A Rule-Based Epidemiological Modelling Framework

TL;DR

This paper introduces a flexible, rule-based epidemiological modeling framework inspired by chemical reaction rules, enabling systematic, adaptable, and interpretable models for pandemics that can incorporate stochastic or deterministic approaches and facilitate data integration.

Contribution

It presents a novel rule-based modeling framework for epidemiology, allowing for flexible, interconnected models that adapt to changing pandemic conditions and improve communication and data analysis.

Findings

Framework supports stochastic and deterministic models

Enables systematic linking to data and machine learning

Facilitates communication with non-specialists

Abstract

Motivated by chemical reaction rules, we introduce a rule-based epidemiological framework for the systematic mathematical modelling of future pandemics. Here we stress that we do not have a specific model in mind, but a whole collection of models which can be transformed into each other, or represent different aspects of a pandemic, and these aspects can change during the course of the emergency, as happened during the Covid-19 pandemic. As conditions for outbreaks in the modern world change on different time-scales, some rapidly, epidemiology has few 'laws', besides perhaps the fundamental infection process described by Kermack-McKendrick. Each single of our variety of models, called framework, is based on a mathematical formulation that we call a rule-based system. They have several advantages, for example that they can be both interpreted stochastically and deterministically, without changing the model structure. Rule-based systems should be easier to communicate to non-specialists, when compared to differential equations. Due to their combinatorial nature, the rule-based model framework we propose is ideal for systematic mathematical modelling, systematic links to statistics, data analysis in general and also machine learning leading to artificial intelligence.