  We have developed a globally applicable diagnostic Covid-19 model by
augmenting the classical SIR epidemiological model with a neural network
module. Our model does not rely upon previous epidemics like SARS/MERS and all
parameters are optimized via machine learning algorithms employed on publicly
available Covid-19 data. The model decomposes the contributions to the
infection timeseries to analyze and compare the role of quarantine control
policies employed in highly affected regions of Europe, North America, South
America and Asia in controlling the spread of the virus. For all continents
considered, our results show a generally strong correlation between
strengthening of the quarantine controls as learnt by the model and actions
taken by the regions' respective governments. Finally, we have hosted our
quarantine diagnosis results for the top 70 affected countries worldwide, on a
public platform, which can be used for informed decision making by public
health officials and researchers alike.
