The potential global health impact and cost-effectiveness of next-generation influenza vaccines: A modelling analysis

Lucy Goodfellow; Simon R Procter; Mihaly Koltai; Naomi R Waterlow; Johnny A N Filipe; Carlos K H Wong; Edwin van Leeuwen; Rosalind M Eggo; Mark Jit; WHO Technical Advisory Group for the Full Value of Influenza Vaccines Assessment and project team; Next-generation influenza vaccine impact modelling contributors

doi:10.1371/journal.pmed.1004655

. 2025 Jun 30;22(6):e1004655. doi: 10.1371/journal.pmed.1004655

The potential global health impact and cost-effectiveness of next-generation influenza vaccines: A modelling analysis

Lucy Goodfellow ^1,^*,^#, Simon R Procter ^1,^*,^#, Mihaly Koltai ¹, Naomi R Waterlow ¹, Johnny A N Filipe ¹, Carlos K H Wong ^1,², Edwin van Leeuwen ^1,³, Rosalind M Eggo ^1,^‡, Mark Jit ^1,^‡; WHO Technical Advisory Group for the Full Value of Influenza Vaccines Assessment and project team^1,^¶; Next-generation influenza vaccine impact modelling contributors^1,^¶

Editor: Rebecca F Grais⁴

PMCID: PMC12237265 PMID: 40587557

Abstract

Background

Next-generation influenza vaccines (NGIVs) are in development and have the potential to achieve substantial reductions in influenza burden, with resulting widespread health and economic benefits. The prices at which their market can be sustained and which vaccination strategies may maximise health impact and cost-effectiveness, particularly in low- and middle-income countries, are unknown, yet such an understanding could provide a valuable tool for vaccine development and investment decision-making at a national and global level. To address this evidence gap, we projected the health and economic impact of NGIVs in 186 countries and territories.

Methods and findings

We inferred current influenza transmission parameters from World Health Organization (WHO) FluNet data in regions defined by their seasonal influenza timing and positivity, and projected 30 years of influenza epidemics, accounting for demographic changes. We considered vaccines including current seasonal vaccines, vaccines with increased efficacy, duration, and breadth of protection, and universal vaccines, defined in line with WHO Preferred Product Characteristics. We estimated cost-effectiveness of different vaccination scenarios using novel estimates of key health outcomes and costs. NGIVs have the potential to substantially reduce influenza burden: compared to no vaccination, vaccinating 50% of children aged under 18 annually prevented 1.3 (95% uncertainty range (UR): 1.2–1.5) billion infections using current vaccines, 2.6 (95% UR: 2.4–2.9) billion infections using vaccines with improved efficacy or breadth, and 3.0 (95% UR: 2.7–3.3) billion infections using universal vaccines. In many countries, NGIVs were cost-effective at higher prices than typically paid for existing seasonal vaccines. However, tiered prices may be necessary for improved vaccines to be cost-effective in lower income countries. This study is limited by the availability of accurate data on influenza incidence and influenza-associated health outcomes and costs. Furthermore, the model involves simplifying assumptions around vaccination coverage and administration, and does not account for societal costs or budget impact of NGIVs. How NGIVs will compare to the vaccine types considered in this model when developed is unknown. We conducted sensitivity analyses to investigate key model parameters.

Conclusions

This study highlights the considerable potential health and economic benefits of NGIVs, but also the variation in cost-effectiveness between high-income and low- and middle-income countries. This work provides a framework for long-term global cost-effectiveness evaluations, and the findings can inform a pathway to developing NGIVs and rolling them out globally.

Author summary

Why was this study done?

Next-generation influenza vaccines are in development, and could prevent additional influenza infections, hospitalisations, and deaths, as they may have improved efficacy and provide longer protection than current seasonal vaccines.
We currently do not know the prices at which these vaccines will become available, but if they reduce costs for the healthcare system and reduce influenza-associated mortality then they may be cost-effective.
Previous studies have estimated the cost-effectiveness of next-generation influenza vaccines in a few countries, but their cost-effectiveness across global regions and national-level income levels is currently unknown.

What did the researchers do and find?

We used an age-structured model of influenza transmission, based on past influenza data in regionally representative countries, to estimate the future health impact in 186 countries over 30 years of using next-generation influenza vaccines compared to using no influenza vaccines.
We generated national-level estimates of the economic value of these prevented health outcomes and healthcare system usage, and used this to estimate the price below which next-generation influenza vaccines would be cost-effective.
We found that these vaccines could have a substantial impact on global influenza burden and be cost-effective in some parts of the world even at high prices, but that for some vaccines in some low- and middle-income countries they may not be cost-effective even if the price is near $0.

What do these findings mean?

Next-generation influenza vaccines may considerably decrease influenza cases, hospitalisations, and deaths, especially with optimal age-targeting.
While likely to be cost-effective in high-income countries, for these vaccines to be globally accessible there is a need for substantial tiered prices and support for vaccine delivery in low- and middle-income countries.
This study is based on assumptions around how next-generation influenza vaccines may compare to current seasonal vaccines and how they may be implemented, and is limited by the availability of accurate data on influenza incidence, health outcomes and costs.

Using influenza surveillance data, Lucy Goodfellow and colleagues model the potential global health impacts and cost effectiveness of next-generation influenza vaccines designed to increase efficacy and breadth.

Introduction

Globally, seasonal influenza is a substantial cause of respiratory illness, morbidity, and mortality, causing 291,243–645,832 deaths annually and significant economic impact through healthcare costs, costs to the individual, and productivity losses [1–3]. The burden varies between countries and wider geographical regions, due to variation in circulating influenza strains and subtypes, population age structure, and current vaccination programmes and coverage. Furthermore, the timing and regularity of influenza epidemics ranges widely around the world, as does the quality and reliability of influenza surveillance data [4].

Seasonal influenza vaccines have been available since the 1940s, and have been subject to extensive improvements and developments since their introduction [5]. However, while influenza vaccines are widely used in the Americas and some high-income countries (HICs), seasonal vaccination coverage remains low globally, and as of 2024 only 34% of low- and lower-middle income countries have a national policy for seasonal influenza vaccination [6].

Current seasonal influenza vaccines face several barriers that can limit their impact and cost-effectiveness. Their duration of protection is less than a year [7], which does not provide immunity through long or multi-peak seasons in temperate and tropical climates, and also requires annual revaccination. Current vaccines must also be reformulated annually based on early estimates of circulating influenza strains and subtypes due to the long timeframe needed for vaccine production. This can lead to very low vaccine effectiveness (VE) in some seasons, particularly in older people for whom vaccines are typically less effective [8]. Next-generation influenza vaccines (NGIVs) are in development which aim to address these limitations, with 40 vaccine candidates currently in clinical trials and over 170 preclinical candidates [9]. The World Health Organization (WHO) defines several types of NGIVs using Preferred Product Characteristics (PPCs); they are categorised as ‘improved’ vaccines, which have increased efficacy or breadth of protection and length of immunity compared to current seasonal vaccines, and ‘universal’ vaccines, which have an increased efficacy and breadth of protection compared to current seasonal vaccines, and immunity lasting up to 5 years [10].

Previous cost-effectiveness analyses conducted in Kenya, UK, and USA have found NGIVs to be cost-effective over a range of willingness-to-pay (WTP) thresholds [11,12]. However, understanding the potential cost-effectiveness of NGIVs globally, and the vaccine prices at which their market can be sustained, is key to informing the planning of possible future investments and decisions made by manufacturers, governments, and potential donors. Here, we expand on models previously used to estimate the national-level cost-effectiveness of NGIVs to generate estimates of prices at which NGIVs would be cost-effective, across 186 countries and territories.

Methods

We used a modelling framework consisting of four steps (Fig 1A) to assess the future impact and cost-effectiveness of NGIVs in 186 countries and territories (hereafter referred to as just countries). The steps were: (1) epidemiological inference model (infer current influenza transmission parameters in regions with similar transmission dynamics), (2) vaccination model (project age- and vaccination status-specific populations in each country), (3) epidemic model (simulate future influenza epidemics), and (4) economic model (estimate cost-effectiveness).

Populations were stratified into four age categories: 0–4, 5–19, 20–64, and 65 + years of age. The age-stratified transmission model used was an extension of the FluEvidenceSynthesis model (Fig 1B) [13], and consisted of 13 compartments: Susceptible (S), Exposed (E1, E2), Infectious (I1, I2), and Recovered (R), their ineffectively vaccinated counterparts (Sv-Rv), and Rev (individuals who were vaccinated effectively) (Fig 1B). The E and I populations were split into two sequential compartments to produce gamma-distributed latent and infectious periods. Susceptibles who were infected progressed through the E and I compartments and entered the R compartment after ceasing to be infectious, whereupon they could not be re-infected during the same epidemic. This transmission model was used for both the epidemiological inference (Step 1) and epidemic model (Step 3). A complete table of model parameters is shown in Table A in S1 Text.

