PREDICCIONES FIN DE LA PANDEMIA COVID 19 EN EL MUNDO Y EN VARIOS PAISES SEGUN LA INTELIGENCIA ARTIFICIAL

• FUENTE: https://ddi.sutd.edu.sg/

  Data-Driven Innovation - Predictive Monitoring of COVID-19 Life Cycle Curves The Data-Driven Innovation Lab develops data science and artificial intelligence methods and tools to inform and inspire innovation decisions and planning at individual, company and government levels. ddi.sutd.edu.sg

  
  

• https://www.mundomisterioso.net/2020/05/cuando-sera-el-fin-de-la-pandemia-segun-la-inteligencia-artificial.html
  

  Cuando será EL FIN DE LA PANDEMIA… según la INTELIGENCIA ARTIFICIAL - Mundo Misterioso Ha sido el Data-Driven Innovation Lab, un importante centro de Inteligencia Artificial a nivel Mundial quienes han mostrado públicamente un estupendo trabajo en el que nos indican la fecha del final (aproximada) de esta pandemia, son buenas noticias que sin duda esperemos que se cumplan. www.mundomisterioso.net

  
  

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• Predictive Monitoring of COVID-19

  (Updated on May 5)

  *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-recovered) model 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 as the result of the changes in real-world scenarios over time. Monitoring such changes in the predictions of untestable theoretical future events is aimed to sense uncertainty (indicated by volatility) and detect dynamic changes in present real-world scenarios. Motivation, theory, method, examples, and caution are in this paper. Below are other and more systematic COVID-19 forecasting efforts around the world.

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

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

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

  - Massachusetts Institute of Technology https://idss.mit.edu/research/idss-covid-19-collaboration-isolat/

   

  *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.

  

  World

  

  Brazil

  

  United States

  

  Indonesia

  

  Mexico

  

  Sweden

  

  Singapore

  

  Poland

  

  Canada

  

  United Kingdom

  

  Italy

  

  Saudi Arabia

  

  Qatar

  

  Turkey

  

  India

  

  Japan

  

  Russia

  

  United Arab Emirates

  

  France

  

  Germany

  

  Spain

  

  Netherlands

  

  Switzerland
• Due to data limitations, not all countries are included in the analysis. It is strongly suggested to drop the earlier predictions for the countries no longer included due to the rapid changes in real-world scenarios. The list of countries reported will also vary daily depending on data.

Libre de virus. www.avast.com