Information clustering and pathogen evolution

Baptiste Filoche and Stefan Hohenegger

Physica A: Statistical Mechanics and its Applications, 2025, vol. 672, issue C

Abstract: Recent outbreaks of infectious diseases have been monitored closely from an epidemiological and microbiological perspective. Extracting from this wealth of data the information that is relevant for the evolution of the pathogen and predict the further dynamic of the epidemic is a difficult task. We therefore consider clusterings of these data to condense this information. We interpret the relative abundance of (genetic) variants of the pathogen as a time-dependent probability distribution and consider clusterings that keep the Fisher information (approximately) invariant, in order to ensure that they capture the dynamics of the pandemic. By first studying analytic models, we show that this condition groups variants together that interact in a similar fashion with the population and show comparable adaptation to the epidemiological situation. Moreover, we demonstrate that the same clustering can be achieved by grouping variants together according to the time-derivative of their information, which is defined as a function of the probabilities alone. A computationally simple clustering based on the probability distribution therefore allows us to probe interactions of different variants of the pathogen with its environment. To validate these findings, we consider data of 551.459 amino acid sequences of the spike protein of SARS-CoV-2 in France over the course of almost 4 years. We demonstrate that our proposed clustering enables us to identify and track point mutations that allow variants to become dominant and identify temporal correlations among such mutations. We identify indicators which point out dangerous variants, with a potential to grow to large probabilities. We show that we can accurately predict the temporal dynamics of such variants by using a universal model discussed in previous work.

Keywords: Information theory; Pathogen evolution; Clustering; Epidemiology; Dynamics; Complex systems (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437125002997
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:672:y:2025:i:c:s0378437125002997

DOI: 10.1016/j.physa.2025.130647

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().