Epidemiological inference model

WHO provides national-level weekly data on laboratory-confirmed influenza through FluNet, an online tool, but the availability and consistency of this data varies widely and could not be used to inform influenza epidemiology in every country [14]. We therefore used a global categorisation of countries with similar influenza epidemiology to project characteristics of influenza transmission inferred for a limited number of countries onto the rest of the world.

We expanded the seven Influenza Transmission Zones (ITZs) produced by Chen and colleagues [4], which classified 109 countries with data available in FluNet using influenza season timing, laboratory-confirmed influenza positivity, and location parameters. The 77 countries not classified in Chen and colleagues [4] due to insufficient influenza surveillance data were assigned to an existing ITZ based on location parameters (Section 2B in S1 Text). The exemplar countries for each ITZ were chosen to maximise the number of years with available data in FluNet and number of laboratory tests performed: Argentina (Southern America), Australia (Oceania-Melanesia-Polynesia), Canada (Northern America), China (Eastern and Southern-Asia), Ghana (Africa), Turkey (Asia-Europe), United Kingdom (Europe). In each exemplar country, we identified distinct influenza A and influenza B epidemics using weekly laboratory-confirmed influenza incidence in the inference period of 1st January 2010–31st December 2019 (Section 2D in S1 Text).

In each exemplar country, age-specific seasonal vaccination coverage was assumed to be constant over the 2010–2019 inference period based on estimates from the same time period, with the exception of the UK, where seasonal vaccination policy changed in 2013 (Section 3D in S1 Text). We determined whether vaccine strains ‘matched’ or ‘mismatched’ dominant circulating strains of influenza A and B in the Northern and Southern Hemisphere in each year using peer-reviewed literature (Section 3E in S1 Text). In line with existing literature, we assumed that in years in which the vaccine strains matched the circulating influenza viruses, VE against infection was 70% in under 65 year olds, and 46% in the age group 65 + , compared to 42% and 28%, respectively, in mismatched years [12,15].

We fitted our model to incidence data independently for each identified epidemic in each country using the Markov Chain Monte Carlo (MCMC) algorithm in the BayesianTools R package [16], and obtained joint posterior samples of the reporting rate, population susceptibility, transmissibility, and initial number of infections (Section 3F in S1 Text).

Vaccination model

To ascertain the future impact of NGIVs, we used a 30 year simulation period between 1st January 2025–31st December 2054. The vaccination model tracked the vaccination status-specific size of each age group over time. Demographic changes (births, mortality, ageing) occurred annually on April 1st (Northern Hemisphere) or October 1st (Southern Hemisphere) using projected 2025 demographic parameters (Section 5A in S1 Text). Vaccinations were given at a constant rate over a 12-week period to accrue to the intended coverage level, beginning on October 1st (Northern Hemisphere) or April 1st (Southern Hemisphere). A proportion of those vaccinated, defined by VE, became immune to infection and entered the Rev compartment; the complement of this proportion did not develop immunity and entered the Sv compartment (Fig 1B). Individuals in Rev moved to S upon the waning of immunity. At the same rate, ineffectively vaccinated individuals returned to their unvaccinated counterpart compartment (i.e., Sv to S, Rv to R).

We considered vaccination scenarios defined by combinations of 5 vaccine types as described by WHO PPCs [10] (Table 1) and 5 age-targeting strategies: ages 0–4, 0–10, 0–17, 65 + , and 0–17 combined with 65 + . The three improved vaccines have increased duration of protection, and efficacy or breadth of protection against strains; universal vaccines are enhanced in all aspects. Vaccine doses were distributed at a rate determined by the mean immunity duration of the vaccine type used (i.e., fewer vaccine doses were given annually for vaccine types with longer immunity duration). Vaccine doses were distributed independently of previous vaccination and infection status, but we did not assume any increased protection upon multiple doses (Section 6B in S1 Text).

Table 1. Vaccine types, based on WHO Preferred Product Characteristics [10]. Some vaccine types (including current) may have ‘mismatched’ seasons where their formulation does not match circulating strains.

Vaccine type	Current seasonal vaccines	Improved (minimal)	Improved (efficacy)	Improved (breadth)	Universal vaccines
Mean duration of protection	6 months	1 year	2 years	3 years	5 years
Vaccine effectiveness (Matched 0–64, 65 + / Mismatched 0–64, 65+)	0.70, 0.46/ 0.42, 0.28	0.70, 0.46/ 0.42, 0.28	0.90, 0.70/ 0.70, 0.46	0.70, 0.46/ 0.70, 0.46	0.90, 0.70/ 0.90, 0.70
Mismatched seasons?	Yes	Yes	Yes	No	No

Open in a new tab

We assumed that vaccination coverage reached 50% in each age group targeted by vaccination programmes, and conducted sensitivity analyses considering 20% or 70% coverage in each targeted age group. We also ran analyses where only duration of immunity or VE improved in NGIVs, to disentangle the combined effects of NGIVs.

Epidemic model

In each year of the simulation period, we randomly sampled a year from the inference period, and sampled the susceptibility and transmissibility of all epidemics starting in that year from their joint posterior distributions, to produce a 30-year period of epidemics occurring with the same frequency and intensity as the inference period in each ITZ. For each year of the simulation period, we also randomly sampled whether formulated vaccines would ‘match’ or ‘mismatch’ circulating strains of influenza A and B in both hemispheres where relevant, using probabilities in alignment with the matching frequencies found in the 2010−19 inference period (Section 3E in S1 Text). We simulated epidemics in each of the 186 countries using the transmission model (Fig 1B), the sampled ITZ-specific epidemiological parameters, and the national age- and vaccination status-specific population sizes calculated in the vaccination model. This was repeated 100 times for each vaccine scenario to determine uncertainty in our estimates. Contact patterns for each country were based on those of Prem and colleagues [17], and reweighted to reflect annual demography (Section 5B in S1 Text).

Infected individuals could experience asymptomatic infection, symptomatic but non-fever infection, fever, hospitalisation, and death. Data on seasonal influenza infection-fatality ratios (IFRs), which are highly age- and context-dependent, are sparse. We calculated national age-specific IFR estimates using data on seasonal influenza-associated respiratory deaths [1], and global age-specific infection-hospitalisation ratios (IHRs) using data from Paget and colleagues [18] (Section 7A in S1 Text), and used these estimates to calculate the predicted number of hospitalisations and deaths. We conducted a systematised review to compare our IFR estimates against the limited literature (Section 10 in S1 Text). There is evidence to support the hypothesis that vaccinated individuals who develop breakthrough infections experience less severe influenza [19,20]; we conducted a sensitivity analysis in which breakthrough infections experienced a 50% reduction in both IHR and IFR, and another in which the infectiousness of individuals experiencing breakthrough infections was assumed to be 50% lower.

Economic model

To estimate the cost-effectiveness of each vaccination scenario, we overlaid a decision tree model onto the underlying dynamic transmission model (Fig AB in S1 Text) and a no-vaccination scenario as the comparator, as national-level data on current seasonal vaccination coverage is sparse and vaccination coverage is low in most of the global population. We calculated the disability-adjusted life years (DALYs) averted by estimating age-specific Years of Life Lost (YLLs) per influenza death using national life tables and combining this with Years Lived with Disability (YLDs) for symptomatic cases, fevers, and hospitalisations (Section 7A in S1 Text). Future DALYs were discounted at a rate of 3%, and in a sensitivity analysis reduced to 0%, as recommended by WHO [21].

We estimated costs from a healthcare-payer perspective (Section 7B in S1 Text). We estimated national costs of hospitalised cases using data from existing systematic reviews in a regression model predicted by national gross domestic product (GDP) per capita, and included the cost of outpatient visits in a sensitivity analysis. Country-level costs of vaccine dose delivery were estimated using data from a meta-regression for low- and middle-income countries [22], and extrapolated to HICs using a regression against healthcare expenditure per capita. As a sensitivity analysis, we also estimated the productivity loss of influenza deaths using a human capital approach. Future costs were discounted at a rate of 3%, and all costs were expressed in 2022 USD.

To inform the potential return on investment to NGIVs developers, we calculated threshold prices per dose for each country below which vaccination would be cost-effective. We used WTP thresholds estimated in Pichon-Riviere and colleagues [23], which are based on how changes in national per-capita health expenditures have affected life expectancy across different countries, and conducted a sensitivity analysis using WTP thresholds of 50% of GDP per capita.

Results

The expanded ITZs and selected exemplar countries are shown in Fig 2A. In most exemplar countries, observed epidemics followed regular seasonality, but the timing of outbreaks was less regular in Ghana and China (the African and Eastern and Southern Asian ITZs, respectively; Fig 2B). Posterior estimates of susceptibility and transmissibility inferred for each epidemic were in similar ranges, and mean predicted reported cases were highly similar to the data (R² = 0.96) (Section 4 in S1 Text).

