• Predictive Monitoring of COVID-19

    Updated on May 11

    Disclaimer: Content from this website is STRICTLY ONLY for educational and research purposes and may contain errors. The model and data are inaccurate to the complex, evolving, and heterogeneous realities of different countries over time. Predictions are uncertain by nature. Readers must take any predictions with caution. Over-optimism based on some predictions is dangerous because it may loosen our disciplines and controls and cause the turnaround of the virus and infection, and must be avoided. Earlier predictions are no longer valid because the real-world scenarios have changed rapidly.


    The project is internalized. Below are some live public COVID-19 forecasting efforts around the world.

    - Imperial College London https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/

    - University of Geneva, ETH Zürich & EPFL https://renkulab.shinyapps.io/COVID-19-Epidemic-Forecasting/

    - Massachusetts Institute of Technology https://www.covidanalytics.io/projections

    - Los Alamos National Laboratories https://covid-19.bsvgateway.org/

    - The University of Washington, Seattle https://covid19.healthdata.org/projections

    - The University of Texas, Austin https://covid-19.tacc.utexas.edu/projections/

    - Northeastern University https://covid19.gleamproject.org/

    - University of California, Los Angeles https://covid19.uclaml.org/


    COVID-19 predictions are foundamentally important for rationalizing planning and mentality, but also challenging due to the innate uncertainty of the complex, dynamic and global COVID-19 pandemic as a typical wicked problem. Traditional prediction or forecasting efforts, which aim to make an accurate prediction now to come true in the future, might be misleading in this context of extreme uncertaintly. Here, to deal with uncertainty, we had experimented predictive monitoring of the epidemic life cycle curves together with accumulating actual data, to capture changes in the continually-made predictions, which would be traditionally viewed as bad or proof of failure of a prediction model, and make sense such changes as meaningful siganls of uncertainty and changes in the real-world scenarios. In turn, such signals from the predicted theoretical future may inform, initiate, and guide actions now to influence the real future. Motivation, theory, method, cases, and caution are in this white paper.

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