Strategies for mitigating an influenza pandemic

Abstract

Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. Here we use a large-scale epidemic simulation to examine intervention options should initial containment of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2-3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40-50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.

Figure 1. Baseline pandemic dynamics.

a, Clinical case incidence per day for the US pandemic (single realization shown) for high (red) and moderate (blue) transmissibility scenarios, assuming a generation time of 2.6 days, and that 50% of infected people are ill enough to be classified as clinical cases. Infection is seeded in the country as a function of the expected importation of infection from overseas derived from a simple global model of pandemic spread and available travel data (see Supplementary Information). Assumed population size of the United States was 300 million. Timing is shown both as days from the first case globally, and as days from the first case in the country. b, As a, but for Great Britain (modelled population size 58.1 million). c, Cumulative (red) and peak daily (blue) clinical attack rates as a function of R₀ for Great Britain, averaged over 40 model realizations. Error bars show standard deviations. d, Histogram showing stochastic variability (across 40 model realizations) in timing of initial case (red), peak of epidemic (blue) and peak attack rate (green) for Great Britain (R₀ = 2.0). e, Snapshots of the extent of spread of the US pandemic (moderate transmissibility) at four time points. Greyscale indicates population density; red indicates areas with infective cases; and green indicates areas where the pandemic is over. See Supplementary Information for full parameter and model details.

Figure 2. Impact of travel restrictions and case-targeted policies.

a, Delay caused by 90%, 99% and 99.9% reduction of imports of infection from day 30 of the global pandemic onwards on peak timing for GB and US pandemics for high (HT) and moderate (MT) transmissibility scenarios. Mean ± s.d. of 5–20 realizations is shown in both a and b. b, Delay in the peak of the US pandemic caused by internal travel restrictions for high (red) and moderate (blue) transmissibility. All policies are assumed to start after 50 cases have been diagnosed in the country. AC, all airports in the United States are closed to internal traffic; BC, border controls (external imports are reduced by 99.9%); BMR, blanket movement restrictions (journeys over 20 or 50 km from the home are eliminated); RMR, reactive movement restrictions (a 20-km exclusion zone is established around every diagnosed case, with overlapping zones being merged, and movement in and out of the exclusion zone is eliminated). c, Cumulative clinical attack rates for same day antiviral treatment policy, shown as a function of R₀. From highest to lowest, the different curves represent 0%, 30%, 50%, 70% and 90% of cases treated. Results for Great Britain are shown (US results are identical). d, As c, but showing dependence on delay (in days) between symptom onset and treatment when 90% of cases are treated (curves, from highest to lowest, are for no treatment, 2 day, 1 day and 0 day delay). e, As c, but showing effect of same day case isolation causing a 90% reduction in contacts (from highest to lowest, curves are for 0%, 50%, 70% and 90% of cases isolated).

Figure 3. Impact of household/socially targeted policies.

Results shown for high (HT) and moderate (MT) transmissibility scenarios in the United States. a–c, Cumulative clinical attack rate (a), peak attack rate (b) and delay in peak achieved by policy (c) (as a percentage of total population size). Values in the absence of interventions are shown in grey. Three household policies are shown: red, treating 90% of clinical cases and applying prophylaxis to their households the day after symptoms start; blue, voluntary quarantine of households identified with a clinical case in the home for 14 days—50% of households are assumed to comply with the policy, and in these, external contact rates are reduced by 75% and within-household contact rates assumed to increase by 100%; green, combination of the previous two (red and blue) policies. d–f, As a–c, but for three school/workplace-targeted policies: red, reactive school closure (that is, closing 100% of schools (and 10% of workplaces) from the day after the first case in pupils or staff is detected until up to 3 weeks after the last case in the school)—contact rates in affected households are assumed to increase by 50% and community contact rates in absent staff/pupils by 25%; blue, as above (red) but assuming 50% of workplaces close; green, as household prophylaxis policy shown in a–c but adding prophylaxis of 90% of members of the same school class or work group as treated cases. Error bars show standard deviation of between 5 and 20 realizations.

Figure 4. Impact of vaccination and combination strategies.

Results for the United States are shown. a, b, As Fig. 3a, b, but for a policy of mass vaccination assuming 1% of the population can be vaccinated per day from day 30 (red, second bar down), 60 (blue), 90 (green) after the first world-wide case, with 0–16 yr olds vaccinated first. Results for random (non-age prioritized) vaccination are also shown (orange), and for a policy of prioritizing those over 60 yr old (purple), both policies assuming that 1% of the population is vaccinated per day from day 60. Values in the absence of interventions are shown in grey. There is almost no effect of vaccination if started after 120 days. For comparison, the first US case is seen on day 47, and an untreated high transmissibility US pandemic peaks on day 113, on average. c, Example epidemic curves (for US high transmissibility scenario) for a selection of combination policies from Supplementary Table SI1. Policies shown are: number 4 (50% household quarantine plus reactive school closure); 12 (as number 4 plus 50% next-day case treatment); 13 (90% case treatment plus reactive school closure); 19 (as number 13 plus household prophylaxis); 21 (as number 19 plus pre-vaccination of 20% of the population, prioritizing children); and 27 (as number 19 plus prophylaxis of 80% of school classmates and close work colleagues plus 99% effective border controls).