Reservoir computing on epidemic spreading: A case study on COVID-19 cases,Physical Review E

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Reservoir computing on epidemic spreading: A case study on COVID-19 cases
Physical Review E ( IF 2.2 ) Pub Date : 2021-07-16 , DOI: 10.1103/physreve.104.014308
Subrata Ghosh ₁ , Abhishek Senapati _{2,

3} , Arindam Mishra ₄ , Joydev Chattopadhyay ₂ , Syamal K Dana ₄ , Chittaranjan Hens ₁ , Dibakar Ghosh ₁

Affiliation

A reservoir computing based echo state network (ESN) is used here for the purpose of predicting the spread of a disease. The current infection trends of a disease in some targeted locations are efficiently captured by the ESN when it is fed with the infection data for other locations. The performance of the ESN is first tested with synthetic data generated by numerical simulations of independent uncoupled patches, each governed by the classical susceptible-infected-recovery model for a choice of distributed infection parameters. From a large pool of synthetic data, the ESN predicts the current trend of infection in

5 %

patches by exploiting the uncorrelated infection trend of

95 %

patches. The prediction remains consistent for most of the patches for approximately 4 to 5 weeks. The machine's performance is further tested with real data on the current COVID-19 pandemic collected for different countries. We show that our proposed scheme is able to predict the trend of the disease for up to 3 weeks for some targeted locations. An important point is that no detailed information on the epidemiological rate parameters is needed; the success of the machine rather depends on the history of the disease progress represented by the time-evolving data sets of a large number of locations. Finally, we apply a modified version of our proposed scheme for the purpose of future forecasting.

中文翻译：

流行病传播的水库计算：以 COVID-19 病例为例

基于水库计算的回波状态网络 (ESN) 在这里用于预测疾病的传播。当 ESN 收到其他位置的感染数据时，ESN 可以有效地捕获某些目标位置中疾病的当前感染趋势。ESN 的性能首先使用由独立非耦合补丁的数值模拟生成的合成数据进行测试，每个补丁都由经典的易感感染恢复模型控制，以选择分布式感染参数。从大量合成数据中，ESN 预测了当前的感染趋势

5 %

通过利用不相关的感染趋势进行补丁

95 %

补丁。大多数补丁的预测在大约 4 到 5 周内保持一致。该机器的性能通过为不同国家收集的当前 COVID-19 大流行的真实数据进一步测试。我们表明，我们提出的方案能够预测某些目标位置长达 3 周的疾病趋势。重要的一点是不需要关于流行病学速率参数的详细信息；机器的成功更取决于由大量位置随时间演变的数据集所代表的疾病进展的历史。最后，为了未来的预测，我们应用了我们提出的方案的修改版本。

更新日期：2021-07-16

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