Simulation suggests that rapid activation of social distancing can arrest epidemic development due to a novel strain of influenza

Abstract

Background: Social distancing interventions such as school closure and prohibition of public gatherings are present in pandemic influenza preparedness plans. Predicting the effectiveness of intervention strategies in a pandemic is difficult. In the absence of other evidence, computer simulation can be used to help policy makers plan for a potential future influenza pandemic. We conducted simulations of a small community to determine the magnitude and timing of activation that would be necessary for social distancing interventions to arrest a future pandemic.

Methods: We used a detailed, individual-based model of a real community with a population of approximately 30,000. We simulated the effect of four social distancing interventions: school closure, increased isolation of symptomatic individuals in their household, workplace nonattendance, and reduction of contact in the wider community. We simulated each of the intervention measures in isolation and in several combinations; and examined the effect of delays in the activation of interventions on the final and daily attack rates.

Results: For an epidemic with an R0 value of 1.5, a combination of all four social distancing measures could reduce the final attack rate from 33% to below 10% if introduced within 6 weeks from the introduction of the first case. In contrast, for an R0 of 2.5 these measures must be introduced within 2 weeks of the first case to achieve a similar reduction; delays of 2, 3 and 4 weeks resulted in final attack rates of 7%, 21% and 45% respectively. For an R0 of 3.5 the combination of all four measures could reduce the final attack rate from 73% to 16%, but only if introduced without delay; delays of 1, 2 or 3 weeks resulted in final attack rates of 19%, 35% or 63% respectively. For the higher R0 values no single measure has a significant impact on attack rates.

Conclusion: Our results suggest a critical role of social distancing in the potential control of a future pandemic and indicate that such interventions are capable of arresting influenza epidemic development, but only if they are used in combination, activated without delay and maintained for a relatively long period.

Figure 1

Relationship between intervention activation delay and final illness attack rates. Results for five different intervention strategies are shown for R₀ = 1.5 (blue); four strategies are shown for R₀ = 2.5 (orange); two strategies are shown for R₀ = 3.5. All data points are averages of 40 randomly seeded simulation runs; standard deviation each 40-run set was < 1.4% of the population.

Figure 2

Relationship between intervention activation delay and peak daily illness attack rates for various intervention strategies. Results for R₀ = 1.5 (blue), R₀ = 2.5 (orange), and R₀ = 3.5 (red) are shown.

Figure 3

Epidemic curves for school closure for a range of activation delays. Cumulative (top) and peak daily (bottom) attack rates are shown for epidemics with unmitigated R₀ values of 1.5 (left) and 2.5 (right).

Figure 4

Age-specific attack rates for social distancing interventions. Final attack rates are shown for each of 7 age groups for a baseline (unmitigated) epidemic, and for epidemics mitigated by 4 intervention measures. An R₀ value of 1.5 is assumed; interventions are assumed to be applied pre-emptively.

Figure 5

Age structure of simulated population. The percentage of the total simulated population (29350) of each of the 7 age groups is shown.