Globally, the number of influenza infections averted depended on the vaccine characteristics and age-targeting strategies. Current vaccines would prevent 1.33 (95% uncertainty range (UR): 1.20–1.48) billion, or 37% of, infections annually when vaccinating 50% of 0–17 year olds worldwide compared to no vaccinations, but only 117 (95% UR: 105–129) million, or 3% of, infections when targeting the 65 + age group. The number of infections averted increased for NGIVs, with improved (minimal) preventing 1.93 (95% UR: 1.72–2.11) billion and improved (efficacy) and improved (breadth) preventing 2.65 and 2.64 (95% UR: 2.39–2.93) billion annual infections respectively when targeting 0–17 year olds, while universal vaccines prevented 2.96 (95% UR: 2.70–3.27) billion, or 83% of, infections annually. See Table H in S1 Text for annual influenza infections averted under each vaccination scenario.

Some age-targeting strategies were clearly more effective than others: while vaccinating children aged 0–10 required approximately the same number of vaccine doses as vaccinating adults aged over 65, the former strategy prevented up to 9.5x as many infections and 2.5x as many deaths. This is likely because young children have higher contact rates, and so preventing infections in children can lead to highly effective indirect protection for unvaccinated individuals. The global number needed to vaccinate (NNV) to avert one DALY was consistently lowest in the 0–10 age-targeting strategy, and highest in the 65 + age-targeting strategy (Fig 3A), similarly for NNV against infections, hospitalisations, and deaths (Fig AG in S1 Text). While most infections were prevented in the 20–64 age group, averted hospitalisations were concentrated in children under age 5 and adults aged 65 + , and fatalities in the 65 + age group (Fig 3B).

Under no vaccinations, we estimated the average annual number of hospitalisations and deaths between 2025–2054 to be 4.83 (95% UR: 2.82–7.34) million and 1.06 (95% UR: 0.80–1.42) million, respectively. This figure is higher than current estimates [1], due to the assumption of no vaccinations and since populations are predicted to grow and age in the next 30 years. Vaccinating all children under age 18 with current vaccines prevented 1.85 (95% UR: 1.06–2.82) million annual hospitalisations and 357,000 (95% UR: 279,000, 454,000) annual deaths compared to no vaccinations; these figures increased to 2.63 (95% UR: 1.53–3.95) million and 519,000 (95% UR: 401,000–653,000) under improved (minimal) vaccines, and 4.04 (95% UR: 2.33–6.12) million and 826,000 (95% UR: 641,000–1,050,000) under universal vaccines.

Threshold prices below which vaccines are cost-effective tended to increase with national-level income, but varied between countries with similar GDP per capita, even within the same ITZ, indicating that willingness to pay for NGIVs also depends on epidemiology and demography (Fig 4A). NGIVs were associated with higher threshold prices than current seasonal influenza vaccines under all age-targeting strategies in all World Bank income groups (Fig 4B). The 0–10 age-targeting strategy was associated with the highest threshold price across vaccine types in the majority of countries (Fig AP in S1 Text).

We found that current seasonal vaccines were not cost-effective in any low-income countries (LICs) under any age-targeting strategy or price, and only had positive threshold prices in 31% of lower-middle-income countries (LMICs) when vaccinating all children aged 0–10 (Table M in S1 Text). In comparison, vaccinating the same age group with current seasonal vaccines had positive threshold prices in 98% of HICs, and reached up to $430 (95% UR: $200-$810). Improved (minimal, efficacy, breadth) vaccines had feasible threshold prices in very few LICs, but were associated with median threshold prices of up to $12, $32, and $41, respectively, in LMICs when vaccinating children aged 0–10. These vaccines could therefore be cost-effective in many countries, but are unlikely to be cost-effective in LICs without tiered pricing, as the unit price of newly introduced NGIVs may be higher than the estimated threshold prices.

Universal vaccines had positive threshold prices in the majority of countries (184/186) under the 0–10 age-targeting strategy. Median threshold prices ranged up to $5.50 in LICs and $78 in LMICs, ranged between $7.90 and $960 in upper-middle-income countries (UMICs), and between $65 and $4,800 in HICs. It is therefore likely that universal vaccines will be highly cost-effective in HICs, many UMICs and LMICs, but few LICs without access to very low prices.

We conducted a range of sensitivity analyses (Section 9 in S1 Text). More infections, hospitalisations, and deaths were prevented when 70% coverage was achieved in targeted age groups, instead of 50%, similarly less for 20%, but increasing vaccination coverage from 50% to 70% had a diminishing marginal impact of each vaccine dose in terms of reduction of infection (Table N in S1 Text). Reduced relative infectiousness or disease modifying in breakthrough infections was associated with a small further reduction in the number of infections over the 30-year period, although the impact on threshold prices for each vaccine type was small. When comparing the effects of increasing VE or the length of immunity provided, more benefits were found to be due to increased length of immunity. In a sensitivity analysis, we estimated the productivity gains from NGIVs, which demonstrate the potential additional economic benefit of NGIVs from a societal perspective (Section 9G in S1 Text).

Discussion

We found that using NGIVs could have a dramatic impact on global influenza burden and be cost-effective in some parts of the world even if prices are higher than most other vaccines in the routinely recommended schedule, however affordability is likely to be a barrier to adoption in lower income countries based on WTP thresholds calculated from the efficiency of current health expenditure in countries. Vaccinating children aged under 18 years old with currently licensed vaccines could prevent 37% of influenza infections (1.33 billion infections) when compared to no influenza vaccinations; this increased to 53% using minimally improved vaccines and 83% using universal vaccines. However, for all vaccine types, we found less impact per dose in extending coverage above age 10.

The unit price at which NGIVs could be cost-effective varied widely. In many countries, NGIVs are likely to be cost-effective if they were to become available at prices similar to or higher than other recently introduced vaccines [24]. Universal influenza vaccines could become one of the highest value vaccines available in some HICs, with the prices at which vaccines would be cost-effective reaching thousands of dollars. Conversely, in some LICs, only slightly improved vaccines might not be cost-effective from a health-service perspective even if the price was close to $0, and universal vaccines would not be cost-effective in any LICs if they were priced at over $6. Our findings highlight the likely need for substantial tiered prices and support for vaccine delivery to enable global access. These results are consistent with previous country-level analyses’ findings that universal vaccines would likely be cost-effective in the UK, and in Kenya if priced less than $4.94 per dose using a WTP threshold of 45% of GDP per capita [11,12].

We developed novel approaches to simulating future influenza epidemics globally, which allowed us to account for the impact of future demographic changes. A limitation of our data sources was that the model could only be fitted using 10 years of influenza data, was subject to simplifying assumptions such as age-consistent reporting rates, and assumed broadly consistent epidemiology across wide regions of the world. We also did not capture within-country variation in vaccine policy and epidemiology, which may be important in geographically large countries such as Canada and China.

The vaccine types considered in this study were guided by WHO PPCs, which are based on expert opinion from 2017 but may not reflect the current state of vaccine development. Using no vaccinations as a comparator scenario overestimates NGIV cost-effectiveness in settings where current seasonal vaccination coverage is high, but these countries make up the minority of the global population as coverage is globally low. Epidemic inference in exemplar countries where coverage is high may have overlooked epidemics that would have occurred without any vaccinations; this effect is likely small, as we observed relatively consistent seasonality in these countries over the inference period.

Many of our simplifying assumptions cause the cost-effectiveness of NGIVs to be underestimated. The assumption that vaccine doses were delivered independently of vaccination and infection history could lead to underestimation of the benefits of NGIVs, since doses could be targeted at individuals with the longest interval since their last dose. Administering vaccines with longer duration of protection is likely to differ from current seasonal vaccination programmes, as populations could receive vaccinations all year round, as opposed to in a pre-epidemic period, or as part of a routine immunisation program, which could lead to further cost-saving. Fixed seasonal timing for vaccination programs may have a diminished impact in subtropical and equatorial countries with multiple epidemic peaks or undefined influenza seasons, particularly for current seasonal vaccines where immunity wanes during the year; previous research has found that while there is no optimal vaccination timing in no-seasonality settings, the timings chosen in this study closely reflect optimal programs in subtropical and temperate settings [25]. Vaccine wastage in the delivery process may also be lower for NGIVs, which we did not consider in this analysis. We did not consider potential future changes in the prevalence of chronic diseases which may be exacerbated by, or exacerbate, influenza burden. Influenza-associated mortality data used in this study only does not account for non-respiratory deaths, for which data is sparse, particularly in LMICs; further discussion of the limitations of this data can be found in [1]. Our estimated threshold prices were influenced by assumptions about the willingness to pay for improvements in health, for which we have used empirical estimates of the opportunity cost of alternative uses of the healthcare budget [23]. The analysis was performed using a healthcare-payer perspective, which does not account for wider economic costs such as out-of-pocket healthcare payments, time spent on informal care-giving, and lost income and improved productivity. Conversely, depending on the market price, there could be a substantial budget impact of NGIVs, particularly if they were to lead to a large expansion in existing influenza vaccine coverage, potentially decreasing cost-effectiveness.

In conclusion, NGIVs have the potential to significantly improve global health if made widely available, and in many countries would be cost-effective compared to current seasonal vaccines, due to their higher VE and reduced need for re-vaccination. Given the high prices achievable in HICs, there may be potential for tiered pricing in the vaccine market to enhance affordability in LICs and LMICs. While these NGIVs are not yet available, our findings have also shown the health and economic benefits of currently licensed seasonal influenza vaccines in many countries when targeted at children and adolescents.

