Transmission dynamics of two-strain disease in the presence of cross-protective immunity.

<p>(a) Model without re-infection with an identical antigenic type (SIR-type; susceptible-infectious-recovered) and (b) model with re-infections by an identical antigenic type (SIS-type; susceptible-infected-susceptible). SIR model is intended to capture the epidemiological dynamics of EV71, while SIS model is applied to pneumococcus. [Compartments] Variable <i>u</i> represents unvaccinated. Two subscripts represent the state of infection (or carriage) with respect to VT and NVT, respectively. For example, <i>u</i>_si represents unvaccinated host who is susceptible to VT but is infected with NVT. [Parameters] <i>c</i>, vaccination coverage; <i>μ</i>, background birth and death rates of human host; <i>λ</i>_A and <i>λ</i>_B, the rates of infection with VT (vaccine type) and NVT (non-vaccine type), respectively; <i>γ</i>_A and <i>γ</i>_B, recovery rates from infection with VT and NVT, respectively; <i>σ</i>, the relative reduction of the risk of infection upon exposure by cross-protective immunity in the SIR model; <i>σ</i>_A and <i>σ</i>_B, the relative reduction of the risk of carriage acquisition upon exposure to VT and NVT by competition, respectively, in the SIS model. For simplicity, both panels represent the population dynamics of unvaccinated population alone. In case no vaccination takes place, <i>c</i> is equal to 0.</p

Parameter values for the SIS (Susceptible-Infected-Susceptible) as applied to the epidemiological dynamics of <i>Streptococcus pneumoniae.</i>

<p>Parameter values for the SIS (Susceptible-Infected-Susceptible) as applied to the epidemiological dynamics of <i>Streptococcus pneumoniae.</i></p

Data_Sheet_1_Understanding dynamics and overlapping epidemiologies of HIV, HSV-2, chlamydia, gonorrhea, and syphilis in sexual networks of men who have sex with men.docx

IntroductionWe aimed to investigate the overlapping epidemiologies of human immunodeficiency virus (HIV), herpes simplex virus type 2 (HSV-2), chlamydia, gonorrhea, and syphilis in sexual networks of men who have sex with men (MSM), and to explore to what extent the epidemiology of one sexually transmitted infection (STI) relates to or differs from that of another STI.MethodsAn individual-based Monte Carlo simulation model was employed to simulate the concurrent transmission of STIs within diverse sexual networks of MSM. The model simulated sexual partnering, birth, death, and STI transmission within each specific sexual network. The model parameters were chosen based on the current knowledge and understanding of the natural history, transmission, and epidemiology of each considered STI. Associations were measured using the Spearman’s rank correlation coefficient (SRCC) and maximal information coefficient (MIC).ResultsA total of 500 sexual networks were simulated by varying the mean and variance of the number of partners for both short-term and all partnerships, degree correlation, and clustering coefficient. HSV-2 had the highest current infection prevalence across the simulations, followed by HIV, chlamydia, syphilis, and gonorrhea. Threshold and saturation effects emerged in the relationship between STIs across the simulated networks, and all STIs demonstrated moderate to strong associations. The strongest current infection prevalence association was between HIV and gonorrhea, with an SRCC of 0.84 (95% CI: 0.80–0.87) and an MIC of 0.81 (95% CI: 0.74–0.88). The weakest association was between HSV-2 and syphilis, with an SRCC of 0.54 (95% CI: 0.48–0.59) and an MIC of 0.57 (95% CI, 0.49–0.65). Gonorrhea exhibited the strongest associations with the other STIs while syphilis had the weakest associations. Across the simulated networks, proportions of the population with zero, one, two, three, four, and five concurrent STI infections were 48.6, 37.7, 11.1, 2.4, 0.3, and ConclusionSTI epidemiologies demonstrate substantial overlap and associations, alongside nuanced differences that shape a unique pattern for each STI. Gonorrhea exhibits an “intermediate STI epidemiology,” reflected by the highest average correlation coefficient with other STIs.</p

How Is Vaccine Effectiveness Scaled by the Transmission Dynamics of Interacting Pathogen Strains with Cross-Protective Immunity?

