Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0415-3) contains supplementary material, which is available to authorized users.

Developing a Decision Support Testing Algorithm to Detect Severity Level of Dengue

“…This study is an attempt to develop a decision model to predict the severity level of dengue patients and is an improved extension of the models in (Jayasundara, et al, 2017) and (Premaratne, et al, 2017). Three variations are carried out.…”

“…The fuzzy membership functions are developed according to the previous studies (Jayasundara, et al, 2017) and (Premaratne, et al, 2017). The membership functions for platelet, dengue NS1 antigen, lymphocyte and dengue IgG antibody levels are given in equations (7), (8), (9) and (10) Decision Tree Output IID3 algorithm based decision trees are implemented for 96 and 108 hours from onset of illness for the parameters in Model A and Model B.…”

“…where ( ), 10 ( ) are the membership values of TNF-α and IL-10 respectively and both are concentrated by 1.1 as in (Jayasundara, et al, 2017). H1 produces the Hamacher product between TNF-α and IL-10.…”

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

“…To forecast dengue outbreak in southern China, researchers apply GAM [32], SVR as well as variations of regression modeling [33], and the factor-detector modeling module of the ArgGIS software [34]. Researchers in Sri Lanka adopt fuzzy system with trapezoidal membership function to predict the developing from classic dengue fever to the severe level using clinical evidences as input data [35].…”

Management of dengue infection: improving morbidity

“…This leads to development of pleural effusion, ascites and shock [13]. A mathematical model had been developed to predict development of DHF looking at a combination of cytokines [14].…”