Article

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

School of Computer Science and Software Engineering, University of Western Australia, Perth, WA, Australia.
BMC Public Health (Impact Factor: 2.32). 05/2009; 9:117. DOI: 10.1186/1471-2458-9-117
Source: PubMed

ABSTRACT 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.
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.
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.
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.

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    • "The removal of within-school edges would therefore be expected to reduce the rate at which the epidemic will spread (and therefore the peak number), but not the total number of infected individuals. These results are consistent with other studies, in which school closure has been shown to reduce the peak number of influenza infections to a greater extent than the total [7] [12] [17]. They are also consistent with the observation that school closure is less effective with higher R 0 epidemics [15] "
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    ABSTRACT: Simulations often involve the use of model parameters which are unknown or uncertain. For this reason, simulation experiments are often repeated for multiple combinations of parameter values, often iterating through parameter values lying on a fixed grid. However, the use of a discrete grid places limits on the dimension of the parameter space and creates the potential to miss important parameter combinations which fall in the gaps between grid points. Here we draw parallels with strategies for numerical integration and describe a Markov chain Monte-Carlo strategy for exploring parameter values. We illustrate the approach using examples from phylogenetics, archaeology, and epidemiology.
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    • "The existing models on pandemic influenza (PI) containment and mitigation aims to address various complex aspects of the pandemic evolution process including: (i) the mechanism of disease progression, from the initial contact and infection transmission to the asymptomatic phase, manifestation of symptoms, and the final health outcome [10] [11] [12], (ii) the population dynamics, including individual susceptibility [13] [14] and transmissibility [10, 15–17], and behavioral factors affecting infection generation and effectiveness of interventions [18] [19] [20], (iii) the impact of pharmaceutical and nonpharmaceutical measures, including vaccination [21] [22] [23], antiviral therapy [24] [25] [26], social distancing [27] [28] [29] [30] [31] and travel restrictions, and the use of low-cost measures, such as face masks and hand washing [26, 32–34]. "
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    06/2011; 2011:579597. DOI:10.1155/2011/579597
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    • "For example, Longini, et al use stochastic epidemic simulations to investigate the effectiveness of targeted antiviral prophylaxis to contain influenza [24]. Kelso, et al simulate the effect of social isolation, such as school closure, individual isolation, workplace nonattendance , and reduction of contact [22]. Carrat, et al explore the impact of interventions, such as vaccination, treatment, quarantine, and closure of schools and workplaces [5]. "
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