Data-Driven Approach to Analysis of SIR (Susceptible-Infected-Removed/ Recovered)-Type Models: The Principle of Parsimony Applied to Epidemics Modeling in the Age of COVID-19

Leonid Kalachev (), Erin L. Landguth () and Jonathan Graham ()
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Leonid Kalachev: University of Montana, Mathematical Sciences
Erin L. Landguth: University of Montana, Center for Population Health Research, School of Public and Community Health Sciences
Jonathan Graham: University of Montana, Mathematical Sciences and Center for Population Health Research, School of Public and Community Health Sciences

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1381-1418 from Springer

Abstract: Abstract As infectious disease models grow in complexity, so too do their number of parameters and heuristic assumptions. Often, the available data necessary to produce reliable estimation of the large number of required parameters are insufficient not only for accurate estimation but also for trustworthy model predictions. In this chapter, the data types that are typically collected and reported are introduced. How these data types can lead to issues with parameter estimation and model prediction is then discussed. Some problems with epidemiological data that researchers are confronted with include notorious underreporting of cases, misclassification in the form of mislabeling of data types, and misclassification in the form of misalignmentData misalignment of data types with model compartments using SIR-type models. A data-driven parsimonious modeling approach for minimizing issues arising from the abovementioned problems and for producing robust modeling results with minimal additional assumptions is presented.

Keywords: Epidemics modeling; SIR model; COVID-19; Flu; Reliable parameter estimation; Data-driven analysis; Data mislabeling; Data misalignment; Underreporting of infections; Susceptible; Infected; Quarantined; Recovered; Removed; Symptomatic and asymptomatic infections; Minimal immunization requirement (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-16368-4_1

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