Abstract:
Background: Dengue causes considerable morbidity and mortality in Sri Lanka. Inflammatory mediators such as
cytokines, contribute to its evolution from an asymptotic infection to severe forms of dengue. The majority of previous
studies have analysed the association of individual cytokines with clinical disease severity. In contrast, we view
evolution to Dengue Haemorrhagic Fever as the behaviour of a complex dynamic system. We therefore, analyse the
combined effect of multiple cytokines that interact dynamically with each other in order to generate a mathematical
model to predict occurrence of Dengue Haemorrhagic Fever. We expect this to have predictive value in detecting
severe cases and improve outcomes. Platelet activating factor (PAF), Sphingosine 1- Phosphate (S1P), IL-1β, TNFα and
IL-10 are used as the parameters for the model. Hierarchical clustering is used to detect factors that correlated with
each other. Their interactions are mapped using Fuzzy Logic mechanisms with the combination of modified Hamacher
and OWA operators. Trapezoidal membership functions are developed for each of the cytokine parameters and the
degree of unfavourability to attain Dengue Haemorrhagic Fever is measured.
Results: The accuracy of this model in predicting severity level of dengue is 71.43% at 96 h from the onset of illness,
85.00% at 108 h and 76.92% at 120 h. A region of ambiguity is detected in the model for the value range 0.36 to 0.51.
Sensitivity analysis indicates that this is a robust mathematical model.
Conclusions: The results show a robust mathematical model that explains the evolution from dengue to its serious
forms in individual patients with high accuracy. However, this model would have to be further improved by including
additional parameters and should be validated on other data sets.
