Big data assimilation to improve the predictability of COVID-19

Abstract

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, and 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 achieve the SDG goal of “Good Health and Well-Being”.

Keywords

COVID-19 / Data assimilation / Big data / Prediction / Sustainable development / SDG

Declaration of Competing Interest

The authors declare that they have no conflict of interest.

Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20100104), the Science-based Advisory Program of the Alliance of International Science Organizations (Grant No. ANSO-SBA-2020-07), the National Natural Science Foundation of China (Grant No. 41801270), and the Foundation for Excellent Youth Scholars of NIEER, CAS.

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