Human influenza is characterized by seasonal epidemics, caused by rapid viral adaptation to population immunity. Vaccination against influenza must be updated annually, following surveillance of newly appearing viral strains. During an influenza season, several strains may be co-circulating, which will influence their individual evolution; furthermore, selective forces acting on the strains will be mediated by the transmission dynamics in the population.
Murray E. Alexander, of the National Research Council Canada's Institute for Biodiagnostics, and Randy Kobes, from the Department of Physics at the University of Winnipeg, emphasize that viral evolution and public health policy are strongly interconnected, and that understanding population-level dynamics of coexisting viral influenza infections would be of great benefit in designing vaccination strategies.
Alexander and Kobes used a Markov network to extend a previous homogeneous model of two co-circulating influenza viral strains by including vaccination (either prior to or during an outbreak), age structure, and heterogeneity of the contact network. They explored the effects of changes in vaccination rate, cross-immunity, and delay in appearance of the second strain, on the size and timing of infection peaks, attack rates, and disease-induced mortality rate; and compare the outcomes of the network and corresponding homogeneous models.
The researchers report that pre-vaccination is more effective than vaccination during an outbreak, resulting in lower attack rates for the first strain but higher attack rates for the second strain, until a "threshold" vaccination level of about 30 percent to 40 percent is reached, after which attack rates due to both strains sharply dropped. A small increase in mortality was found for increasing pre-vaccination coverage below about 40 percent, due to increasing numbers of strain 2 infections. The amount of cross-immunity present determines whether a second wave of infection will occur. Some significant differences were found between the homogeneous and network models, including timing and height of peak infection(s).
Alexander and Kobes conclude that contact and age structure significantly influence the propagation of disease in the population. They add that the present model explores only qualitative behavior, based on parameters derived for homogeneous influenza models, but may be used for realistic populations through statistical estimates of inter-age contact patterns. This could have significant implications for vaccination strategies in realistic models of populations in which more than one strain is circulating. Their research was published in BMC Public Health.
Reference: Alexander ME and Kobes R. Effects of vaccination and population structure on influenza epidemic spread in the presence of two circulating strains. BMC Public Health 2011, 11(Suppl 1):S8doi:10.1186/1471-2458-11-S1-S8.