Joint economic and epidemiological modelling of alternative pandemic response strategies

[2512.08355] Joint economic and epidemiological modelling of alternative pandemic response strategies Skip to main content Search Submit Donate Log in Quantitative Biology > Populations and Evolution arXiv:2512.08355 (q-bio) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 9 Dec 2025 ( v1 ), last revised 26 May 2026 (this version, v2)] Title: Joint economic and epidemiological modelling of alternative pandemic response strategies Authors: M J Plank , M Sushames , T Fisher-Taylor , A Afshari , R N Thompson , A Hurford , S C Hendy View PDF HTML (experimental) Abstract: In an emerging pandemic, policymakers need to make important decisions with limited information, for example choosing between a mitigation, suppression or elimination strategy. These strategies may require trade-offs to be made between the health impact of the pandemic and the economic costs of the interventions introduced in response. Mathematical models are a useful tool that can help understand the consequences of alternative policy options on the future dynamics and impact of the epidemic. Most models have focused on direct health impacts, neglecting the economic costs of control measures. Here, we introduce a model framework that captures both health and economic costs. We use this framework to compare the expected aggregate costs of mitigation, suppression and elimination strategies, across a range of different epidemiological and economic parameters. We find that for diseases with low severity, mitigation tends to be the most cost-effective option. For more severe diseases, suppression tends to be most cost effective if the basic reproduction number $R_0$ is relatively low, while elimination tends to be more cost-effective if $R_0$ is high. We use the example of New Zealand's elimination response to the Covid-19 pandemic in 2020 to anchor our framework to a real-world case study. We find that parameter estimates for Covid-19 in New Zealand put it close to or above the threshold at which elimination becomes more cost-effective than mitigation. We conclude that our proposed framework holds promise as a decision-support tool for future pandemic threats, although further work is needed to account for population heterogeneity and other factors relevant to decision-making. Subjects: Populations and Evolution (q-bio.PE) ; Physics and Society (physics.soc-ph) Cite as: arXiv:2512.08355 [q-bio.PE] (or arXiv:2512.08355v2 [q-bio.PE] for this version) https://doi.org/10.48550/arXiv.2512.08355 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of the Royal Society Interface (2026) 23: 20260098 Related DOI : https://doi.org/10.1098/rsif.2026.0098 Focus to learn more DOI(s) linking to related resources Submission history From: Michael Plank [ view email ] [v1] Tue, 9 Dec 2025 08:32:26 UTC (854 KB) [v2] Tue, 26 May 2026 01:59:16 UTC (855 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.PE < prev | next > new | recent | 2025-12 Change to browse by: physics physics.soc-ph q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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