结合温度海拔建模疟疾传播，用神经网络预测疫情轨迹
Analysis of a mathematical model for malaria using data-driven approach
- 构建考虑温/海拔的疟疾传播模型，分析稳定态
- 三种神经网络从数据中拟合参数，预测五类人群变化
- 用动态模态分解计算疫情风险，适合公共卫生研究者
疟疾是全球最致命疾病之一，每年数百万人感染，众多生命因此逝去。只有清晰理解疾病传播规律，医疗人员与政府才能制定有效防控措施。本文提出一个分组模型研究疟疾传播动力学，考虑传播率随温度和海拔变化。对模型进行稳态分析，验证无病稳态与地方性流行稳态的稳定性。采用人工神经网络（ANN）预测模型五个组别未来轨迹。比较三种网络架构：全连接神经网络（ANN）、卷积神经网络（CNN）与循环神经网络（RNN），用于从数据轨迹中估计参数。为评估疾病严重程度，利用动态模态分解（DMD）从感染者轨迹中计算风险值。
原文摘要 · Abstract (English)
Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.
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