Python Code for Qualitative Exposuremetrics Analysis

Qualitative exposuremetrics is a measure of the biological proximity or deviation of an organism from the observed progressive threat or anti-threat responses to the expected cumulative threat or anti-threat responses (i.e., the residual, net, or gross expectation) as a result of an exposure to a harmful agent(s) [1]. It estimates the organismal threat or anti-threat response dynamics of resistance and susceptibility within a population over successful events and generations. The qualitative exposuremetrics methodology was limited to qualitative variables that define the threat or anti-threat responses, such as the frequency of mortality or survival, and the presence or absence of threat-induced biological responses [1].

In this context, organismal resistance is defined as the isoreflectivity (isoreflective pairing) of observed anti-threat responses to the expected cumulative anti-threat responses (i.e., the residual, net, or gross expectation). The organismal susceptibility is the deviation from attaining a complete and total susceptibility status [1].

The process of qualitative exposuremetrics comprises four distinct phases:
a) Preprocessing (conceptualization, parameterization, adaptive customization and optimization) phase: This phase forms the core of the methodology, defining key concepts and expectations derived from observed variables to guide the design of the statistical mirror. It also involves customizing and optimizing parameters to ensure the statistical mirroring process is well-suited to the specific analytical task [1].
b) Statistical mirroring analysis phase [2]: This involves applying a suitable statistical mirroring type based on the phase 1 adaption of the established adaptive customization and optimization of statistical mirroring parameters.
c) Kabirian-based optinalysis model calculation phase [3]: This phase is focused on computing estimates (such as the Kabirian coefficient of proximity, the probability of proximity, and the deviation) based on Kabirian-based isomorphic optinalysis models.
d) Advanced Exposuremetrics Calculations phase: These advanced metrics are the simple arithmetic differences between qualitative exposuremetrics estimates [1].

References:
[1] Abdullahi, K. B.; Suleiman, M.; Wagini, N. H.; Sani, I. (2025). Qualitative exposuremetrics: A comprehensive and sensitive estimation framework for analyzing organismal resistance and susceptibility dynamics using qualitative variables. [You can follow updates for the published citation details]

[2] Abdullahi, K. B. (2024). Statistical mirroring: A robust method for statistical dispersion estimation. MethodsX, 12, 102682. doi: 10.1016/j.mex.2024.102682

[3] Abdullahi, K. B. (2023). Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences. MethodsX, 11, 102400. doi: 10.1016/j.mex.2023.102400