ArticleEpidemiology and infection2014
Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand.
Article in Epidemiology and infection, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Modelling COVID 19 in the Basque Country from introduction to control measure response.Scientific reports · 2020Article
- The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries.PLoS neglected tropical diseases · 2016Article
- Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?Scientific reports · 2015Article
- Stability, bifurcation and chaos analysis of vector-borne disease model with application to Rift Valley fever.PloS one · 2014Article
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4 authors.
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Abstract
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.
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