How to run open-source Tensorflow models on Kubernetes and reviewing how effective the COVID-19 spread model was in measuring the effect of interventions. A team from Imperial College London, whose COVID-19 model ... The authors of the report said: “Young people need to understand the spread of COVID-19. ... As part of rethinking the report for a younger audience, the team ...
How to run open-source Tensorflow models on Kubernetes and reviewing how effective the COVID-19 spread model was in measuring the effect of interventions.
Earlier this month, the United Kingdom became the first European country to approve and administer the first doses of Pfizer/BioNTech’s COVID-19 vaccine. The United States quickly followed suit with the FDA and CDC recently recommending Moderna’s vaccine as well as Pfizer’s to give the world a glimmer of hope. Other international players, notably China and Russia, are also pushing to approve and produce their own vaccines. Even as COVID-19 continues to rage on, this news of vaccines signals a hopeful end in sight.
To that end, I wanted to revisit a study from the Imperial College COVID-19 Response Team, “Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries”, published in March. The study used a semi-mechanistic Bayesian hierarchical model to estimate the impact of non-pharmaceutical interventions such as isolation, the closing of public spaces (e.g. schools, churches, sports arenas), as well as widescale social distancing measures.
The Tensorflow implementation used in the paper is open-sourced under the MIT License and available at [Tensorflow.org_](https://www.tensorflow.org/probability/examples/Estimating_COVID_19_in_11_European_countries) and [Github_](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Estimating_COVID_19_in_11_European_countries.ipynb).
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