How to minimize the attack rate during multiple influenza outbreaks in a heterogeneous population.
BACKGROUND
If repeated interventions against multiple outbreaks are not feasible, there is an optimal level of control during the first outbreak. Any control measures above that optimal level will lead to an outcome that may be as sub-optimal as that achieved by an intervention that is too weak. We studied this scenario in more detail.


METHODS
An age-stratified ordinary-differential-equation model was constructed to study infectious disease outbreaks and control in a population made up of two groups, adults and children. The model was parameterized using influenza as an example. This model was used to simulate two consecutive outbreaks of the same infectious disease, with an intervention applied only during the first outbreak, and to study how cumulative attack rates were influenced by population composition, strength of inter-group transmission, and different ways of triggering and implementing the interventions. We assumed that recovered individuals are fully immune and the intervention does not confer immunity.


CONCLUSIONS
The optimal intervention depended on coupling between the two population sub-groups, the length, strength and timing of the intervention, and the population composition. Population heterogeneity affected intervention strategies only for very low cross-transmission between groups. At more realistic values, coupling between the groups led to synchronization of outbreaks and therefore intervention strategies that were optimal in reducing the attack rates for each subgroup and the population overall coincided. For a sustained intervention of low efficacy, early intervention was found to be best, while at high efficacies, a delayed start was better. For short interventions, a delayed start was always advantageous, independent of the intervention efficacy. For most scenarios, starting the intervention after a certain cumulative proportion of children were infected seemed more robust in achieving close to optimal outcomes compared to a strategy that used a specified duration after an outbreak's beginning as the trigger.
Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, United States of America. ichfung@gmail.com
Isaac Chun-Hai
IC
Fung
Rustom
R
Antia
Andreas
A
Handel
D000328:Adult; D000367:Age Factors; D002648:Child; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control* / Q000635:transmission; D008962:Models, Theoretical; D013997:Time Factors