Supporting information

S1 Text

Including: Table A. Model parameters, used in the epidemic inference, vaccination, and epidemic models (steps 1–3). Fig A. Geographical distribution of the seven ITZs produced by Chen and colleagues [4]. Map base layer from https://gis-who.hub.arcgis.com/pages/detailedboundary. Fig B. (a) The longitude and latitude of the capital cities of each country in the ITZs, and each ITZ’s cluster centroid (marked as X). (b) World map of all countries included in this analysis. Map base layer from https://gis-who.hub.arcgis.com/pages/detailedboundary. Table B. Influenza Transmission Zone assignments of 186 countries, assigned either by Chen and colleagues [5] or added based on geographical parameters. Table C. Summary of FluNet data for each of the chosen exemplar countries between January 2010 and December 2019. Fig C. Epidemic model for inference, with no underlying vaccination model. Vaccinated individuals were assigned Rev with probability equal to vaccine efficiency. Table D. Vaccination coverage levels used for inference in exemplar countries between 2010 and 2,019 in each of the model age groups. Table E. Matching (M) and mismatched (U) vaccinations in each year of the inference period, for influenza A and B, in both hemispheres. Fig D. Posterior distributions of population-level susceptibility and influenza transmissibility in each epidemic used for inference. Fig E. Validation of inference model’s goodness of fit, comparing reported influenza cases and mean predicted influenza cases across all epidemics, stratified by influenza strains and using log scales on both axes. Dotted line indicates x = y. Fig F. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Argentina, Influenza A). (b) Model fits using parameter posteriors. Fig G. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Argentina, Influenza B). (b) Model fits using parameter posteriors. Fig H. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Australia, Influenza A). (b) Model fits using parameter posteriors. Fig I. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Australia, Influenza B). (b) Model fits using parameter posteriors. Fig J. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Canada, Influenza A). (b) Model fits using parameter posteriors. Fig K. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Canada, Influenza B). (b) Model fits using parameter posteriors. Fig L. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (China, Influenza A). (b) Model fits using parameter posteriors. Fig M. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (China, Influenza B). (b) Model fits using parameter posteriors. Fig N. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Ghana, Influenza A). (b) Model fits using parameter posteriors. Fig O. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Ghana, Influenza B). (b) Model fits using parameter posteriors. Fig P. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Turkey, Influenza A). (b) Model fits using parameter posteriors. Fig Q. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (Turkey, Influenza B). (b) Model fits using parameter posteriors. Fig R. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (United Kingdom, Influenza A). (b) Model fits using parameter posteriors. Fig S. (a) Posterior distributions of the initial number of infections, reporting rate, population-level susceptibility, and influenza transmissibility in each epidemic used for inference (United Kingdom, Influenza B). (b) Model fits using parameter posteriors. Fig T. The vaccination model, example shown for the first two years of the simulation period. The whole population begins as unvaccinated. On the ageing date, individuals were removed from the model at age-specific mortality rates (μ_i), and aged into the next age groups at rates proportional to their size. Susceptible newborns were introduced at a rate proportional to the crude birth rate (CBR). Over the vaccination period (12 weeks), individuals were moved into the vaccinated compartment at age-specific rates which depend on vaccination coverage and efficacy (v_i). After the vaccination period, individuals lost their vaccine-induced immunity and moved back into the unvaccinated compartment at rate ω, which varies by vaccine type. The ageing, waning, and vaccination occurs again annually. Fig U. Example vaccination coverage in the 0–4 age group in a Northern Hemisphere country, under 70% vaccination coverage in the 0–4 age group. Table F. Global average annual vaccine doses given over the 30-year projection period under each age-targeting strategy and vaccine type, under 50% vaccination coverage. Fig V. Annual age-specific vaccine doses given under each age-targeting strategy and vaccine type, assuming 50% vaccination coverage. Fig W. Annual vaccine doses given worldwide, stratified by vaccination status of the recipient, under 50% vaccination coverage of 0–17 and 65 + age groups. Fig X. Proportion of annual age-specific vaccine doses given to already-vaccinated individuals (‘null’), assuming 50% vaccination coverage of 0–17 and 65 + age groups. Fig Y. Overlay of 10 simulations of influenza incidence in each exemplar country with no vaccination coverage, stratified by strain. Table G. Annual influenza infections under each vaccine type and age-targeting strategy, assuming 50% vaccination coverage (median, 95% uncertainty intervals). Fig Z. Median distribution of influenza infections across age groups under no vaccinations in each WHO region (shown as crosses), compared to distribution of the 2,025 population (shown as triangles). Table H. Annual influenza infections averted under each vaccine type and age-targeting strategy, assuming 50% vaccination coverage (median, 95% uncertainty intervals), and median percentage of influenza infections averted, compared to under no vaccinations. Fig AA. Number needed to vaccinate, stratified by WHO region, under each age-targeting strategy and vaccine type. Fig AB. Overview of the economic decision tree model. Table I. Probabilities of symptomatic influenza and fever upon infection. Fig AC. Age-specific national IFRs, per 100,000 infections. Map base layer from https://gis-who.hub.arcgis.com/pages/detailedboundary. Table J. Calculated age-specific infection hospitalisation ratios. Table K. Influenza disability weights for each health outcome [26]. Fig AD. Mean estimated costs of care for adult, children, and elderly hospitalisations and outpatient visits, with GDP per capita shown on a log scale. GDP per capita and costs of care in 2022 USD. Data points shown are estimates from the literature. Fig AE. National willingness-to-pay thresholds [23] and 50% of 2,022 GDP per capita. Dotted line indicates y = x. Fig AF. Costs of vaccine dose delivery in LMICs from Portnoy and colleagues [22] with 95% uncertainty intervals (black), and additional HIC data for regression (red), against healthcare expenditure per capita, on a log-log scale. Fig AG. Global number needed to vaccinate to prevent one influenza-associated infection, hospitalisation, or death, for each vaccine type and age-targeting strategy, on a log scale. Fig AH. Number needed to vaccinate to avert one DALY in each WHO region, for each vaccine type and age-targeting strategy, on a log scale. Fig AI. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the African Region. Fig AJ. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the Region of the Americas. Fig AK. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the Eastern Mediterranean Region. Fig AL. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the European Region. Fig AM. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the South-East Asian Region. Fig AN. Averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, in the Western Pacific Region. Fig AO. Median national threshold vaccine prices in each WHO Region, for each vaccine type and age-targeting strategy. Table L. Regional minimum and maximum annual averted outcomes per 100,000 population between 2025–2029 (inclusive), under 50% vaccination coverage in under 18-year-olds (median and 95% uncertainty ranges), for each vaccine type. Range of years chosen for increased comparability with current population sizes. Fig AP. Number of countries in which each age-targeting strategy has the highest median threshold price, under each vaccine type. Table M. Minimum and maximum national threshold prices in each World Bank income group, assuming 50% vaccination coverage, under each age-targeting strategy and vaccine type, and proportion of countries in which the median threshold cost is above $0. Fig AQ. Global annual averted age-specific health outcomes under each age-targeting strategy and vaccine type, under 20%, 50%, and 70% vaccination coverage. Table N. Annual global averted infections, hospitalisations, and deaths under 20%, 50%, and 70% coverage, under the 0–10 age-targeting strategy (median and 95% uncertainty ranges). Fig AR. Median national threshold vaccine prices in each World Bank income group, for each vaccine type and age-targeting strategy, with reduced relative infectiousness in vaccinated individuals. Fig AS. Median national threshold vaccine prices in each World Bank income group, for each vaccine type and age-targeting strategy, with disease modification in vaccinated individuals. Fig AT. Number needed to vaccinate associated with original vaccine mechanisms and with reduced relative infectiousness of vaccinated individuals, under each age-targeting strategy and vaccine type, with 50% and 95% uncertainty intervals. Table O. Vaccine characteristics under the base case, breath, and depth scenarios. Fig AU. Number needed to vaccinate for each original and modified vaccine type, under each age-targeting strategy and vaccine type, with 50% and 95% uncertainty intervals. Fig AV. Median national threshold vaccine prices in each World Bank income group, for each vaccine type and age-targeting strategy, with willingness-to-pay thresholds set as 50% of GDP per capita. Fig AW. Median national threshold vaccine prices in each World Bank income group, for each vaccine type and age-targeting strategy, with discount rates for DALYs set at 0%. Fig AX. Median national threshold vaccine prices in each World Bank income group, for each vaccine type and age-targeting strategy, with the inclusion of outpatient visits and their associated costs. Table P. Productivity costs saved in 2025–2050, inclusive, by 50% vaccination coverage in individuals aged 0–17, for various influenza vaccines, in each WHO region. Costs presented in $2022, discounted at a rate of 3%. Fig AY. PRISMA flow diagram of the selection of studies reporting infection-fatality ratios. Table Q. Characteristics of the studies included in the review. Fig AZ. Forest plot of seasonal influenza IFR estimates from the Hong Kong study and from the model. The empirical estimates are from three different periods during 2009 through to 2011 and from two influenza strains, A(H3N2) and A(H1N1) 2009. Fig BA. Forest plot of A(H1N1) 2009 pandemic influenza IFR estimates from empirical studies and from the seasonal influenza model.

