Premaratne M K, Perera S S N, Malavige G N, Jayasinghe Saroj
Research and Development Center for Mathematical Modeling, Faculty of Science, University of Colombo, Colombo, Sri Lanka.
Centre for Dengue Research, Faculty of Medicine, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
Comput Math Methods Med. 2017;2017:2187390. doi: 10.1155/2017/2187390. Epub 2017 Feb 15.
. Predicting the risk of severity at an early stage in an individual patient will be invaluable in preventing morbidity and mortality caused by dengue. We hypothesized that such predictions are possible by analyzing multiple parameters using mathematical modeling. . Data from 11 adult patients with dengue fever (DF) and 25 patients with dengue hemorrhagic fever (DHF) were analyzed. Multivariate statistical analysis was performed to study the characteristics and interactions of parameters using dengue NS1 antigen levels, dengue IgG antibody levels, platelet counts, and lymphocyte counts. Fuzzy logic fundamentals were used to map the risk of developing severe forms of dengue. The cumulative effects of the parameters were incorporated using the Hamacher and the OWA operators. . The operator classified the patients according to the severity level during the time period of 96 hours to 120 hours after the onset of fever. The accuracy ranged from 53% to 89%. . The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients. The model allows prediction of severe cases of dengue which could be useful for optimal management of patients during a dengue outbreak. Further analysis of the model may also deepen our understanding of the pathways towards severe illness.
预测个体患者早期病情严重程度的风险对于预防登革热引起的发病和死亡将非常宝贵。我们假设通过使用数学模型分析多个参数可以进行这样的预测。对11例登革热（DF）成年患者和25例登革出血热（DHF）患者的数据进行了分析。使用登革热NS1抗原水平、登革热IgG抗体水平、血小板计数和淋巴细胞计数进行多变量统计分析，以研究参数的特征和相互作用。运用模糊逻辑基本原理来描绘发展为严重登革热形式的风险。使用哈马赫算子和有序加权平均（OWA）算子纳入参数的累积效应。该算子在发热开始后96小时至120小时的时间段内根据严重程度对患者进行分类。准确率在53%至89%之间。结果显示了一个强大的数学模型，该模型解释了个体患者从登革热到其严重形式的演变。该模型能够预测登革热重症病例，这对于登革热疫情期间患者的优化管理可能有用。对该模型的进一步分析也可能加深我们对重症疾病发病途径的理解。
