Purpose

Epidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.

Design/methodology/approach

A deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.

Findings

The experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.

Originality/value

The proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.

中文翻译：

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智慧城市中基于深度神经网络的情境感知智能流行病监测

目的

流行病不仅影响公众健康，而且对一个国家的增长和经济也构成威胁。对流行病进行早期预测，有利于采取预防措施，减少流行病在一个地区的影响。

设计/方法/途径

已经提出了一种基于深度神经网络 (DNN) 的情境感知智能流行病系统，以预防和监测地理区域中的流行病传播。各种神经网络 (NN) 已被使用：LSTM、RNN、BPNN 来检测疾病的级别、地理区域中疾病传播的方向并标记高风险区域。多个 DNN 收集和处理各种数据点，这些 DNN 是根据数据点的类型决定的。一个 DNN 的输出被另一个 DNN 使用以达到最终预测。

发现

对于 2016 年巴西寨卡流行病生成的合成数据集，所提出框架的实验评估达到了 87% 的准确率。

原创性/价值

所提议的框架的设计方式是每个数据点都经过仔细处理并有助于最终决策。这些多个 DNN 将充当最终用户的单个 DNN。