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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2111.07336 |
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| _version_ | 1866929352560607232 |
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| author | Alonso, David Bauer, Steffen Kirkilionis, Markus Kreusser, Lisa Maria Sbano, Luca |
| author_facet | Alonso, David Bauer, Steffen Kirkilionis, Markus Kreusser, Lisa Maria Sbano, Luca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_07336 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Rule-Based Epidemiological Modelling Framework Alonso, David Bauer, Steffen Kirkilionis, Markus Kreusser, Lisa Maria Sbano, Luca Populations and Evolution A.0, I.6 A.0; I.6 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. |
| title | A Rule-Based Epidemiological Modelling Framework |
| topic | Populations and Evolution A.0, I.6 A.0; I.6 |
| url | https://arxiv.org/abs/2111.07336 |