Modelling infectious diseases with herd immunity in a randomly mixed population

A post-marketing study to evaluate the safety and immunogenicity of a quadrivalent influenza split-virion vaccine in elderly people aged 60 years and older

BACKGROUND

Influenza remains a global public health concern. Understanding the vaccination-induced response in an aging population, which is susceptible and at high risk, is essential for disease prevention and control. Here, we report findings on the safety and immunogenicity of a quadrivalent influenza split-virion vaccine (15 µg/subtype/0.5 ml/dose) (hereinafter referred to as the "quadrivalent influenza vaccine") in a population aged ≥ 60 years.

METHODS

This open-label, pragmatic post-marketing trial enrolled 1399 older adults to receive one dose of an approved commercially available quadrivalent influenza vaccine manufactured by Hualan Biological Bacterin Inc. (hereinafter referred to as "Hualan Bio"). Participants with contraindications for the vaccine were excluded, while poor health condition was acceptable. All vaccinated subjects experienced adverse events collection within 30 days and serious adverse events within 180 days post-vaccination. 25% subjects, selected randomly, underwent venous blood sampling pre-vaccination and 30 days after post-vaccination, for detecting antibody titers against each subtype of influenza virus by hemagglutination inhibition assay. The incidences of adverse events and antibody titers against each subtype of influenza virus were statistically analyzed using SAS 9.4.

RESULTS

No grade 3 adverse reactions occurred within 30 days post-vaccination. The incidences of overall adverse reactions, local adverse reactions and systemic adverse reactions were 3.79%, 2.86% and 1.00%, respectively. No serious adverse reactions occurred within 180 days post-vaccination. There were 350 subjects who completed venous blood sampling pre-vaccination, among whom 348 subjects completed venous blood sampling at 30 days post-vaccination for immunogenicity assessment. With respect to hemagglutination inhibition antibodies against influenza viruses H1N1, H3N2, BV and BY subtypes, at 30 days post-vaccination, the seroconversion rates were 87.64%, 75.57%, 73.28% and 78.74%, respectively; the seropositive rates were 93.97%, 98.56%, 79.31% and 95.40%, respectively; and the geometric mean increase (GMI) in post-immunization/pre-immunization antibodies was 24.80, 7.26, 10.39 and 7.39, respectively.

CONCLUSION

One 15 µg/subtype dose of the vaccine had a good safety profile and elicited favorable immunogenicity among subjects aged ≥ 60 years. The results of this study indicate that Hualan Bio quadrivalent influenza vaccine strike balance between safety and immunogenicity, supporting unnecessity to increase dosage or inoculation frequency for further enhancing immunogenicity.

TRIAL REGISTRATION

Registered on ClinicalTrials.gov.

REGISTRATION NUMBER

NCT06334510. Registered on 28/03/2024 (retrospectively registered).

Analytical tool for COVID-19 using an SIR model equivalent to the chain reaction equation of infection

Insights from data analysis of existing cases are important to prevent future outbreaks of coronavirus disease 2019 (COVID-19). Although mathematical models are expected to be useful for this purpose, the adequacy of reproducibility of these models is difficult to confirm because they are based on hypotheses. For example, using the time variation of the parameter of the basic reproduction number for the time variation of complex data on the number of infected persons is a change of expression and does not capture the substance of the problem. We previously showed that the simplest Susceptible, Infected, Recovered (SIR) model alone, without any complex models, exhibits acceptable reproducibility. By clarifying the rationale for this reproducibility, quantifiable characteristics regarding the infection spread, such as the duration of the pandemic and the mechanism of occurrence of several large waves, can be uncovered and this can contribute to countermeasures. Here, we show this method equals the chain reaction equation for infection, allowing the parameters (infection rate, population) of the mathematical models to be extracted from the data. Once a model that reproduces the actual situation is determined, much of the information becomes apparent. As an example, we present three characteristics of the spread of infection effective in controlling COVID-19: the time of onset of infection, the rapidity of the spread, and the time to acquisition of herd immunity. Acquiring this information is likely to increase the predictive accuracy of the model.

Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19

An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics—the World, Israel, The United States of America, and Japan.

Modelling the impact of perfect and imperfect vaccination strategy against SARS CoV-2 by assuming varied vaccine efficacy over India

Background

The outbreak of Coronavirus disease (COVID-19) has swiftly spread globally and caused public health and socio-economic disruption in many countries. An epidemiological modelling studies in the susceptible-infectious-removed (SIR) has played an important role for making effective public health policy to mitigate the spread of COVID-19. The aim of the present study is to investigate the optimal vaccination strategy to control the COVID-19 pandemic in India.

Methods

We have applied compartment mathematical model susceptible-vaccination-infectious-removed (SVIR) with different range of vaccine efficacy scenarios and predicted the population to be covered for vaccination per day in India as well as state level was performed.

Results

The model assumed that a vaccine has 100% efficacy, predicted that >5 million populace to be vaccinated per day to flatten the epidemic curve in India. Similarly, different vaccination mechanisms such as ‘all-or-nothing’ (AoN) and leaky vaccines does not have potential discordance in their effectiveness at higher efficacies (>70%). However, AoN vaccine was found to be marginally effective than leaky at lower efficacies (<70%) when administered at the higher coverage strategies. Further state level analyses were performed and it was found that 0.3, 0.3, 0.2 and 1 million vaccinations required per day in Andhra Pradesh, Gujarat, Kerala and Maharashtra as it assumes that the vaccine efficacy is 70%.

Conclusion

The proposed modelling approach shows a range of assumptions on the efficacy of vaccine which helps the health authorities to prioritize the vaccination strategies to prevent the transmission as well as disease.