Cost-Effective Control of Infectious Disease Outbreaks Accounting for Societal Reaction

Abstract

Background: Studies of cost-effective disease prevention have typically focused on the tradeoff between the cost of disease transmission and the cost of applying control measures. We present a novel approach that also accounts for the cost of social disruptions resulting from the spread of disease. These disruptions, which we call social response, can include heightened anxiety, strain on healthcare infrastructure, economic losses, or violence.

Methodology: The spread of disease and social response are simulated under several different intervention strategies. The modeled social response depends upon the perceived risk of the disease, the extent of disease spread, and the media involvement. Using Monte Carlo simulation, we estimate the total number of infections and total social response for each strategy. We then identify the strategy that minimizes the expected total cost of the disease, which includes the cost of the disease itself, the cost of control measures, and the cost of social response.

Conclusions: The model-based simulations suggest that the least-cost disease control strategy depends upon the perceived risk of the disease, as well as media intervention. The most cost-effective solution for diseases with low perceived risk was to implement moderate control measures. For diseases with higher perceived severity, such as SARS or Ebola, the most cost-effective strategy shifted toward intervening earlier in the outbreak, with greater resources. When intervention elicited increased media involvement, it remained important to control high severity diseases quickly. For moderate severity diseases, however, it became most cost-effective to implement no intervention and allow the disease to run its course. Our simulation results imply that, when diseases are perceived as severe, the costs of social response have a significant influence on selecting the most cost-effective strategy.

Fig 1. Overview of model dynamics.

(A) Each day, infection spreads on the disease network, which sends a social response signal to the social response network. Social response is then transmitted on the social response network via social connections and a media signal. (B) Social response is primarily affected by the perceived risk of the disease. Outbreaks with the same number of cases produce different social responses depending upon whether the disease in question is perceived as low risk (such as seasonal influenza) or high risk (such as SARS).

Fig 2. Most cost-effective intervention level and timing with no additional social response resulting from intervention.

The least cost probability of edge removal and the associated expected cost are shown for three intervention thresholds (τ = 10 cases, 1000 cases, 5000 cases) and for four values of the cost coefficient of social response (c _S). The cost coefficient of intervention (c _I) was 0.05c _D. The most cost-effective intervention is marked with a star (*) for each value of c _S. In cases where the pairwise difference in expected cost between strategies was not statistically significant with a permutation test, all least-cost strategies are marked with dots (⋅). The error bars on the expected cost indicate the bootstrapped empirical 95% confidence interval. When no cost was assigned to the social response, the most cost-effective solution was to intervene at a low level as the outbreak approached its peak—here, after 1000 cases. As c _S increased, the most cost-effective strategy shifted toward intervening at higher levels, earlier in the outbreak. This effect was especially pronounced as the perceived risk of the disease (κ) increased.

Fig 3. Cost of 80%, 90%, and 100% edge removal early in the outbreak.

The expected costs are shown for a disease with high perceived risk (κ = 1.0) and interventions with 80%, 90%, and 100% edge removal all beginning after 10 cases of the disease. The cost coefficient for intervention (c _I) was 0.05c _D. The most cost-effective intervention for each value of c _S is marked with a star (*). In cases where the pairwise difference in expected cost between strategies was not statistically significant with a permutation test, all least-cost strategies are marked with dots (⋅). For c _S ≤ 0.2c _D, 90% edge removal was significantly less costly than either 80% or 100% edge removal. For c _S ≥ 0.4c _D, 100% edge removal was the most cost-effective intervention level. Nevertheless, 90% edge removal was still less costly than intervention later in the outbreak.

Fig 4. Cost of media attention resulting from the initiation of an intervention to control the disease.

The expected cost is shown for an intervention initiated at 1000 cases with 40% edge removal. The cost coefficient for intervention (c _I) was 0.05c _D. The cost increased with the perceived risk of disease and with the social response cost coefficient (c _S). When the perceived risk of the disease was low (κ = 0.50), the media attention resulting from the intervention did not increase the overall cost. When the perceived risk was higher, the cost was substantially increased by the media attention.

Fig 5. Most cost-effective intervention level and timing when intervention leads to a surge in media attention and social response.

The least cost probability of edge removal and the associated expected cost are shown for three intervention thresholds (τ = 10 cases, 1000 cases, 5000 cases) and for four values of the cost coefficient of social response (c _S). The cost coefficient for intervention (c _I) was 0.05c _D. The most cost-effective intervention is marked with a star (*) for each value of c _S. In cases where the pairwise difference in expected cost between strategies was not statistically significant with a permutation test, all least-cost strategies are marked with dots (⋅). The error bars on the expected cost indicate the bootstrapped empirical 95% confidence interval. The most cost-effective intervention strategy was unchanged when the disease has low perceived risk (κ = 0.50). For diseases with moderate or high perceived risk, the least-cost strategy shifted to implementing no intervention, for most values of the cost coefficient of social response that we examined. For c _S = 0.6c _D, it remained optimal to intervene following only 10 cases with 100% edge removal.