Cost-Effective Strategies for Infectious Diseases: A Multi-Objective Framework with an Interactive Dashboard

Abstract

During an infectious disease outbreak, policymakers must balance medical costs with social and economic burdens and seek interventions that minimize both. To support this decision-making process, we developed a framework that integrates multi-objective optimization, cost-benefit analysis, and an interactive dashboard. This platform enables users to input cost parameters and immediately obtain a cost-optimal intervention strategy. We applied this framework to the early outbreak of COVID-19 in South Korea. The results showed that cost-optimal solutions for costs per infection ranging from 4,410 USD to 361,000 USD exhibited a similar pattern. This indicates that once the cost per infection is specified, our approach generates the corresponding cost-optimal solution without additional calculations. Our framework supports decision-making by accounting for trade-offs between policy and infection costs. It delivers rapid optimization and cost-benefit analysis results, enabling timely and informed decision-making during the critical phases of a pandemic.

Figures (9)

• Figure 1: Schematic overview of the integrated decision-support framework for pandemic management. The framework consists of five sequential stages (orange diamonds) designed to suggest a tool for policy decision-making under a pandemic. 1) Mathematical Modeling for the target outbreak. 2) Parameter Estimation to estimate unknown parameters against observable data. 3) Multi-objective Optimization to derive the Pareto optimal solutions. 4) Economic evaluations to identify cost-minimizing intervention patterns among Pareto optimals. and 5) App Development resulting in a user-interactive dashboard for real-time strategy visualization under customized cost-related parameters.
• Figure 2: Mathematical model for infectious diseases. The squares represent compartments of the mathematical model, and the black arrows represent flows between the compartments. The black dashed arrow represents external importation, which serves as a trigger for an epidemic. The red dashed arrow represents the force of infection, which drives the spread of the disease in a country. The parameters in black and red are the disease-related and estimated parameters, respectively.
• Figure 3: Data-fitting results. (a) Cumulative confirmed cases and simulation results. (b) Estimated values of $\mu(t)$ over time. (c) Daily confirmed cases and corresponding implemented social distancing strategy.
• Figure 4: Pareto-optimal intervention strategies. (a) Objective space and Pareto frontier: The black solid line represents the Pareto-optimal frontier, illustrating the trade-off between the average effectiveness of transmission reduction ($f_1\left( \mu \left(t\right)\right)$) and the proportion of the population infected. The colored circles (S1–S5) denote five representative Pareto-optimal solutions selected based on specific cumulative infection rate targets: 10% (S1), 1% (S2), 0.1% (S3), 0.01% (S4), and 0.001% (S5). The red diamond (SE) indicates the estimated real-world strategy from South Korean data, which sits above the Pareto curve, reflecting the inherent discrepancy between empirical implementation and theoretical optimality. (b) Temporal transmission-reduction profiles: Time-series representation of the specific intervention patterns corresponding to the points in panel (a). The profiles show that achieving more stringent public health targets (moving from S1 to S5) requires initiating high-intensity interventions earlier in the outbreak. The red curve (SE) displays the historical transmission reduction estimated during the 26-week study period.
• Figure 5: Cost-benefit analysis results with Pareto solution. (a) Total costs along the Pareto curve under the cost-benefit analysis. The orange area, green area, and gray line represent the infection cost, transmission reduction-related intervention cost, and total cost, respectively. (b) Total costs along the Pareto curve for different values of cost per infection. The green points represent the cost-optimal solutions for varying infection costs. The gray line corresponds to panel (a). (c) Cost-optimal solutions for the cost per infection. The line parallel to the x-axis represents the cost-optimal solution for each infection cost and corresponds to the green points in the panel (b). (d) Cost-optimal policies for each cost per infection range.