Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

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.