(DOCX)

pmed.1004655.s001.docx^{(21.6MB, docx)}

S2 Text. CHEERS 2022 Checklist.

(DOCX)

pmed.1004655.s002.docx^{(19.9KB, docx)}

Acknowledgments

WHO Technical Advisory Group for the Full Value of Influenza Vaccines Assessment and project team: WHO FVIVA Technical Advisory Group members: Jon Abramson, Salah Al Awaidy, Silvia Bino, Rebecca Jane Cox, Luzhao Feng, Jodie McVernon, Harish Nair, Anthony T Newall, Punnee Pitisuttithum. WHO FVIVA project team members: Philipp Lambach, Mitsuki Koh, Joseph Bresee, Stefano Malvolti, Carsten Mantel, Sara Sá Silva, Adam Soble, Carlo Federici. Next-generation influenza vaccine impact modelling contributors: Paula Barbosa, Shawn Gilchrist, Dafrossa Lyimo, Rajinder Suri, Joseph T Wu. We also thank Eduardo Azziz-Baumgartner and Kathryn Lafond for helpful discussions. Two members of WHO FVIVA project team work for the World Health Organisation (PL and MK). The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the World Health Organisation.

Data Availability

All input data is from publicly available sources and can be found at at https://doi.org/10.5281/zenodo.15535351.

Funding Statement

LG, SRP, NRW, RME and MJ were funded through the Task Force for Global Health (grant numbers INF-CDC-R2R; INF-CDC-PV3, INF-CDC-PV4, www.taskforce.org) in collaboration with Partnership for Influenza Vaccine Introduction (PIVI, www.pivipartners.org), Ready2Respond (www.ready2respond.org), Wellcome Trust (www.wellcome.org), Centers for Disease Control and Prevention (CDC, www.cdc.gov), and by the World Health Organization (grant number 2305-IAI-PDR-Flu-Vac, www.who.int). JF and CW were funded by AIR@InnoHK administered by Innovation and Technology Commission, Government of Hong Kong Special Administrative Region, China, as part of the Laboratory of Data Discovery for Health (D24H, www.d24h.hk). EvL, RME, and MJ were also supported by the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, a partnership between UKHSA, Imperial College London, and LSHTM (grant number NIHR200908, www.nihr.ac.uk). The views expressed are those of the authors and not necessarily those of the UK Department of Health and Social Care (DHSC), NIHR, or UKHSA. The funders had no other role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet. 2018;391(10127):1285–300. doi: 10.1016/S0140-6736(17)33293-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Van Wormer JJ, King JP, Gajewski A, McLean HQ, Belongia EA. Influenza and Workplace Productivity Loss in Working Adults. J Occup Environ Med. 2017;59(12):1135–9. doi: 10.1097/JOM.0000000000001120 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Federici C, Cavazza M, Costa F, Jommi C. Health care costs of influenza-related episodes in high income countries: A systematic review. PLoS One. 2018;13(9):e0202787. doi: 10.1371/journal.pone.0202787 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Chen C, Jiang D, Yan D, Pi L, Zhang X, Du Y, et al. The global region-specific epidemiologic characteristics of influenza: World Health Organization FluNet data from 1996 to 2021. Int J Infect Dis. 2023;129:118–24. doi: 10.1016/j.ijid.2023.02.002 [DOI] [PubMed] [Google Scholar]
5.Kim Y-H, Hong K-J, Kim H, Nam J-H. Influenza vaccines: Past, present, and future. Rev Med Virol. 2022;32(1):e2243. doi: 10.1002/rmv.2243 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.World Health Organization. Global Market Study, Seasonal Influenza Vaccine. 2024. Available from: https://cdn.who.int/media/docs/default-source/immunization/mi4a/who_mi4a_global_market_study_seasonal_influenza_vaccine.pdf?sfvrsn=9f24acbf_3&download=true#:~:text=Seasonal%20influenza%20demand%20is%20stable,policy%20for%20seasonal%20influenza%20vaccination [Google Scholar]
7.Young B, Sadarangani S, Jiang L, Wilder-Smith A, Chen MI-C. Duration of Influenza Vaccine Effectiveness: A Systematic Review, Meta-analysis, and Meta-regression of Test-Negative Design Case-Control Studies. J Infect Dis. 2018;217(5):731–41. doi: 10.1093/infdis/jix632 [DOI] [PubMed] [Google Scholar]
8.Belongia EA, McLean HQ. Influenza Vaccine Effectiveness: Defining the H3N2 Problem. Clin Infect Dis. 2019;69(10):1817–23. doi: 10.1093/cid/ciz411 [DOI] [PubMed] [Google Scholar]
9.Universal Influenza Vaccine Technology Landscape | CIDRAP. 21. Feb 2017 [cited 14 Mar 2024]. Available from: https://ivr.cidrap.umn.edu/universal-influenza-vaccine-technology-landscape [Google Scholar]
10.WHO Preferred Product Characteristics for Next-Generation Influenza Vaccines. 1 May 2017. [cited 14 Mar 2024]. Available from: https://www.who.int/publications-detail-redirect/9789241512466 [Google Scholar]
11.Waterlow NR, Radhakrishnan S, Dawa J, van Leeuwen E, Procter SR, Lambach P, et al. Potential health and economic impact of paediatric vaccination using next-generation influenza vaccines in Kenya: a modelling study. BMC Med. 2023;21(1):106. doi: 10.1186/s12916-023-02830-w [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Waterlow NR, Procter SR, van Leeuwen E, Radhakrishnan S, Jit M, Eggo RM. The potential cost-effectiveness of next generation influenza vaccines in England and Wales: A modelling analysis. Vaccine. 2023;41(41):6017–24. doi: 10.1016/j.vaccine.2023.08.031 [DOI] [PubMed] [Google Scholar]
13.van Leeuwen E, Klepac P, Thorrington D, Pebody R, Baguelin M. fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks. PLoS Comput Biol. 2017;13(11):e1005838. doi: 10.1371/journal.pcbi.1005838 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.World Health Organization. FluNet. Global Influenza Programme. [cited 2024 August 22]. Available from: https://www.who.int/tools/flunet [Google Scholar]
15.Baguelin M, Flasche S, Camacho A, Demiris N, Miller E, Edmunds WJ. Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. PLoS Med. 2013;10(10):e1001527. doi: 10.1371/journal.pmed.1001527 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Hartig F, Minunno F, Paul S, Cameron D, Ott T, Pichler M. BayesianTools: General-purpose MCMC and SMC samplers and tools for Bayesian statistics. 2023. https://cran.r-project.org/web/packages/BayesianTools/index.html [Google Scholar]
17.Prem K, Zandvoort K van, Klepac P, Eggo RM, Davies NG, et al.; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Comput Biol. 2021;17(7):e1009098. doi: 10.1371/journal.pcbi.1009098 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Paget J, Staadegaard L, Wang X, Li Y, van Pomeren T, van Summeren J, et al. Global and national influenza-associated hospitalisation rates: Estimates for 40 countries and administrative regions. J Glob Health. 2023;13:04003. doi: 10.7189/jogh.13.04003 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Arriola C, Garg S, Anderson EJ, Ryan PA, George A, Zansky SM, et al. Influenza Vaccination Modifies Disease Severity Among Community-dwelling Adults Hospitalized With Influenza. Clin Infect Dis. 2017;65(8):1289–97. doi: 10.1093/cid/cix468 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Regan AK, Arriola CS, Couto P, Duca L, Loayza S, Nogareda F, et al. Severity of influenza illness by seasonal influenza vaccination status among hospitalised patients in four South American countries, 2013-19: a surveillance-based cohort study. Lancet Infect Dis. 2023;23(2):222–32. doi: 10.1016/S1473-3099(22)00493-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.World Health Organization. WHO guide for standardization of economic evaluations of immunization programmes. 2nd ed. 17 Oct 2019. [cited 25 Jun 2024]. Available from: https://www.who.int/publications/i/item/who-guide-for-standardization-of-economic-evaluations-of-immunization-programmes-2nd-ed [Google Scholar]
22.Portnoy A, Vaughan K, Clarke-Deelder E, Suharlim C, Resch SC, Brenzel L, et al. Producing Standardized Country-Level Immunization Delivery Unit Cost Estimates. Pharmacoeconomics. 2020;38(9):995–1005. doi: 10.1007/s40273-020-00930-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Pichon-Riviere A, Drummond M, Palacios A, Garcia-Marti S, Augustovski F. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. Lancet Glob Health. 2023;11(6):e833–42. doi: 10.1016/S2214-109X(23)00162-6 [DOI] [PubMed] [Google Scholar]
24.Hussain R, Bukhari NI, Ur Rehman A, Hassali MA, Babar Z-U-D. Vaccine Prices: A Systematic Review of Literature. Vaccines (Basel). 2020;8(4):629. doi: 10.3390/vaccines8040629 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Yue M, Dickens BL, Yoong JS-Y, I-Cheng Chen M, Teerawattananon Y, Cook AR. Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study. Value Health. 2019;22(12):1345–54. doi: 10.1016/j.jval.2019.07.001 [DOI] [PubMed] [Google Scholar]
26.Institute for Health Metrics and Evaluation IHME. Global Burden of Disease Study 2016 (GBD 2016) Disability Weights. [cited 2023 October 1]. Available from: https://ghdx.healthdata.org/record/ihme-data/gbd-2016-disability-weights [Google Scholar]

PLoS Med. doi: 10.1371/journal.pmed.1004655.r001

Decision Letter 0

Louise Gaynor-Brook

Dear Dr Goodfellow,

Thank you for submitting your manuscript entitled "The potential global impact and cost-effectiveness of next-generation influenza vaccines: a modelling analysis" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review. Please include continuous line numbers in your revised manuscript (i.e. not starting from 1 with each new page).

