidemiology, and investigate the interplay between stochasticity, seasonality andimport. The introduction of stochasticity is needed to explain the fluctuations observedin some of the available data sets, revealing a scenario where noise and complexdeterministic skeleton strongly interact. For large enough population size, the stochasticsystem can be well described by the deterministic skeleton gaining insight on the relevantparameter values purely on topological information of the dynamics, rather than classicalparameter estimation of which application is in general restricted to fairly simpledynamical scenarios.