1. IntroductionIt is necessary to evaluate a nation’s pandemic influenza preparedness plan for whether it effectively reflects the capacity of the public health system on a national basis. Several papers have reported that an influenza pandemic can stretch the capacity of a nation’s health system [1-3]. Some studies use static models without parameter sensitivity analysis but consider a few fixed values for attack rates, hospitalization rates, and mortality rates [4-10], and a few consider the therapeutic use of neuraminidase inhibitors [5,8,10]. As pharmaceutical and nonpharmaceutical interventions can change the course of a pandemic, any sensitivity analysis must include whether these can lower the burden on the national public health system to a manageable level.However, this evaluation is difficult because we do not know the contagiousness of any upcoming unknown influenza strain. Here, we defined feasible ranges for the parameters of a future influenza pandemic, and then randomly sampled from these ranges. For each combination of sampled parameter values, we simulated the course of the pandemic wave using InfluSim software (http://www.influsim.info) [11,12]. We thereby generated a whole range of plausible influenza pandemics for which we could evaluate how many persons would seek medical help or need hospitalization. Simulations were conducted both with and without interventions, and the effects of the intervention were then estimated for each set of parameter values.
2. Materials and MethodsWe conducted the sensitivity analysis using InfluSim version 2.1, a deterministic compartment model that extends the ‘susceptible–exposed-infectious–removed’ (SEIR) model by using clinical and demographic parameters relevant for pandemic preparedness planning [11,12]. The simulation produces daily time courses and cumulative numbers of influenza cases, outpatients, and
3. ResultsThis parameter sensitivity analysis has revealed some interesting properties of an influenza pandemic in Korea. As expected, the basic reproduction number and distribution of contagiousness over the infectious period have the largest effect on the course of the epidemic. Other parameters describing the contagiousness of cases in the late prodromal period and the contagiousness of asymptomatic and moderate cases compared to severe cases have only a moderate effect on the course of the epidemic. Interestingly, an increased contagiousness of moderately sick individuals reduces the peak and cumulative number of cases.The antiviral stockpile of 4–6% is sufficient for the expected eligible number of cases to be treated. However, the eligible number assumed (30% for severe cases and 26% for extremely severe cases) is very low compared to the equivalent figure in European countries, where up to 90% of the population are assumed to be eligible for antiviral treatment . The two parameters describing the effect of antiviral treatment, i.e., the reduction in the duration of the contagious period and the reduction in the contagiousness of treated individuals,have only a minor effect on the simulation results.The general reduction of contacts has a large effect on the course of the epidemic, while the threshold for closing schools has only a minor effect. This can be explained by the small overall effect of school closure even though Korea has a larger fraction of school children than most European countries. However, the school closure threshold has been investigated for very small threshold values. The use of a deterministic simulator such as InfluSim may not be adequate to address the analysis of optimal school closure thresholds.Comparing the effect of social distancing interventions with antiviral treatment, social distancing interventions clearly have a larger effect. This can be explained by the small fraction of individuals eligible for treatment (26–30%). However, additional prophylaxis for healthcare workers and essential service workers will rapidly exploit the antiviral stockpile, so that treatment of cases will no longer be possible and the overall effect will be negative.
4. DiscussionIt is important to consider ranges of parameter values.Sampling random values from reasonable intervals translates input uncertainty into expected output variability. The wide regions of tolerance for the total number of outpatients and hospitalizations (Figure 1) show that pandemic preparedness plans should consider “best case” and “worst case” scenarios, not “average case” scenarios.The most important parameter that determines both the duration and the height of a pandemic is the basic reproduction number, R0. However, there is a wide range of proposed values for past pandemics and for seasonal influenza, ranging from 1.5 to 4 [17-21]. Many authors have adopted Longini’s containment strategies for R0, using a value of 1.1-2.4 . Ferguson et al’s R0 for 1918 pandemic data should be regarded as an effective reproduction number that also reflects the effect of interventions, and they proposed R0 = 1.7 as “moderate” and R0 = 2.0 as “high” transmission scenarios . We explored a wider range of pandemics (R0 = 1.5-3.5) and also considered hospital bed occupancy and intensive care unit (ICU) demand.The effects of antiviral treatment depend on the patients’ treatment time and on where they have already spent most of the contagious period before treatment. The success of social distancing measures depends on the compliance of the population. At the most pessimistic end of our simulations (high R0 and a strong concentration of contagiousness in the early phase of the infection, combined with low public health compliance and low treatment effects), the number of hospitalizations can be 1.9 times higher than the mean, whereas at the most optimistic end, a major outbreak may be prevented (cf. the 99% interval for the combined intervention in Figure 2C).Our study confirms the results of previous studies using static models [5,8] that have pointed out ICU capacity as a bottleneck in hospital settings, and have stated that appropriate contingency planning must consider a rapid expansion of ICU capacity. We show that, in pessimistic cases, a non-negligible percentage of hospitalized patients (ranging from 5.5% to 39.5%) would be at a higher risk of death if 50% of the currently existing ICU beds could be made available at the peak of the epidemic. We believe that, as ICU capacity is difficult to expand and costly to maintain, additional measures must be considered and extensive preparation will be needed. This includes occupational safety measures and the development of triage policies (Figures 3 and 4).