Interdisciplinary Program in Applied Mathematics, University of Arizona, Tucson, Arizona, United States of America.
Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, United States of America.
PLoS Comput Biol. 2021 Nov 19;17(11):e1009467. doi: 10.1371/journal.pcbi.1009467. eCollection 2021 Nov.
We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluate the networks in their ability to replicate the spatiotemporal features of mosquito populations predicted by the mechanistic model, and discuss how augmenting the training data with time series that emphasize specific dynamical behaviors affects model performance. We conclude with an outlook on how such equation-free models may facilitate vector control or the estimation of disease risk at arbitrary spatial scales.
我们提出人工神经网络作为替代蚊子数量机械模型的可行方案。我们开发了前馈神经网络、长短期记忆递归神经网络和门控递归单元网络。我们评估了这些网络在复制机械模型预测的蚊子种群时空特征方面的能力，并讨论了如何通过强调特定动态行为的时间序列来增强训练数据，从而影响模型性能。最后，我们展望了这种无方程模型如何促进在任意空间尺度上进行病媒控制或疾病风险的估计。
