Joint economic and epidemiological modelling of alternative pandemic response strategies

TL;DR

This paper introduces a combined economic and epidemiological model to evaluate pandemic response strategies, providing insights into cost-effectiveness of mitigation, suppression, and elimination under various disease severities and parameters.

Contribution

It presents a novel integrated framework that simultaneously considers health and economic impacts of different pandemic control strategies, applied to real-world data from New Zealand's COVID-19 response.

Findings

Mitigation is most cost-effective for low-severity diseases.

Suppression is optimal for severe diseases with low R0.

Elimination becomes more cost-effective at high R0 values.

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.