*Statement: This site presents an independent research project, not funded by any agency, and not tied to any company, government, or political party. We acknowledge the enormous input, feedback, inspiration, and support of the people and the global community that has allowed for the continual improvement of this research.

 

The site provides continuous predictive monitoring of COVID-19 as a complement to traditional monitoring or traditional prediction practices. SIR (susceptible-infected-removed) model of epidemic dynamics is regressed with daily updated data from different countries to estimate the pandemic life cycle curves and theoretical ending dates, with codes from Milan Batista and data from Our World in Data. The continuously updated predictions with the latest data are expected to change and capture the changes in real-world scenarios over time. Therefore, our effort differs fundamentally from the traditional prediction exercises that hope a prediction made now be accurate about the real future. However, the current pandemic is a wicked problem and makes the intent for prediction accuracy be misleading. Here we aim to monitor the changes in the estimations of the overall pandemic life cycle curves and predicted futures as the signals of the uncertainty (indicated by volatility) and the changes in the present real-world scenarios, which in turn may inform, initiate, and guide actions now to influence the real future. Motivation, theory, method, examples, and caution are in this paper.

 

We will discontinue daily updates soon. Below are other COVID-19 forecasting efforts around the world.

 

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

 

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

 

3. Massachusetts Institute of Technology www.covidanalytics.io/projections

 

4. Los Alamos National Laboratories covid-19.bsvgateway.org/

 

5. The University of Washington, Seattle covid19.healthdata.org/projections

 

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

 

7. Northeastern University covid19.gleamproject.org/

 

8. University of California, Los Angeles covid19.uclaml.org/

 

 

*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. Predictions are uncertain by nature. Readers must take any predictions with caution. Over-optimism based on some predicted end dates is dangerous because it may loosen our disciplines and controls and cause the turnaround of the virus and infection, and must be avoided.