When will the pandemic be over? Computer science may have the answer

In early 2022, nearly two years after Covid was declared a pandemic by the World Health Organization, experts are mulling a big question: when is a pandemic “over”?

So, what’s the answer? What criteria should be used to determine the “end” of Covid’s pandemic phase? These are deceptively simple questions and there are no easy answers.

I am a computer scientist who investigates the development of ontologies. In computing, ontologies are a means to formally structure knowledge of a subject domain, with its entities, relations, and constraints, so that a computer can process it in various applications and help humans to be more precise.

Ontologies can discover knowledge that’s been overlooked until now: in one instance, an ontology identified two additional functional domains in phosphatases (a group of enzymes) and a novel domain architecture of a part of the enzyme. Ontologies also underlie Google’s Knowledge Graph that’s behind those knowledge panels on the right-hand side of a search result.

Applying ontologies to the questions I posed at the start is useful. This approach helps to clarify why it is difficult to specify a cut-off point at which a pandemic can be declared “over”. The process involves collecting definitions and characterizations from domain experts, like epidemiologists and infectious disease scientists, consulting relevant research and other ontologies, and investigating the nature of what entity “X” is.

“X”, here, would be the pandemic itself – not a mere shorthand definition, but looking into the properties of that entity. Such a precise characterization of the “X” will also reveal when an entity is “not an X”. For instance, if X = house, a property of houses is that they all must have a roof; if some object doesn’t have a roof, it definitely isn’t a house.

With those characteristics in hand, a precise, formal specification can be formulated, aided by additional methods and tools. From that, the what or when of “X” – the pandemic is over or it is not – would logically follow. If it doesn’t, at least it will be possible to explain why things are not that straightforward.

This sort of precision complements health experts’ efforts, helping humans to be more precise and communicate more precisely. It forces us to make implicit assumptions explicit and clarifies where disagreements may be.

Definitions and diagrams

I conducted an ontological analysis of “pandemic”. First, I needed to find definitions of a pandemic.

Informally, an epidemic is an occurrence during which there are multiple instances of an infectious disease in organisms, for a limited duration of time, that affects a community of said organisms living in some region. A pandemic, as a minimum, extends the region where the infections take place.

Next, I drew from existing foundational ontologies. This contains generic categories like “object”, “process”, and “quality”. I also used domain ontologies, which contain entities specific to a subject domain, like infectious diseases. Among other resources, I consulted the Infectious Disease Ontology and the Descriptive Ontology for Linguistic and Cognitive Engineering.

First, I aligned “pandemic” to a foundational ontology, using a decision diagram to simplify the process. This helped to work out what kind of thing and generic category “pandemic” is:

(1) Is [pandemic] something that is happening or occurring? Yes (perdurant, i.e., something that unfolds in time, rather than be wholly present).

(2) Are you able to be present or participate in [a pandemic]? Yes (event).

(3) Is [a pandemic] atomic, i.e., has no subdivisions and has a definite endpoint? No (accomplishment).

The word “accomplishment” may seem strange here. But, in this context, it makes clear that a pandemic is a temporal entity with a limited lifespan and will evolve – that is, cease to be a pandemic and evolve back to epidemic, as indicated in this diagram.