Simulation of strategies for containing pandemic influenza

Abstract

We use a stochastic simulation model of pandemic influenza to investigate realistic intervention strategies that can be used in reaction to developing outbreaks. The model is constructed to represent a typical midsized North American city. Our model predicts average illness attack rates and economic costs for various intervention scenarios, e.g., in the case when low-coverage reactive vaccination and limited antiviral use are combined with minimally disruptive social distancing strategies, including short-term closure of individual schools. We find that such combination strategies can be substantially more effective than vaccination alone from epidemiological and economic standpoints. INTRODUCTIONWith modern advances in science and technology, simulation has been widely used in many research fields. Epidemic simulation models provide useful tools to increase our understanding of the dynamics and patterns of disease propagation, and to study and evaluate the potential impacts of various government policies and intervention strategies for pandemic diseases, including prophylactic use of antivirals and social distancing strategies such as school closure, quarantine, and isolation. These tools are especially timely in light of the recent H1N1 swine flu pandemic. Similar to other simulation studies, epidemic simulations consist of three parts: identification of relevant inputs, model building, and output data analysis. The inputs typically include basic population data, the contact behavior of the individuals in the population, and disease transmission parameters. In the U.S., the population and their behavior are generated based on census bureau and demographic research, respectively, and the transmission parameters are based on epidemiologic research. Regarding model logic, the most-popular approaches are individual-based epidemiological compartment models, for example, 2221 978-1-4244-9865-9/10/$26.00 ©2010 IEEE

Simulating influenza epidemics with waning vaccine immunity

“…For example, while the differential equations are popular in simulating the course of influenza transmission, they tend to focus more on modeling the behavior of the groups as a whole. To focus on modeling the probability of individuals being infected given their demographic or community information, agent-based models are capable of considering the stochastic nature of the transmission rate between individuals [11,24] . For more discussions on the differential equations and the agent-based simulation models, see Paleshi et al [12] Our parsimonious mathematical models and differential equations are meant for capturing the main patterns that are evident in the empirical data for the following numerical study.…”

“…In this study, we assume that, at any given time, each individual in the population (N) belongs to one (and only one) of the following groups: susceptible (S), vaccinated (V), infectious (I), or recovered (R). [11,[21][22][23][24] In particular, S includes those who are healthy but exposed to the risk of contracting influenza from any infectious individuals; V refers to the proportion of individuals in N who are being vaccinated but who can also continuously leak to group S; I denotes those who are infectious agents of influenza to those in S; and R denotes those who have contracted influenza, recovered, and are therefore immune for the remainder of the season. Figure 1 exhibits a flowchart that conceptually illustrates to which group the individuals in a population could belong during an influenza epidemic.…”

“…Methodologically, we employ simulation, which has been one common approach for understanding the course of an influenza epidemic both in practice and in the literature. [ 11 , 12 ] Goeyvaerts et al [ 13 ] developed a seasonal influenza transmission model for evaluating the impact of vaccination on the incidence of infection, disease, and mortality. The authors proposed to directly estimate the values of the seasonal influenza model parameters by fitting the model to influenza data over multiple influenza seasons.…”

“…In this study, we aim to update classic epidemiological models by explicitly accounting for waning vaccine immunity over time, as well as to examine the effects that various parameters have on the size of the influenza epidemic. Methodologically, we employ simulation, which has been one common approach for understanding the course of an influenza epidemic both in practice and in the literature [11,12] . Goeyvaerts et al [13] developed a seasonal influenza transmission model for evaluating the impact of vaccination on the incidence of infection, disease, and mortality.…”