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We develop here a data-driven approach for disease recognition based on givensymptoms, to be efficient tool for anomaly detection. In a clinical setting andwhen presented with a patient with a combination of traits, a doctor may wonderif a certain combination of symptoms may be especially predictive, such as thequestion, "Are fevers more informative in women than men?" The answer to thisquestion is, yes. We develop here a methodology to enumerate such questions, tolearn what are the stronger warning signs when attempting to diagnose adisease, called Conditional Predictive Informativity, (CPI), whose ranking wecall CPIR. This simple to use process allows us to identify particularlyinformative combinations of symptoms and traits that may help medical fieldanalysis in general, and possibly to become a new data-driven advised approachfor individual medical diagnosis, as well as for broader public policydiscussion. In particular we have been motivated to develop this tool in thecurrent enviroment of the pressing world crisis due to the COVID 19 pandemic.We apply the methods here to data collected from national, provincial, andmunicipal health reports, as well as additional information from online, andthen curated to an online publically available Github repository.

Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19