Exploring the role of mass immunisation in influenza pandemic preparedness: A modelling study for the UK context

Mathematical modelling of the 100-day target for vaccine availability after the detection of a novel pathogen: A case study in Indonesia

Globally, there has been a commitment to produce and distribute a vaccine within 100 days of the next pandemic. This 100-day target will place pressure on countries to make swift decisions on how to optimise vaccine delivery. We used data from the COVID-19 pandemic to inform mathematical modelling of future pandemics in Indonesia for a wide range of pandemic characteristics. We explored the benefits of vaccination programs with different start dates, rollout capacity, and age-specific prioritisation within a year of the detection of a novel pathogen. Early vaccine availability, public uptake of vaccines, and capacity for consistent vaccine delivery were the key factors influencing vaccine benefit. Monitoring age-specific severity will be essential for optimising vaccine benefit. Our study complements existing pathogen-specific pandemic preparedness plans and contributes a tool for the rapid assessment of future threats in Indonesia and similar middle-income countries.

What can we learn from historical pandemics? A systematic review of the literature

What are the insights from historical pandemics for policymaking today? We carry out a systematic review of the literature on the impact of pandemics that occurred since the Industrial Revolution and prior to Covid-19. Our literature searches were conducted between June 2020 and September 2023, with the final review encompassing 169 research papers selected for their relevance to understanding either the demographic or economic impact of pandemics. We include literature from across disciplines to maximise our knowledge base, finding many relevant articles in journals which would not normally be on the radar of social scientists. Our review identifies two gaps in the literature: (1) the need to study pandemics and their effects more collectively rather than looking at them in isolation; and (2) the need for more study of pandemics besides 1918 Spanish Influenza, especially milder pandemic episodes. These gaps are a consequence of academics working in silos, failing to draw on the skills and knowledge offered by other disciplines. Synthesising existing knowledge on pandemics in one place provides a basis upon which to identify the lessons in preparing for future catastrophic disease events.

Assessing potential insights of an imperfect testing strategy: Parameter estimation and practical identifiability using early COVID-19 data in India

A deterministic model with testing of infected individuals has been proposed to investigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depending on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic's severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.

Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.

Influenza Vaccination Assessment according to a Value-Based Health Care Approach

Background: Seasonal influenza has a considerable public health impact, and vaccination is the key to preventing its consequences. Our aim was to describe how the value of influenza vaccination is addressed in the scientific literature considering a new value framework based on four pillars (personal, allocative, technical, and societal value). Methods: A systematic review was conducted by querying three databases. The analysis was performed on international studies focused on influenza vaccination value, and the four value pillars were taken into consideration for their description. Results: Overall, 40 studies were considered. Most of them focused on influenza vaccination in the general population (27.5%), emphasizing its value for all age groups. Most studies addressed technical value (70.4%), especially in terms of economic models and cost drivers to be considered for the economic evaluations of influenza vaccines, and societal value (63%), whereas few dealt with personal (37%) and allocative values (22.2%). Conclusions: The whole value of influenza vaccination is still not completely recognized. Knowledge and communication of the whole value of influenza vaccination is essential to guide value-based health policies. To achieve this goal, it is necessary to implement initiatives that involve all relevant stakeholders.

Organisational and Structural Drivers of Childhood Immunisation in the European Region: A Systematic Review

Despite the implementation of widespread vaccination programs, the European Health Systems continue to experience care challenges attributable to organizational and structural issues. This study aimed to review the available data on aspects within the organizational and structural domains that might impact vaccination coverage. We searched a comprehensive range of databases from 1 January 2007 to 6 July 2021 for studies that reported quantitative or qualitative research on interventions to raise childhood vaccine coverage. Outcome assessments comprised organizational and structural factors that contribute to vaccine concern among pediatric parents, as well as data reported influencing the willingness to vaccinate. To analyze the risk of bias, the Ottawa, JBI's (Joanna Briggs Institute) critical appraisal tool, and Amstar quality assessment were used accordingly. The inclusion criteria were met by 205 studies across 21 articles. The majority of the studies were conducted in the United Kingdom (6), the European Union (3), and Italy (3). A range of interventions studied in primary healthcare settings has been revealed to improve vaccination coverage rates including parental engagement and personalization, mandatory vaccination policies, program redesign, supply chain design, administering multiple/combination vaccines, improved vaccination timing and intervals, parental education and reminders, surveillance tools and Supplemental Immunisation Activity (SIA), and information model.

Testing, tracing and isolation in compartmental models

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.