Application of glass box AI to large numbers of medical records for rapid response to future respiratory virus pandemics. Examples considering potential future high-fatality COVID strains and a potential avian influenza pandemic in humans

Abstract

It is crucial to consider the consequences that new strains of respiratory viruses such as COVID-19 and avian influenza could have on humans. Possible future human-to-human transmission of avian influenza is of particular concern. As discussed, not all countries took a worst-case approach to COVID-19 at the outset, with regrettable outcomes. To better prepare, it is important to have access to as much information as possible, including digital patient records, and to use that information in a timely fashion so that appropriate actions can be taken early. A glass-box AI approach, complementary to current mainly black-box AI, can effectively manage uncertainty, missing data, and feature interactions in a probabilistic fashion. This approach can obtain standard epidemiological measures, discover unexpected demographic and clinical interactions in past data, and then apply them to small amounts of future data. As this concerns future response, this is primarily a review and position paper. It is emphasized that our results at both the quantitative and qualitative levels are based on models for future pandemics of unknown nature and possibly great severity and are not intended to be realistic. We may sometimes overemphasize severity, but that is a worst-case strategy. We do not consider all epidemiological modeling methods. Rather, this paper concerns how some simple, less variant measures from the first COVID-19 wave and more general qualitative information might be used in combination with analysis of rapidly updated patient records in the first few days of the first wave of a future pandemic.

Keywords

• Pandemics
• Epidemics
• Patient medicial records
• Longtitudinal health records
• Early response
• Epidemiology