Before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Sep 23 2024 11:59PM.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email me at lgaynor@plos.org if you have any queries relating to your submission.

Kind regards,

Louise Gaynor-Brook, MBBS PhD

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004655.r002

Decision Letter 1

Syba Sunny

Dear Dr Goodfellow,

Many thanks for submitting your manuscript "The potential global impact and cost-effectiveness of next-generation influenza vaccines: a modelling analysis" (PMEDICINE-D-24-03112R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, the reviewers asked for some things to be clarified and offered some suggestions for improvement. However, they all agreed that the paper was of interest. After discussing the paper with the editorial team and an academic editor with relevant expertise, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Feb 11 2025 11:59PM. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Best regards,

Syba

Syba Sunny, MBBS, MRes, FRCPath

Associate Editor

PLOS Medicine

ssunny@plos.org

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: This is a very well written manuscripts with interesting data presented. Here are some minor comments for the authors to consider:

1) In lines 186 - 187 the authors state that the vaccines were given over a 12-week period, beginning on October 1st (Northern Hemisphere) or April 1st (Southern Hemisphere). Can the authors consider discussing the possible implications of this timing approach, particularly for countries that experience multiple epidemics and for the current seasonal vaccines where the mean duration of protection is 6 months, on disease burden averted estimates that they provide? It likely won't matter much for modeled estimates for the "improved" and "universal" vaccines.

2) Also, for the 12-week vaccination period, can the authors clarify whether they assume a uniform weekly/monthly distribution in vaccination rates to accrue to the 20%, 50% and 70% coverage mark that they assess?

3) As the authors righty state, data on seasonal influenza infection-fatality ratios are sparse, more so in LICs and LMICs. Notably, the authors indicate that they calculated national age-specific IFR estimates using data on seasonal influenza-associated respiratory deaths by Iuliano et al. Given the limitations also provided by Iuliano et al in their study on limited data available in LICs and LMICs, and the fact that these IFRs are based on respiratory deaths (which exclude non-respiratory deaths due to influenza), can the authors discuss the potential impact/limitation on their estimates on predicted number of deaths?

4) Might the authors want to include (perhaps in a supplemental table) a summary of the disease burden that can be averted by influenza vaccination each country/territory? Doing this, at least for the current vaccines, might provide useful data for countries that have previously not generated these estimates to start contemplating the potential value of influenza vaccination programs if none exist.

Reviewer #2: In this paper the authors use mathematical modeling to study the potential importance of improved flu vaccines for the epidemic control in the future. The question is relevant, and modeling is in general the right framework to investigate it. The paper is well written but some of the model and other methods are not presented sufficiently clear and may require revision. Results are summarized reasonably well in multiple figures and tables with impressive amount of other results placed in the Supplement. I would like to bring up three groups of questions which need to be addressed for improving readability and increasing the practical value of this work for informing public-health decisions beyond its theoretical contribution:

1) Presenting/justifying modeling setup: Employed methods should be carefully described and motivated.

* For instance, it is unclear what justifies the existence of the ineffectively vaccinated compartments (row 2 in the model diagram 1B). Are they partially protected? Do you apply behavioral changes (elevated or reduced risk) following vaccination to make rows 1 and 2 in the model diagram different or these compartments are created only for tracking vaccination numbers. If the later is true, then it may be better to collapse this structure and estimate vaccination numbers through multiplier of the effectively vaccinated.

* You claim that the model is fitted to incidence data which I presume means reported cases. Please, explain how you estimated ascertainment rate and what variability you assume across countries.

* How do you implement targeted (0-10 or 0-17) vaccination scenarios when such age groups are not defined in the model?

* Assumptions about the magnitude of contact differences and mixing patterns between age groups should be discussed in more details because my guess is that they have critical influence on the relative impact of different targeting approaches.

* How often "mismatched seasons" occur? Is that the same across scenarios which allow it?

2) Vaccine characteristics and vaccination strategies:

* In general, there are multiple possible vaccine-induced effects (on susceptibility to infection, on disease progression, on likelihood for severe outcomes, etc.). Here only "all-or-nothing" vaccines affecting susceptibility are modeled which is a strong assumption. Defining VE as the proportion of vaccinated who develop protection is acceptable as a potential protection mechanism but should be justified. Is that the way flu vaccines work or more likely they provide partial protection to all?

* It is unclear what proportion of the vaccinations are ineffective and what that means (see above). Additional confusion comes from the Supplement (text and fig S23) where the term "ineffectively vaccinated" is used in the sense of "wasted" vaccinations when individuals are still protected from previous vaccination. This is very different from the meaning that we can read from the model diagram implying "no take and no protection".

* In the modeled interventions vaccine doses are distributed independently of previous vaccination. This makes little sense to me if long term (5-year) protection is established and 5-year campaigns are a feasible option.

3) Results interpretations:

* To understand results better will be useful to see the distribution of infections by age groups in the base-case (no vaccination) scenario.

* 37% annual incidence reduction when vaccination 50% of the kids seems very optimistic given your assumptions of VE and coverage even if you match every year. Can you elaborate? The suggestion above may help.

* You predict 50% more infections prevented only due to improved durability from 6 to 12 months (row 272-273). That seems unrealistic given seasonality patterns which btw is unclear how it is modeled. Is this due to the fact that the model assumes exponentially distributed durability of protection and the vaccinated compartment Rev starts losing people immediately which results in significant portion becoming susceptible way before predicted durability window of 6 or 12 months. If yes, is that realistic? If not, what will be the result if alternative distributions are employed.

* I see your point in using no vaccination as a competitor (rows 373-376) but the traditional approach requires to use current standard of care which means current vaccination coverage. Your analysis makes cost-eff estimates more optimistic which should be discussed and acknowledged.

Minor points:

- Figure 1B: Confusion with arrows for w and v*. I think all arrows between row 1 and 2 a labeled wrongly and need to be switched. Unclear what is the meaning of waning (w) for ineffectively vaccinated if they were not protected.

- Row 273-274: Prevented number of infections is the same for improved (efficacy) and improved (breadth) strategies (mean and UR). That is surprising.

Reviewer #3: The authors present a very interesting and important study estimating the potential global impact of the next generation of flu vaccines. Their methods are articulated and span several tasks. From fitting past seasons to gather posterior distributions of key epidemiological variables, to projections of future seasons, and to an economic inspired model to estimate the cost-effectiveness of these vaccines.

The writing is very clear and the narrative fluid. The authors clearly did a colossal work and managed to describe it concisely in main text. Well done.

I think that articles like this are very important, and the work done here deserves to be published. I have however a few comments that I believe should be addressed before hand. In particular:

- The authors project future flu seasons across 180+ countries. To this end, they fit/estimate key epidemic parameters from a group of "exemplar" countries selected considering data quality and availability. These parameters are then assigned to all the other countries in the same cluster of flu transmission. It would be great to see whether this approach produces something that is close to observations in at least of some countries for which we have some data. For example, the parameters estimated in the UK in season X are helpful to project the epidemic in Italy, France, Spain etc..? This test would be important to understand how close/far things could be and to estimate the differences we could expect.

- I wonder if we really need to project some much far into the future. Even more, could not we gather an estimate of the potential impact of the next-gen of vaccines using counterfactual scenarios in the past? What I mean is to fit as many seasons and countries as possible using past data. Then use these estimated parameters to run matched epidemic simulations with and without next-get vaccines. This approach, I feel, might reduce quite a bit the uncertainty that we have when we project so much far in the future.

- On a more general point, it would be great to have an idea about the performance of the estimation also in the main. In the SI we can see the curves for the exemplar countries, which all look very good. It would be great to have a quantitative assessment of the goodness of the fits in the main.

- To simulate future seasons, the author sample at random one of the seasons in the training set. However, the influenza patterns are known to span at least two years (south-north hemisphere dynamics). Considering this, I wonder whether the same approach could be extended by using two seasons (first picked at random plus the adjacent one).

- It is not clear to me how the "mismatched" seasons are selected in the future.

