Big data assimilation to improve the predictability of COVID-19

Li, Xin; Zhao, Zebin; Liu, Feng

The global outbreak of COVID-19 requires us to accurately predict the spread of disease and decide how adopting corresponding strategies to ensure the sustainable development. Most of the existing infectious disease forecasting methods are based on the classical Susceptible-Infectious-Removed (SIR) model. However, due to the highly nonlinearity, nonstationarity, sensitivities to initial values and parameters, SIR type models would produce large deviations in the forecast results. Here, we propose a framework of using the Markov Chain Monte Carlo method to estimate the model parameters, and then the data assimilation based on the Ensemble Kalman Filter to update model trajectory by cooperating with the real time confirmed cases, so as to improve the predictability of the pandemic. Based on this framework, we have developed a global COVID-19 real time forecasting system. Moreover, we suggest that big data associated with the spatiotemporally heterogeneous pathological characteristics, social environment in different countries should be assimilated to further improve the COVID-19 predictability. It is hoped that the accurate prediction of COVID-19 will contribute to the adjustments of prevention and control strategies to contain the pandemic, and help achieving the SDG goal of “Good Health and Well-Being”.