Joint economic and epidemiological modelling of alternative pandemic response strategies

TL;DR

This work addresses how to choose pandemic response strategies under uncertainty by integrating health outcomes and economic costs into a single framework. It develops a SIR-based model with a time-varying activity level $a(t)$ and a cost function that includes infection costs $k$ and activity-reduction costs, comparing centralised and decentralised mitigation with suppression and elimination. The key findings show that mitigation tends to be most cost-effective when $k$ is low, while suppression is preferred at higher $k$ when $R_0$ is modest and elimination prevails at high $R_0$; NZ's Covid-19 2020 parameters anchor the analysis and illustrate how these trade-offs depend on epidemiological and policy factors. The framework provides a practical decision-support tool for future pandemic threats, while acknowledging simplifications such as homogeneous mixing and deterministic dynamics, and outlining future work to incorporate heterogeneity, healthcare capacity, and uncertainty in vaccine timelines.

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.

Figures (4)

• Figure 1: Comparison of the unmitigated (blue), decentralised mitigation (red), centralised mitigation (yellow), suppression (purple), and elimination (green) responses for $R_0=1.5$ and three values of the cost per infection $k$: (a,b) $k=$ $2000; (c,d) $k=$ $6000; (e,f) $k=$ $12000. Left-hand plots (a,c,e) show the time-dependent transmission rate $a(t)^2$ relative to the unmitigated case (solid curves) and the cumulative proportion of the population infected (dashed curves). Note that infections are assumed to be negligible for suppression and elimination. Right-hand plots (b,d,f) show the cumulative cost. Parameter values as in Table 1.
• Figure 2: Comparison of the unmitigated (blue), decentralised mitigation (red), centralised mitigation (yellow), suppression (purple), and elimination (green) responses for $R_0=3$ and three values of the cost per infection $k$: (a,b) $k=$ $2000; (c,d) $k=$ $6000; (e,f) $k=$ $12000. Left-hand plots (a,c,e) show the time-dependent transmission rate $a(t)^2$ relative to the unmitigated case (solid curves) and the cumulative proportion of the population infected (dashed curves). Note that infections are assumed to be negligible for suppression and elimination. Right-hand plots (b,d,f) show the cumulative cost. Parameter values as in Table 1.
• Figure 3: Aggregate cost of different response types at $T=600$ days for a range of values of $R_0$ and cost per infection $k$: (a) a decentralised mitigation response; (b) centralised mitigation response; (c) an elimination or suppression response (whichever has the lower cost). The vertical dashed white line in (c) separates suppression and elimination strategies: suppression is more cost-effective to the left of the dashed line; elimination is more cost-effective to the right of the dashed line. Panel (d) dhows which strategy has the lowest cost out of no PHSMs, mitigation, suppression and elimination. In the region labelled 'no PHSMs', the centralised mitigation response does not reduce activity levels $a(t)$ by more than $0.1$% below normal at any time.
• Figure 4: Threshold time for vaccine availability below which elimination or suppression becomes more cost-effective than mitigation: (a) for a range of values of $R_0$ and cost per infection $k$; (b) for a range of values of the border-related outbreak detection to return time ratio ($rt_\mathrm{det}$) and TTI effectiveness ($\alpha$) for $R_0=3$ and $k=$ $6000. Other parameter values as in Table \ref{['tab:params']}. Lighter colours indicate that it is more cost-effective to follow an elimination or suppression strategy even if vaccine availability is not expected for a longer time. Note that in the white part of panel (a) where $R_0=1.25$, suppression is always optimal because in the model this can be achieved with TTI measures alone and so does not require any population-level reduction in contact rates.