Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand.

M. Aguiar (1) , Richard E. Paul (2) , Anavaj Sakuntabhai (2) , N. Stollenwerk (1)

1 ULISBOA - Universidade de Lisboa = University of Lisbon
2 Génétique fonctionnelle des Maladies infectieuses - Functional Genetics of Infectious Diseases

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.

Keywords: Data analysis; dengue fever; multi-strain model; parameter estimation; predictability

Domains: Life Sciences [q-bio]; Santé publique et épidémiologie

M. Aguiar, Richard E. Paul, Anavaj Sakuntabhai, N. Stollenwerk. Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand.. Epidemiology and Infection, 2014, 142 (11), pp.2447-59. ⟨10.1017/S0950268813003348⟩. ⟨pasteur-02081539⟩