Abstract
BackgroundDengue 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.ResultsThe 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.ConclusionsThe 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.
Highlights
Dengue causes considerable morbidity and mortality in Sri Lanka
In this study our objective is to develop a mathematical model that can detect patients proceeding to dengue haemorrhagic fever (DHF) level at an early stage by analysing the combined effect from the parameters sphingosine 1-phosphate (S1P), Interleukin- 1β (IL-1β), Tumor Necrosis Factor (TNF-α), Platelet Activating Factor (PAF) and Interleukin
If the model output value is below 0.36 the patient is considered as DHF and if the model output value is above 0.51 the patient is considered as dengue fever (DF)
Summary
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. 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. A key mechanism of severity is leakage of fluid from blood vessels to surrounding tissues and the resultant drop in volumes within the vascular compartment and hypotension This occurs for about 48 h and is referred to as critical phase [4]. Early clinical diagnosis and careful body fluid management is critical to care
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