Modern Statistical Modeling Techniques in Life Sciences

Vanja Dukic ()
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Vanja Dukic: Department of Economics (courtesy), University of Colorado Boulder, Department of Applied Mathematics

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1575-1586 from Springer

Abstract: Abstract Modern life science research, particularly in ecology and epidemiology, increasingly relies on sophisticated statistical models that extend far beyond traditional linear and generalized linear models (GLMs). While GLMs have been utilized for decades, their phenomenological nature often falls short of addressing the mechanistic complexity inherent in biological systems. Such mechanisms are often best described by systems of differential equations that can capture dynamic processes, feedback loops, spatial heterogeneity, and nonlinear interactions. Such features are fundamental to understanding many biological processes including population dynamics, disease transmission, and ecosystem functioning. Statistical computing, parameter estimation, and inference are all, however, far from trivial in such models. This chapter provides an overview of complex modeling approaches in life sciences, with particular emphasis on differential equation-based models in ecology and epidemiology, with an emphasis on computational challenges associated with parameter estimation and statistical inference in these systems.

Keywords: Inference; Weak form; Dynamical systems; MCMC; Computational statistics; Differential equations (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16368-4_77

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DOI: 10.1007/978-3-032-16368-4_77

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