<div><h3>Background</h3><p>Many novel vaccines can cover only a fraction of all antigenic types of a pathogen. Vaccine effectiveness (VE) in the presence of interactions between vaccine strains and others is complicated by the interacting transmission dynamics among all strains. The present study investigated how the VE estimates measured in the field, based on estimated odds ratio or relative risks, are scaled by vaccination coverage and the transmission dynamics in the presence of cross-protective immunity between two strains, i.e. vaccine and non-vaccine strains.</p> <h3>Methodology/Principal Findings</h3><p>Two different types of epidemiological models, i.e. with and without re-infection by the same antigenic type, were investigated. We computed the relative risk of infection and the odds ratio of vaccination, the latter of which has been measured by indirect cohort method as applied to vaccine effectiveness study of <em>Streptococcus pneumoniae</em>. The VE based on the relative risk was less sensitive to epidemiological dynamics such as cross-protective immunity and vaccination coverage than the VE calculated from the odds ratio, and this was especially the case for the model without re-infection. Vaccine-induced (cross-protective) immunity against a non-vaccine strain appeared to yield the highest impact on the VE estimate calculated from the odds ratio of vaccination.</p> <h3>Conclusion</h3><p>It is essential to understand the transmission dynamics of non-vaccine strains so that epidemiological methods can appropriately measure both the direct and indirect population impact of vaccination. For pathogens with interacting antigenic types, the most valid estimates of VE, that are unlikely to be biased by the transmission dynamics, may be obtained from longitudinal prospective studies that permit estimation of the VE based on the relative risk of infection among vaccinated compared to unvaccinated individuals.</p> </div

Vaccine effectiveness in the SIR (Susceptible-Infectious-Recovered) model.

<p>Field estimate (vertical axis) represents the vaccine effectiveness estimate derived from empirical observation in the field. Solid line represents vaccine effectiveness based on odds ratio, VE_O, while broken line represents that based on relative risk, VE_R. Assumed vaccine efficacy against VT (vaccine type) is shown at the right end of each line. Cross-protective immunity is expressed as perfect protection with a probability <i>σ</i> for an all-or-nothing type vaccine, and expressed as the relative reduction in the instantaneous risk of infection upon exposure against a serotype among those who have already experienced infection with the other serotype for a leaky type vaccine. (a)-(c) show the effectiveness of all-or-nothing vaccine (i.e. perfect protection given successful immunization and no protection for unsuccessful vaccination), whereas (d)-(f) show the effectiveness of leaky vaccine (i.e. imperfect protection for all vaccinated individuals).</p

The relationship between vaccine effectiveness against VT (vaccine type) and assortativity coefficient <i>θ</i>.

<p>Solid line represents vaccine effectiveness based on odds ratio, VE_O, while broken line represents that based on relative risk, VE_R. Vaccine efficacy against VT is shown at the right end of lines. (a) and (c) show the result from SIR model, while (b) and (d) are from SIS model for <i>Streptococcus pneumoniae</i>. (a) and (b) show the effectiveness of all-or-nothing vaccine (i.e. perfect protection given successful immunization and no protection for unsuccessful vaccination), whereas (c) and (d) show the effectiveness of leaky vaccine (i.e. imperfect protection for all vaccinated individuals).</p

Vaccine effectiveness in the SIS (Susceptible-Infected-Susceptible) model.

<p>Field estimate (vertical axis) represents the vaccine effectiveness estimate derived from empirical observation in the field. Solid line represents vaccine effectiveness based on odds ratio, VE_O, while broken line represents that based on relative risk, VE_R. Assumed vaccine efficacy against VT (vaccine type) is shown at the right end of each line. Vaccine-induced immunity was dealt with as in two different ways, (i) all-or-nothing type or (ii) leaky type. (a)-(d) show the effectiveness of all-or-nothing vaccine (i.e. perfect protection given successful immunization and no protection for unsuccessful vaccination), whereas (e)-(h) show the effectiveness of leaky vaccine (i.e. imperfect protection for all vaccinated individuals).</p

Change in the proportion of immunised wild boar by the bait vaccination among the total wild boar population at the beginning of vaccination campaign, (<i>V</i>_i (<i>t</i>))/(<i>N</i>_i(28)).

Mean estimated increment of the proportion of ELISA(+) PCR(-) is demonstrated by the solid line. The 95% confidence intervals are shown by dashed lines. The bait vaccination was implemented in the weeks 28–29, weeks 34–35, week 44, and weeks 49–50. Arrows denote the timing of vaccine campaigns.</p

Parameter values for the SIR (Susceptible-Infectious-Recovered) model as applied to the epidemiological dynamics of enterovirus 71.

<p>Parameter values for the SIR (Susceptible-Infectious-Recovered) model as applied to the epidemiological dynamics of enterovirus 71.</p

Sensitivity analysis with respect to the condition for data extraction.

The required vaccination effort was estimated by varying the data-extraction criteria in terms of the test frequency against CSF. The grids where at least one test was enrolled by every 9, 10, 11, 12, and 13 weeks were analysed (baseline value = 12). Mean estimated vaccination effort for the elimination of CSF is demonstrated by the solid lines. The dashed lines denote 95% confidence intervals of the estimated values. (EPS)</p