Background: Dengue causes considerable morbidity and mortality in Sri Lanka. Immune mediated and cytokine related factors contribute to its evolution from an asymptotic infection to severe forms of dengue. Previous studies have analysed the association of individual cytokines with clinical disease severity. In contrast, we have viewed this evolution to severe dengue as the behaviour of a complex dynamic system. We therefore analysed the combined effect of multiple cytokines that interact dynamically with each other in order to generate a mathematical model to predict the occurrence of severe dengue. We expect this to have predictive value in detecting severe cases and improve outcomes. Methods & Materials: We analysed data on 11 adult patients with dengue fever (DF) and 25 patients with dengue haemorrhagic fever (DHF) recruited from the Colombo South Teaching Hospital, Sri Lanka. Platelet activating factor (PAF), sphingosine 1- phosphatase (S1P), IL1beta, TNFalpha and IL10 were used as the cytokine parameters for the model. Hierarchical clustering was used to detect factors that correlated with each other. Their interactions were mapped using Fuzzy Logic mechanisms with the combination of Hamacher and OWA operators. Results: Clustering indicated that S1P and IL1beta levels were associated with each other. Since, PAF, IL-10 and TNF-alpha have shown to associate with severe dengue, they were combined together by allocating these cytokines a higher prominence in the model. Operator value below 0.3 in the overall model correctly predicted development of DHF with 76.6% accuracy. A region of ambiguity was detected in the model for the value range 0.35 to 0.55. However, in six instances patients with DHF indicated operator values above 0.6 and in four instances, patients with DF showed operator values below 0.35. The accuracy of this model in predicting severe dengue was 76.19% at 96 hours from the onset of illness, 75% at 108 hours and 74.07% at 120 hours. Conclusion: The results show a robust mathematical model that explains the evolution of dengue infection to its serious forms. This model should be further improved by including additional parameters and be validated on other data sets.
Jayasundara, P., Malavige, N., Perera, S., & Jayasinghe, S. (2016). Dengue: Mathematical modelling of cytokine levels in the evoultion of severity. International Journal of Infectious Diseases, 45, 333. https://doi.org/10.1016/j.ijid.2016.02.721