Mohamad Mohsin Mohamad Farhan, Abu Bakar Azuraliza, Hamdan Abdul Razak
Data Mining and Optimization Research Group, Centre for Artificial Intelligence Technology, Faculty of Science & Information Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Appl Soft Comput. 2014 Nov;24:612-622. doi: 10.1016/j.asoc.2014.08.030. Epub 2014 Aug 22.
In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm. To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase.
在疫情爆发检测中，关键问题之一是需要应对早期疫情信号的弱点，因为当未见过的疫情模式与训练模型中的模式不同时，这会导致检测模型在鲁棒性方面的能力较弱。结果，在高检测率和低误报率之间出现了不平衡。为了解决这个问题，本研究提出了一种基于危险理论的新型疫情爆发检测模型；这是一种受生物启发的方法，它复制了人体对抗病原体的方式。我们提出了一种基于累积和与累积成熟抗原接触值的信号形式化方法，以适应疫情爆发特征和危险理论。两种疫情疾病，登革热和非典，都采用了危险理论算法；即树突状细胞算法。为了评估该模型，应用了四个测量指标：检测率、特异性、误报率和准确率。通过实验，所提出的模型优于其他检测方法，并且在两种疾病的疫情爆发检测中都显示出显著的改进。研究结果表明，在处理不一致的疫情信号时，所提出的免疫模型的鲁棒性会增强。该模型能够检测新的未知疫情模式，并且即使在没有训练阶段的情况下，也能够以一致的高检测率、高灵敏度和较低的误报率区分疫情爆发和非疫情爆发情况。
