Bacteria whose colonization or infection course may be affected by interaction with influenza.

<p>Bacteria whose colonization or infection course may be affected by interaction with influenza.</p

Illustration of a simple model of two circulating pathogens in interactions.

<p>Schematic of the compartments and rates of transition between compartments, with equations of the forces of infection by pathogen 1 (λ₁), pathogen 2 (λ₂) for susceptible hosts, and pathogen 1 (λ₂₁) and pathogen 2 (λ₁₂) for hosts already infected by the other pathogen. The full system of ordinary differential equations describing the changes of the compartment’s populations over time is described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006770#ppat.1006770.s003" target="_blank">S1 Appendix</a>, section B. Details of the model and parameters are provided in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006770#ppat.1006770.box003" target="_blank">Box 3</a>.</p

Influenza interactions with other pathogens occur within host or at the population level.

<p>Each interaction could either inhibit or enhance coinfection, depending on the combination of pathogens. (A) Cellular-level interactions: (1) direct interactions between viral products; (2) altered receptor presentation; (3) cell damage, e.g., its surface receptors; (4) modification of release of immune system mediators; (5) competition for host resources among influenza and other pathogens. (B) Host-level interactions: (1) change of transmissibility due to symptoms; (2) individual variation in commensal microbiota; (3) effect of symptomatic responses to infection; (4) tissue damage, e.g., in the nasopharynx or lung; (5) competition for host resources, e.g., target cells for infection; (6) immune cell–mediated interaction; (7) immune signalling–mediated interaction; (8) antibody-mediated interaction. (C) Population-level interaction: (1) behavioural responses to disease; (2) medication use; (3) vaccination behaviour. Bacterial interaction mechanisms include A1–5, B1–4 and 7, C1–3. Viral interaction mechanisms include A1–2 and 4–5, B1–3 and 4–8, C1–3.</p

Example model outputs showing effect of synergistic and competitive interaction.

<p><a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006770#ppat.1006770.box003" target="_blank">Box 3</a> gives details on the model that produces these epidemic trajectories. (A) In the baseline enhancing scenario, an endemic bacterial pathogen (blue) occurs at 5% prevalence. An influenza epidemic occurs with no interaction, and the bacterial prevalence does not change. If the presence of influenza coinfection increases bacterial transmissibility by 4-fold (<i>σ</i>_<i>1</i> = 4), then there is a transient rise in bacterial prevalence. If there is also an increase in influenza transmissibility during coinfection (<i>σ</i>_<i>1</i> = 4 and <i>σ</i>_<i>2</i> = 2), then there is also a higher and earlier influenza peak as a result of coinfection. (B) In the baseline competition scenario, the second epidemic pathogen is introduced later than influenza. The two pathogens have the same transmission characteristics (same <i>γ</i>, same β). If there is only a 50% chance of infection with pathogen 2 when individuals are infected with pathogen 1 (<i>δ</i>_<i>1</i> = 0.5), then the epidemic trajectory of pathogen 2 is lower and later. If competition is even stronger (<i>δ</i>_<i>1</i> = 0.1) so there is a 90% reduction in chance of coinfection, the profile of pathogen 2 is even further separated from pathogen 1. Computer code generating these trajectories is given in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006770#ppat.1006770.s004" target="_blank">S1 Code</a>.</p

Viruses that may be affected by interaction with influenza.

<p>Viruses that may be affected by interaction with influenza.</p

Cycle of factors affected by nonneutral interactions at the individual level and their impact on influenza surveillance, treatment, prevention, and control.

<p>Factors that affect coinfection on an individual scale can feed forward to an effect on population surveillance through their effects on the reporting of infection. Decisions on public health interventions are made in response to population-level data. These interventions then take effect at the individual level, to give a feedback loop both generated and impacted by effects of coinfection.</p

Workflow for the fluEvidenceSynthesis package.

<p>Workflow for the fluEvidenceSynthesis package.</p

Additional file 1: of Cost-effectiveness analysis of quadrivalent seasonal influenza vaccines in England

Assessing the impact of different paediatric programmes, sensitivity analyses for programmes 2 and 3, and cost-effectiveness acceptability curves. (DOCX 44 kb

Reduction in influenza incidence under increased vaccination of the age group between 5 and 15.

<p>The orange colour signifies the reduction in patients classified as high risk. The blue colour is the reduction in the low risk population. The first results correspond to a scenario where 40 percent coverage is achieved, the second to a coverage of 80.</p

Posterior and prior probability of R0 for different seasons and serotype H3N2.

<p>Posterior and prior probability of R0 for different seasons and serotype H3N2.</p