- It would be good to gather as much evidence as possible about the performance (out of sample) of the economic model in past seasons. As noted by the authors many elements feeding that model are uncertain, so gathering estimation about the performance is very important.

- The authors wrote "We fitted our model to incidence data independently for each identified epidemic" does this mean independently for each country or country and year?

Reviewer #4: This is a very interesting paper. I commend the effort to take on such a huge task but I feel it is one worthwhile undertaking. Whilst there is significant uncertainty in these projections over such a long period of time, it highlights the differences between HICs and LMICs which I feel was the intention of this piece of work. After all, all models are wrong but some are useful.

Some comments below:

1) In my PDF copy, Table 1 was a bit mangled - please have a look and fix if needed (it may have just been my version).

2) Was the epidemic model repeated only 100 times for each vaccine scenario to determine uncertainty because of extensive run time - can this be made clear please as one could argue that 100 runs is insufficient and arbitrary.

3) What was the rationale of using a decision tree for the economic model other than simplicity?

4) Please justify using the healthcare payer perspective in the economic modelling for this analysis given that the economic burden of treatment can fall on different stakeholders depending on which country the treatment is provided due to healthcare provision being funded differently. One would argue that a societal perspective should be used for an intervention such as this which includes out-of-pocket payment, loss of productivity and carer costs and health/illness impact. I appreciate this was later highlighted as a limitation.

5) In your discussion, you state that NGIVs could be cost-effective in most parts of the world even if vaccine prices were to be 'high' but it is highly likely that if they were high in LMICs then they would not be used which would diminish the effectiveness in those settings.

6) I would like to see more discussion around the need for cross-subsidy, how this would happen, and to what extent it is needed as I think this is an area where this paper could really have some impact in terms of moving this policy forward on a global level.

7) In the abstract, given the limitations within how the economic analysis was conducted, I would rephrase the final sentence "This work provides a framework for long-term global cost-effectiveness evaluations, and contributes to a full value of influenza vaccines assessment to inform recommendations by WHO, providing a pathway to developing NGIVs and rolling them out globally." to remove the statement about the full value because this is simply not true.

Any attachments provided with reviews can be seen via the following link: [LINK]

--------------------------------------------------------- ---

General editorial requests:

(Note: not all will apply to your paper, but please check each item carefully)

* We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. Please do not add or remove authors without first discussing this with the handling editor. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

* Please upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

FORMATTING - GENERAL

* At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Ideally each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study. In the final bullet point of 'What Do These Findings Mean?', please include the main limitations of the study in non-technical language. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

FIGURES AND TABLES

* Please define all abbreviations used in each figure/table (including those in Supporting Information files).

* Please consider avoiding the use of red and green in order to make your figure more accessible to those with color blindness.

SUPPLEMENTARY MATERIAL

* Please do ensure that you cite your Supporting Information (if relevant) as outlined here: https://journals.plos.org/plosmedicine/s/supporting-information

REFERENCES

* Where website addresses are cited, please include the complete URL and specify the date of access (e.g. [accessed: 12/06/2023]).

MODELLING STUDIES

The following list is derived from Geoffrey P Garnett, Simon Cousens, Timothy B Hallett, Richard Steketee, Neff Walker. Mathematical models in the evaluation of health programmes. (2011) Lancet DOI:10.1016/S0140-6736(10)61505-X:

* If pertinent, please provide a diagram that shows the model structure, including how the natural history of the disease is represented, the process and determinants of disease acquisition, and how the putative intervention could affect the system.

* Please provide a complete list of model parameters, including clear and precise descriptions of the meaning of each parameter, together with the values or ranges for each, with justification or the primary source cited and important caveats about the use of these values noted.

* Please provide a clear statement about how the model was fitted to the data, including goodness-of-fit measure, the numerical algorithm used, which parameter varied, constraints imposed on parameter values, and starting conditions.

* For uncertainty analyses, please state the sources of uncertainties quantified and not quantified [can include parameter, data, and model structure].

* Please provide sensitivity analyses to identify which parameter values are most important in the model. Uncertainty estimates seek to derive a range of credible results on the basis of an exploration of the range of reasonable parameter values. The choice of method should be presented and justified.

* Please discuss the scientific rationale for the choice of model structure and identify points where this choice could influence conclusions drawn. Please also describe the strength of the scientific basis underlying the key model assumptions.

* For studies that develop a prediction model or evaluate its performance, please ensure that the study is reported according to the TRIPOD statement (https://www.equator-network.org/reporting-guidelines/tripod-statement) and include the completed checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD) statement (S1 Checklist)." For studies using machine learning, please use the TRIPOD-AI checklist. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

PLoS Med. doi: 10.1371/journal.pmed.1004655.r003

Decision Letter 2

Alison Farrell

Dear Dr. Goodfellow,

Thank you very much for re-submitting your manuscript "The potential global impact and cost-effectiveness of next-generation influenza vaccines: a modelling analysis" (PMEDICINE-D-24-03112R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by 4 reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

Please note that we request that you address the final point of reviewer 2, in addition to the editorial comments.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

In addition to these revisions, you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by May 30 2025 11:59PM.

Sincerely,

Alison Farrell, PhD

Senior Editor

PLOS Medicine

------------------------------------------------------------

Requests from Editors:

Line 24: Please replace “greater” with a different adjective due to lack of comparator in the sentence (even if implicit).

Line 27: Please qualify “impact”. Add ‘health’? or some other impact? Please consider adding ‘health’ to the title as well.

Line 29: As framed earlier in the abstract, you have not indicated that there is an evidence gap. Can you very briefly state what is unknown, perhaps by revising the second sentence of the Abstract (e.g., along the lines of “The prices at which their market can be sustained…..countries, are unknown, yet such an understanding could provide…”)?

Line 34: Unclear what is meant by “regions defined by their transmission dynamics”. Please clarify.

Line 35: Please use active voice. ”We considered current seasonal vaccines…”

Line 45: Please clarify ‘cross-subsidy” for the general reader.

Line 82: please correct the number.

Line 105: Qualify ‘increased efficacy’ over existing seasonal vaccines.

Line 113: Please ensure that the Introduction ends with a clear description of the study question or hypothesis. In this case, please specify global estimates of what? Are you able to clarify how a global estimate can inform country-level decisions? Or is it better to say estimates across 186 counties and territories?

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Ideally each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study. In the final bullet point of ‘What Do These Findings Mean?’ Please include the main limitations of the study in non-technical language.

Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

Please ensure that you clearly refer the reader to the complete list of model parameters in Supplementary Materials.

For studies in which a novel model is central to the manuscript's findings, as is the case here, authors are responsible for providing the source code needed to replicate the study's findings in a repository (such as GitHub, SourceForge or Bitbucket) or a cloud computing service (such as Code Ocean). Is it possible to assign a doi from Zenodo for your GitHub file?

Please report your economic analysis according to the appropriate study design provided at http://www.equator-network.org/?post_type=eq_guidelines&eq_guidelines_study_design=economic-evaluations&eq_guidelines_clinical_specialty=0&eq_guidelines_report_section=0&s= and provide the relevant completed checklist. In the checklist please include sufficient text excerpted from the manuscript to explain how you accomplished all applicable items.

Please confirm that the funding statement includes: specific grant numbers, initials of authors who received each award, URLs to sponsors’ websites.

It appears that one or more study authors is affiliated with one or more of the agencies that funded the study. Thus, the statement “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript” does not apply. Please revise the Financial Disclosure accordingly, as in "[Author name] is [author's role] at [funding agency]. The funders had no other role in study design…..”

*Please confirm that the appropriate usage rights apply to the use of the maps in Fig. 1 and in Supplementary Materials. Please see our guidelines for map images: https://journals.plos.org/plosmedicine/s/figures#loc-maps

* PLOS has a 'Inclusivity in Global Research' policy which aims to promote collaboration and inclusivity in global health research. You are required to complete PLOS’ questionnaire on inclusivity in global research and submit it with your revised paper. The policy and questionnaire can be found at https://journals.plos.org/plosone/s/best-practices-in-research-reporting.

* Please define all elements of box plots in the figure caption - center line, box limits and whiskers.

* Please consider avoiding the use of both red and green within figures (e.g. the maps and Fig. 4a) in order to make your figures more accessible

Comments from Reviewers:

Reviewer #1: My comments have sufficiently been addressed. Thank you!

Reviewer #2: The authors have made a commendable effort to address all my comments, resulting in a significantly improved revision that is suitable for publication.

However, I remain unconvinced by the conclusion that extending vaccine protection durability from 6 to 12 months would yield a profound impact.

Theoretically, if annual vaccination campaigns were modeled under the following conditions:

- All vaccinated individuals receive their doses at the same time each year.

- Everyone experiences full protection for a fixed duration (either 6 or 12 months) before becoming unprotected.

- The majority of infections occur within the first 6 months post-vaccination.

then the expected difference in outcomes between the two scenarios would be minimal.

Reviewer #3: My comments have been addressed.

