Designing optimal allocations for cancer screening using queuing network models

Justin Dean, Evan Goldberg and Franziska Michor

PLOS Computational Biology, 2022, vol. 18, issue 5, 1-30

Abstract: Cancer is one of the leading causes of death, but mortality can be reduced by detecting tumors earlier so that treatment is initiated at a less aggressive stage. The tradeoff between costs associated with screening and its benefit makes the decision of whom to screen and when a challenge. To enable comparisons across screening strategies for any cancer type, we demonstrate a mathematical modeling platform based on the theory of queuing networks designed for quantifying the benefits of screening strategies. Our methodology can be used to design optimal screening protocols and to estimate their benefits for specific patient populations. Our method is amenable to exact analysis, thus circumventing the need for simulations, and is capable of exactly quantifying outcomes given variability in the age of diagnosis, rate of progression, and screening sensitivity and intervention outcomes. We demonstrate the power of this methodology by applying it to data from the Surveillance, Epidemiology and End Results (SEER) program. Our approach estimates the benefits that various novel screening programs would confer to different patient populations, thus enabling us to formulate an optimal screening allocation and quantify its potential effects for any cancer type and intervention.Author summary: We describe a mathematical modeling methodology that offers quantitative insights into the potential benefits of screening and other interventions on cancer mortality. Our queuing-theoretic approach represents a potentially useful alternative to more traditional modeling approaches, in that it can provide more detailed results and entirely circumvents the need for simulations. Our methodology can be widely applied to estimate costs and benefits of screening strategies. By providing a detailed example of our method applied to epidemiological data, we hope to encourage greater uptake of this methodology in the community.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010179

DOI: 10.1371/journal.pcbi.1010179

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