Reviewer #4: Thank you for engaging with the comments, the justifications provided, and the revisions made. :)

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1004655.r004

Decision Letter 3

Alison Farrell

Dear Dr Goodfellow,

On behalf of my colleagues and the Academic Editor, Rebecca Grais, I am pleased to inform you that we have agreed to publish your manuscript "The potential global health impact and cost-effectiveness of next-generation influenza vaccines: a modelling analysis" (PMEDICINE-D-24-03112R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Alison Farrell, Ph.D.

Senior Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text

(DOCX)

pmed.1004655.s001.docx^{(21.6MB, docx)}

S2 Text. CHEERS 2022 Checklist.

(DOCX)

pmed.1004655.s002.docx^{(19.9KB, docx)}

Data Availability Statement

All input data is from publicly available sources and can be found at at https://doi.org/10.5281/zenodo.15535351.

[pmed.1004655.ref001] 1.Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet. 2018;391(10127):1285–300. doi: 10.1016/S0140-6736(17)33293-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref002] 2.Van Wormer JJ, King JP, Gajewski A, McLean HQ, Belongia EA. Influenza and Workplace Productivity Loss in Working Adults. J Occup Environ Med. 2017;59(12):1135–9. doi: 10.1097/JOM.0000000000001120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref003] 3.Federici C, Cavazza M, Costa F, Jommi C. Health care costs of influenza-related episodes in high income countries: A systematic review. PLoS One. 2018;13(9):e0202787. doi: 10.1371/journal.pone.0202787 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref004] 4.Chen C, Jiang D, Yan D, Pi L, Zhang X, Du Y, et al. The global region-specific epidemiologic characteristics of influenza: World Health Organization FluNet data from 1996 to 2021. Int J Infect Dis. 2023;129:118–24. doi: 10.1016/j.ijid.2023.02.002 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref005] 5.Kim Y-H, Hong K-J, Kim H, Nam J-H. Influenza vaccines: Past, present, and future. Rev Med Virol. 2022;32(1):e2243. doi: 10.1002/rmv.2243 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref006] 6.World Health Organization. Global Market Study, Seasonal Influenza Vaccine. 2024. Available from: https://cdn.who.int/media/docs/default-source/immunization/mi4a/who_mi4a_global_market_study_seasonal_influenza_vaccine.pdf?sfvrsn=9f24acbf_3&download=true#:~:text=Seasonal%20influenza%20demand%20is%20stable,policy%20for%20seasonal%20influenza%20vaccination [Google Scholar]

[pmed.1004655.ref007] 7.Young B, Sadarangani S, Jiang L, Wilder-Smith A, Chen MI-C. Duration of Influenza Vaccine Effectiveness: A Systematic Review, Meta-analysis, and Meta-regression of Test-Negative Design Case-Control Studies. J Infect Dis. 2018;217(5):731–41. doi: 10.1093/infdis/jix632 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref008] 8.Belongia EA, McLean HQ. Influenza Vaccine Effectiveness: Defining the H3N2 Problem. Clin Infect Dis. 2019;69(10):1817–23. doi: 10.1093/cid/ciz411 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref009] 9.Universal Influenza Vaccine Technology Landscape | CIDRAP. 21. Feb 2017 [cited 14 Mar 2024]. Available from: https://ivr.cidrap.umn.edu/universal-influenza-vaccine-technology-landscape [Google Scholar]

[pmed.1004655.ref010] 10.WHO Preferred Product Characteristics for Next-Generation Influenza Vaccines. 1 May 2017. [cited 14 Mar 2024]. Available from: https://www.who.int/publications-detail-redirect/9789241512466 [Google Scholar]

[pmed.1004655.ref011] 11.Waterlow NR, Radhakrishnan S, Dawa J, van Leeuwen E, Procter SR, Lambach P, et al. Potential health and economic impact of paediatric vaccination using next-generation influenza vaccines in Kenya: a modelling study. BMC Med. 2023;21(1):106. doi: 10.1186/s12916-023-02830-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref012] 12.Waterlow NR, Procter SR, van Leeuwen E, Radhakrishnan S, Jit M, Eggo RM. The potential cost-effectiveness of next generation influenza vaccines in England and Wales: A modelling analysis. Vaccine. 2023;41(41):6017–24. doi: 10.1016/j.vaccine.2023.08.031 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref013] 13.van Leeuwen E, Klepac P, Thorrington D, Pebody R, Baguelin M. fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks. PLoS Comput Biol. 2017;13(11):e1005838. doi: 10.1371/journal.pcbi.1005838 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref014] 14.World Health Organization. FluNet. Global Influenza Programme. [cited 2024 August 22]. Available from: https://www.who.int/tools/flunet [Google Scholar]

[pmed.1004655.ref015] 15.Baguelin M, Flasche S, Camacho A, Demiris N, Miller E, Edmunds WJ. Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. PLoS Med. 2013;10(10):e1001527. doi: 10.1371/journal.pmed.1001527 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref016] 16.Hartig F, Minunno F, Paul S, Cameron D, Ott T, Pichler M. BayesianTools: General-purpose MCMC and SMC samplers and tools for Bayesian statistics. 2023. https://cran.r-project.org/web/packages/BayesianTools/index.html [Google Scholar]

[pmed.1004655.ref017] 17.Prem K, Zandvoort K van, Klepac P, Eggo RM, Davies NG, et al.; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Comput Biol. 2021;17(7):e1009098. doi: 10.1371/journal.pcbi.1009098 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref018] 18.Paget J, Staadegaard L, Wang X, Li Y, van Pomeren T, van Summeren J, et al. Global and national influenza-associated hospitalisation rates: Estimates for 40 countries and administrative regions. J Glob Health. 2023;13:04003. doi: 10.7189/jogh.13.04003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref019] 19.Arriola C, Garg S, Anderson EJ, Ryan PA, George A, Zansky SM, et al. Influenza Vaccination Modifies Disease Severity Among Community-dwelling Adults Hospitalized With Influenza. Clin Infect Dis. 2017;65(8):1289–97. doi: 10.1093/cid/cix468 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref020] 20.Regan AK, Arriola CS, Couto P, Duca L, Loayza S, Nogareda F, et al. Severity of influenza illness by seasonal influenza vaccination status among hospitalised patients in four South American countries, 2013-19: a surveillance-based cohort study. Lancet Infect Dis. 2023;23(2):222–32. doi: 10.1016/S1473-3099(22)00493-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref021] 21.World Health Organization. WHO guide for standardization of economic evaluations of immunization programmes. 2nd ed. 17 Oct 2019. [cited 25 Jun 2024]. Available from: https://www.who.int/publications/i/item/who-guide-for-standardization-of-economic-evaluations-of-immunization-programmes-2nd-ed [Google Scholar]

[pmed.1004655.ref022] 22.Portnoy A, Vaughan K, Clarke-Deelder E, Suharlim C, Resch SC, Brenzel L, et al. Producing Standardized Country-Level Immunization Delivery Unit Cost Estimates. Pharmacoeconomics. 2020;38(9):995–1005. doi: 10.1007/s40273-020-00930-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref023] 23.Pichon-Riviere A, Drummond M, Palacios A, Garcia-Marti S, Augustovski F. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. Lancet Glob Health. 2023;11(6):e833–42. doi: 10.1016/S2214-109X(23)00162-6 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref024] 24.Hussain R, Bukhari NI, Ur Rehman A, Hassali MA, Babar Z-U-D. Vaccine Prices: A Systematic Review of Literature. Vaccines (Basel). 2020;8(4):629. doi: 10.3390/vaccines8040629 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004655.ref025] 25.Yue M, Dickens BL, Yoong JS-Y, I-Cheng Chen M, Teerawattananon Y, Cook AR. Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study. Value Health. 2019;22(12):1345–54. doi: 10.1016/j.jval.2019.07.001 [DOI] [PubMed] [Google Scholar]

[pmed.1004655.ref026] 26.Institute for Health Metrics and Evaluation IHME. Global Burden of Disease Study 2016 (GBD 2016) Disability Weights. [cited 2023 October 1]. Available from: https://ghdx.healthdata.org/record/ihme-data/gbd-2016-disability-weights [Google Scholar]

PERMALINK

The potential global health impact and cost-effectiveness of next-generation influenza vaccines: A modelling analysis

Lucy Goodfellow

Simon R Procter

Mihaly Koltai

Naomi R Waterlow

Johnny A N Filipe

Carlos K H Wong

Edwin van Leeuwen

Rosalind M Eggo

Mark Jit

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Epidemiological inference model

Vaccination model

Table 1. Vaccine types, based on WHO Preferred Product Characteristics [10]. Some vaccine types (including current) may have ‘mismatched’ seasons where their formulation does not match circulating strains.

Epidemic model

Economic model

Results

Fig 3. a) Global number needed to vaccinate to avert one DALY, for each vaccine type and age-targeting strategy, on a log scale. b) Global averted annual age-specific health outcomes under each age-targeting strategy and vaccine type, with 95% uncertainty ranges.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Louise Gaynor-Brook

Roles

Decision Letter 1

Syba Sunny

Roles

Decision Letter 2

Alison Farrell

Roles

Decision Letter 3

Alison Farrell

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases