Highlights
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We used an agent-based model to estimate the impact of more effective influenza vaccines.
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Simulations included vaccine effectiveness ranging from 40 to 95%.
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We also modeled the impact of increased or decreased vaccine uptake.
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Highly effective vaccines would limit influenza burden.
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Even modest increase in vaccine effectiveness had large impact on influenza burden.
Keywords: Influenza, Agent-based modeling, Vaccine
Abstract
Introduction
Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden.
Methods
Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40–95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US.
Results
Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000.
Discussion
Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.
Introduction
Although vaccines for influenza have been available in the US since the late 1940′s [1], vaccine effectiveness varies and is often modest. The Centers for Disease Control and Prevention (CDC) assesses influenza vaccine effectiveness yearly; in the 2004–5 to 2019–20 seasons, influenza vaccine effectiveness ranged from 10 to 60 %, with a mean of ∼ 40 % [2]. Hypothesized reasons for this low effectiveness include poor strain match of vaccine to major circulating strain [3], [4], changes in vaccine during production [5], low immunogenicity [6], and interference from immunity caused by first exposure [7] or by recent vaccination [8], [9], [10].
Development of vaccines using mRNA technology has been proceeding since the 1990s [11]. While initially the technology encountered significant obstacles, many have been resolved over the past decade. mRNA vaccines offer several advantages over conventional vaccines, including rapid vaccine development and manufacturing compared to vaccines grown in cell systems [11], [12]. mRNA vaccines have been shown to elicit strong immune responses in animals [13] and this technology is positioned to become the leader in responding to infectious diseases, particularly newly emerging ones [12]. The technology has been investigated for use against several infectious diseases, including Zika, HIV and rabies [11]. The COVID-19 pandemic accelerated research in mRNA vaccine technology, resulting in development and deployment of mRNA vaccines with efficacy reported as high as 95 % at preventing COVID-19 infection [14], [15], [16], [17]. mRNA vaccines for influenza are in development and have shown to be immunogenic in animal models and human trials [18], [19], [20], [21].
The high efficacy of mRNA vaccines against COVID-19 and the success of mRNA vaccines against influenza in animal models suggest that such influenza vaccines may be more effective in humans than the current vaccines. To investigate the impact of higher efficacy vaccines, an agent-based model (ABM) of influenza implemented in the Framework for Reproducing Epidemiological Dynamics (FRED) with varying levels of vaccine efficacy was used to estimate the possible impact of more effective vaccines on seasonal influenza in the US. ABMs have been used extensively to model influenza [22], [23], [24], [25], [26], [27]. This type of model is ideal for investigating vaccine impacts because characteristics such as age, gender and specific susceptibility to disease can be assigned on an individual basis to agents in the simulation population, resulting in highly flexible and granular models. An additional benefit of the FRED platform is that infections result from the interaction of agents over the course of an influenza season. Therefore, the effects of increasing vaccine efficacy are applied not only to the vaccinated agents but to the wider population of agents with whom they interact, through decreased transmission over the entire season.
Some evidence indicates that COVID-19 mRNA vaccination caused decreased infectiousness and decreased length of infectious period of breakthrough cases, [28], [29], [30], [31], [32], [33], [34] so this was also investigated for seasonal influenza. A portion of the population has reservations about the safety of mRNA vaccines and so might not utilize an mRNA vaccine but conversely, a more effective vaccine for influenza might also encourage vaccination. To investigate those scenarios, the impact of increased and decreased vaccine uptake was also modeled with varied vaccine efficacy.
Methods
FRED is an ABM platform that uses census-based synthetic populations in which agents have household demographics, incomes and locations that are statistically realistic at the US census block group level. FRED agents spread infectious conditions through agent interactions in schools, workplaces, neighborhoods and households [35]. FRED models are based upon conditions, which include definitions of states, state transitions and their probabilities and time periods for evaluation of state changes. Conditions can be written to be generic and combined in a simulation. At each timestep, every agent will be in exactly one state in each condition included in the simulation. Conditions can interact directly by allowing a state change in one condition to cause a state change in another condition. FRED has been used to model influenza and other diseases and conditions and has been described in detail [24], [25], [36], [37].
In FRED the reproduction number for an infectious condition is not an input but is generated by the simulation. Each infectious condition has a transmissibility parameter, an infectious period and other characteristics that interact with the specified population to generate a reproduction number in the simulation. The main simulations described here produced an effective reproduction number (Reff) of ∼ 1.2 in the early phases of the simulation timeline. Comparisons of the base simulations were also performed with higher transmissibility, resulting in Reff of ∼ 1.4 and ∼ 1.8. Reproduction number as calculated from FRED results is a proxy for, but not identical to, reproduction number calculated from surveillance data (see Appendix).
This study used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) model with the addition of a 1-day pre-symptomatic infectious period (Fig. 1). Agents in the model can be hospitalized from influenza and hospitalized agents may die. Hospitalization and death occurred at probabilities drawn from published CDC reports [38]. Input parameters and simulation description are included in Table 1.
Fig. 1.
Influenza model diagram. Influenza was modeled as a modified SEIR, with addition of a pre-symptomatic state, an asymptomatic infectious state, and states representing hospitalization and death.
Table 1.
Model Inputs.
| Inputs | |
| Population | 1,218,695 agents derived from Allegheny County census population |
| Influenza Model States | |
| E exposed | Duration, number of days drawn from a lognormal distribution with μ = 1.9 and σ = 1.23 |
| Ps pre-symptomatic | 1 day; infectious at 50 % of level of Is |
| Is symptomatic infectious | Duration, number of days drawn from a lognormal distribution with μ = 4 and σ = 1.5 in baseline simulation; 75 % of infected agents |
| Ia asymptomatic infectious | Duration, number of days drawn from a lognormal distribution with μ = 5 and σ = 1.5 in baseline simulation; infectious at 50 % of level of Is; 25 % of infected agents |
| Hospitalization (probability applied to Symptomatic infectious agents) | Rates by age group: age 0–4, 6.97 %; age 5–17, 2.74 %; age 18–49, 5.61 %; age 50–64, 1.06 %; age 65+, 9.09 % |
| Death (probability applied to Hospitalized agents) | Rates by age group: age 0–4, 0.80 %; age 5–17, 0.80 %; age 18–49, 3.10 %; age 50–64, 5.75 %; age 65+, 7.73 % |
| Prior Immunity applied at simulation start | Rates by age group: age 0–4, 20.9 %; age 5–17, 15.4 %; age 18–49, 11.1 %; age 50–64, 13.4 %; age 65+, 3.6 %, |
| Vaccine Uptake | Rates by age group: age 0.5–17, 50.4 %; age 18–49, 34.2 %; age 50–64 46.8 %; age 65+, 68.7 % in baseline simulation |
| Vaccine Efficacy | Varied 40–95 % |
| Simulation Parameters | |
| Simulation Period | September 15 to May 31 |
| Simulations Per Scenario | 100 |
To capture immunity from prior year infections on simulation start, agents in the model were initialized with immunity to influenza at rates based on CDC reported influenza infections for the 2019–20 season [38] (rates by age group: age 0–4, 20.9 %; age 5–17, 15.4 %; age 18–49, 11.1 %; age 50–64, 13.4 %; age 65+, 3.6 %, see Appendix). Agents were vaccinated beginning on October 1 of the simulation year. Vaccine uptake was by age group at rates reported by the CDC for 2019–20 [39] (rates by age group: age 0.5–17, probability 0.504; age 18–49 probability 0.342; age 50–64 probability 0.468; age>=65 probability 0.687, see Appendix). Vaccination date for each agent was drawn from a uniform distribution of 1–45 days after October 1 to stagger the timing of immunity. Vaccine immunity is effective 14 days after vaccination in the model. Agents are randomly chosen for application of prior immunity and vaccination (Appendix). Vaccination induced immunity waned at a rate of 7 % per month over the simulation [40].
Simulations used a population created from the 2010 Allegheny County Pennsylvania census population. The population consists of ∼ 1.2 million agents living in urban or suburban areas. The simulation period was one influenza season, extending from September 15 to May 31 (Fig. 2). Results were scaled to reflect total US population. The influenza season was started in the simulations by inserting 50 cases on November 15. The model included a single strain of influenza, representing a season in which a single type A strain predominated.
Fig. 2.
Simulation timeline. Simulations represented one influenza season, beginning on September 15 and ending on May 31. Prior immunity was applied on simulation start. Vaccination began on October 1. The seasonal outbreak began with seeding of cases on November 15.
Vaccination caused a decrease in susceptibility to influenza. Decrease in susceptibility was varied from 40 to 95 %. In additional simulations, vaccine efficacy was varied along with a decrease of infectiousness of breakthrough infections by 25 or 50 % with or without a 1 or 2 day decrease in length of infectious period for breakthrough cases. An additional set of simulations varied vaccine efficacy and included a 1 or 2 day decrease in length of infectious period for breakthrough infections with no change in degree of infectiousness for those cases. Simulation scenarios are listed in Table 2.
Table 2.
Simulation scenarios.
| Vaccine efficacy | Infectious period of breakthrough infections | Level of infectiousness of breakthrough infections | |
|---|---|---|---|
| Base simulation | 40–95 % | Base* | Base* |
| Base with decreased infectious period | 1 day shorter | Base | |
| 2 days shorter | Base | ||
| 25 % decreased level of infectiousness with varied infectious period | base | 25 % decrease | |
| 1 day shorter | 25 % decrease | ||
| 2 days shorter | 25 % decrease | ||
| 50 % decreased level of infectiousness with varied infectious period | base | 50 % decrease | |
| 1 day shorter | 50 % decrease | ||
| 2 days shorter | 50 % decrease |
* Base level of infectiousness produces R ∼ 1.2 with base infectious period.
To estimate the impact of increased or decreased vaccine uptake, uptake rates were increased or decreased from CDC reported rates for 2019–20 by 10 % and 20 % for all age groups. Results were compared with the baseline model at each level of vaccine efficacy.
The University of Pittsburgh Institutional Review Board has determined that this study was not human subject research.
Results
The base model with immunity from prior year infection and vaccination at 40 % efficacy with uptake rates similar to those reported by the CDC resulted in a mean over 100 simulations of 28,052,175 symptomatic cases (Std Dev 5,424,113) when scaled to the total US population. From 2010 to 2020, the CDC estimated a range of 9.3 to 41 million symptomatic influenza infections per year in the US (mean 28,230,000, Std Dev 8,680,508) [41].
Modeled increases in vaccine efficacy alone resulted in dramatic decreases in estimated influenza cases (Fig. 3 Panel A, Table 3). An increase in vaccine efficacy from 40 % to 50 % decreased estimated cases by 43 % from the base scenario to a mean of 15,884,871 (Std Dev 5,859,781). Vaccine efficacy of 80 % resulted in a 99 % decrease in cases (mean 90,549, Std Dev 109,365) (Appendix Table S3).
Fig. 3.
Impact of Higher Vaccine Effectiveness, Decreased Level of Infectiousness and Shorter Infectious Period on Total Symptomatic Influenza Cases in the US. Base scenarios varied vaccine effectiveness from 40 % to 95 %. In scenarios representing decreased length of infectious period in breakthrough cases, length of infectious period was decreased by 1 day or 2 days. In simulations representing a lower level of infectiousness in breakthrough infections scenarios, level of infectiousness was decreased by 25 % or 50 %. In some scenarios, both level of infectiousness and length of infectious period were modified.
Table 3.
Total US symptomatic influenza cases in base simulation with increased vaccine effectiveness and with lower level of infectiousness and/or shorter period of infectiousness in breakthrough infections (infections occurring in vaccinated agents).
| Vaccine Effectiveness |
Simulation Scenario* |
||
|---|---|---|---|
| Base | 1-day shorter infectious period | 2-day shorter infectious period | |
| 40 % | 28,052,175 (5,424,113) | 8,992,725 (4,217,098) | 736,736 (715,947) |
| 50 % | 15,884,871 (5,859,781) | 2,908,146 (2,301,825) | 283,505 (251,078) |
| 60 % | 4,328,639 (2,736,138) | 671,510 (553,063) | 84,031 (73,662) |
| 70 % | 570,059 (582,682) | 145,390 (142,240) | 57,641 (48,999) |
| 80 % | 90,549 (109,365) | 46,929 (43,299) | 28,142 (17,742) |
| 90 % | 27,931 (24,225) | 24,037 (13,646) | 22,653 (10,234) |
| 95 % | 22,144 (10,970) | 21,386 (16,127) | 19,233 (8,326) |
| 25 % less infectious | |||
| Base infectious period | 1-day shorter infectious period | 2-day shorter infectious period | |
| 40 % | 4,482,185 (2,868,150) | 723,381 (666,119) | 129,384 (122,008) |
| 50 % | 1,582,181 (1,261,457) | 306,846 (266,661) | 73,062 (65,945) |
| 60 % | 346,080 (341,315) | 108,201 (101,863) | 52,434 (37,994) |
| 70 % | 105,515 (120,617) | 52,044 (45,781) | 33,844 (19,784) |
| 80 % | 42,114 (32,265) | 31,077 (18,914) | 24,700 (12,505) |
| 90 % | 25,572 (18,343) | 21,871 (9,935) | 19,666 (8,053) |
| 95 % | 20,262 (7,460) | 19,572 (8,010) | 18,361 (6,873) |
| 50 % less infectious | |||
| Base infectious period | 1-day shorter infectious period | 2-day shorter infectious period | |
| 40 % | 193,922 (172,039) | 95,228 (89,498) | 48,621 (33,149) |
| 50 % | 108,878 (93,709) | 49,991 (35,930) | 36,885 (22,902) |
| 60 % | 63,652 (51,335) | 43,303 (35,756) | 33,379 (17,733) |
| 70 % | 39,057 (24,108) | 31,762 (17,143) | 27,614 (13,006) |
| 80 % | 28,052 (15,346) | 24,971 (11,901) | 22,436 (9,129) |
| 90 % | 20,270 (7,826) | 20,143 (9,164) | 18,524 (6,672) |
| 95 % | 19,553 (7,677) | 18,575 (7,087) | 17,579 (5,482) |
*Mean (standard deviation) over 100 simulations.
Simulations with higher transmissibility values resulting in Reff in 100 simulations of ∼ 1.4 and ∼ 1.8 (Appendix Fig. S3, Table S2) resulted in higher infection rates. A given increase in vaccine efficacy reduced cases by a lower percent in higher Reff simulations; however increased vaccine efficacy still resulted in large decreases in infections with reduction to very low levels (>97 % reduction) by vaccine efficacy of 80 % or above without changes in level of infectiousness or length of infectious period of breakthrough cases.
Decreasing the level of infectiousness of breakthrough cases (defined as infections in vaccinated agents) resulted in greater decreases in influenza burden for all levels of vaccine efficacy in the Reff ∼ 1.2 scenarios (Fig. 3 Panel B, Table 3, Appendix Table S3). An increase in vaccine efficacy from 40 % to 50 % coupled with a 25 % decrease in level of infectiousness decreased estimated cases by 94 % from the base level with 1,582,181 cases (Std Dev 1,261,457). Vaccine efficacy of 80 % with 25 % decrease in level of infectiousness resulted in a > 99 % decrease in cases (42,114 cases, Std Dev 32,265). A larger decrease in level of infectiousness of breakthrough cases to 50 % resulted in even greater reductions in influenza burden.
Decreasing the length of the infectious period of breakthrough cases resulted in decreases in influenza burden in addition to that caused by changes in vaccine efficacy (Fig. 3 Panels C & D). At 40 % vaccine efficacy, a decrease in length of infectious period by 1 day resulted in a 68 % decrease in cases over the baseline (8,992,725 cases (Std Dev 4,217,098)). When vaccination efficacy was 80 % with a 1 day decrease in length of infectious period, symptomatic cases decreased by > 99 % to 46,929 (Std Dev 43,299). Decreasing the length of the infectious period by 2 days resulted in larger decreases in symptomatic cases in the simulation (40 % vaccine efficacy, 736,736 cases, Std Dev 715,947; 80 % vaccine efficacy, 28,142 cases, Std Dev 17,742) (Table 3, Appendix Table S3).
Decreasing the level of infectiousness along with decreasing the length of the infectious period of breakthrough cases resulted in the largest decreases in influenza burden (Table 3). With a 50 % effective vaccine, reduction in level of infectiousness by 25 % combined with decreased period of infectiousness by 1 day gave a 99 % reduction in cases from the base simulation, with a mean of 306,846 cases (Std Dev 266,661). At 80 % vaccine efficacy with the same reductions in level of infectiousness and decreased infectious period, mean cases dropped to 31,077 (Std Dev 18,914). Reducing the level of infectiousness of breakthrough cases by 50 % and/or decreasing the length of the infectious period by 2 days resulted in even greater reductions in influenza burden, with reductions of > 99 % in all scenarios (Fig. 3 Panels C & D, Appendix Table S3). Hospitalizations and deaths in the simulations followed similar patterns to reduction in cases (Appendix Table S3).
Increasing or decreasing vaccine uptake resulted in roughly proportional decreases or increases in cases, respectively, for vaccine efficacy values of 40 to 60 % (Fig. 4). A 20 % decrease in vaccine uptake had greater impact at 70 % vaccine efficacy. At 90 % or greater efficacy, differences in uptake had little impact on yearly influenza burden (Appendix Table S4) due to very large decreases in estimated cases.
Fig. 4.
Yearly Symptomatic Influenza Cases in US With Increased Vaccine Effectiveness and Increased or Decreased Vaccine Uptake Base scenarios varied vaccine effectiveness from 40% to 95% and used CDC reported influenza vaccine uptake rates. Vaccine uptake rates were increased or decreased by 10% or 20% across all age groups.
Discussion
Efficacy results from clinical trials as well as effectiveness results in vaccinated populations for the COVID-19 mRNA vaccines exceeded expectations, with short-term reduction in cases, hospitalizations and deaths of greater than 90 % [42], [43]. Development of mRNA influenza vaccines was underway before the COVID-19 pandemic [11] and the success of this type of vaccine for COVID-19 suggests that mRNA vaccines for influenza could be more effective than current vaccines, which have suboptimal vaccine effectiveness [18], [19], [44]. In addition to potentially having a major impact on influenza burden, mRNA vaccines have additional benefits including shorter development and production time and avoidance of the problems associated with egg-based vaccine production (e.g., mutations due to production in eggs). These benefits alone could make the use of mRNA vaccines worthwhile for influenza.
In addition to being highly effective, mRNA vaccination for COVID-19 may have decreased the infectivity of breakthrough cases, potentially through decreasing the level of infectiousness and length of infectious period [28], [29], [31], [32]. Some studies have found viral load in vaccine breakthrough cases to be decreased but the results have been mixed and this phenomenon may be SARS-CoV-2 variant dependent [30], [33], [34]. Breakthrough cases may clear virus more quickly and therefore be infectious for a shorter period [33]. Influenza virus shedding may be reduced in amount and duration due to immunity from vaccine or prior infection [45], making a similar phenomenon possible with mRNA influenza vaccines.
Our simulation results suggest that increases in vaccine efficacy to levels achieved by COVID-19 mRNA vaccines could markedly reduce influenza burden, with even moderate increases in efficacy potentially having a large impact. Even a modest reduction in infectious period or degree of infectiousness of breakthrough cases would be highly beneficial in vaccines of modest efficacy because many infections would be breakthroughs. Decreases in influenza burden in these simulations reflect not only the impact of a more effective vaccine on the vaccinated agent but also the interruption of transmission; therefore, the impact is much higher than what would be seen only by decreasing the probability of infection for the vaccinated agents. Vaccination benefits not only the vaccinated but also those whom they would infect, propagated onward through the population over the influenza season.
In simulations, higher efficacy vaccines had substantial effects without increased vaccine uptake levels above that of recent years. Even a decrease in vaccine uptake from recent levels had little impact on influenza cases in the model when vaccine efficacy was 80 % or higher. When higher efficacy was combined with decrease in infectivity and/or decrease in infectious period, the model estimated substantial reductions in disease burden.
Although mRNA vaccines have been extremely successful for COVID-19, it is unknown whether they will perform as well for influenza. COVID-19 vaccines may have benefited from the highly antigenic spike protein target and from circulation of relatively few antigenic variants of that target compared to influenza antigen targets. Some vaccines using the spike protein target have not achieved as high a level of efficacy as the mRNA vaccines [46]. Durability of protection with mRNA vaccines is still to be determined. mRNA vaccines also tend to be more reactogenic and may be more expensive. Other technologies, such as self-amplifying RNA [47] may prove to be superior. Ongoing studies will provide the data to evaluate mRNA vaccines for influenza but there is a clear need for more effective influenza vaccines.
Strengths and limitations
Modeling has inherent limitations; models are simplifications of reality whose reliability depends on the validity of the model itself and on the accuracy of estimation of parameters upon which the model relies. The usefulness of a model is not in the exact results it produces but in the insights it generates. The simulations described here were designed to estimate the impact of increased vaccine efficacy and of decreases in level of infectiousness and length of infectious period in breakthrough cases due to a more effective vaccine. It may not completely replicate what would occur in a real epidemic, but it provides useful information on the possible impact of those parameters on influenza burden.
The influenza model used in the simulations reported here is based upon one that was designed for the 2009 influenza pandemic. It has been validated and used in multiple influenza simulations since that time and so can be considered a robust design [24], [25], [35]. The model has been modified to include a measure of immunity due to prior infection and to allow for vaccination. Parameters for the reported simulations were drawn mainly from official CDC data and therefore represent the best estimates. With high vaccine efficacy, the simulations produced limited hospitalizations and deaths, resulting in large relative standard deviations for these outcomes.
This set of simulations does not specifically model mRNA vaccines; it models the impact of increased vaccination efficacy. Therefore, although COVID-19 mRNA vaccines suggested the model, the results are generalizable to any more effective vaccine.
Conclusions
Even moderate increases in efficacy of influenza vaccines from mRNA or other novel platforms could have a large impact on influenza burden. Further benefits would occur if such vaccines also caused a decrease in infectivity and/or decrease in infectious period.
Funding
This work was supported by the Center for Disease Control and Prevention U01-IP001141-01.
The University of Pittsburgh Institutional Review Board has determined that this study was not human subject research and is therefore exempt study design.
The study sponsor had no role in study design; collection, analysis, and interpretation of data; writing the report; or the decision to submit the report for publication.
Financial disclosure
Drs. Zimmerman and Raviotta have research grant funding from Sanofi Pasteur on an unrelated vaccine topic. Dr John Williams serves on the Scientific Advisory Board of Quidel and an Independent Data Monitoring Committee for GlaxoSmithKline, neither involved in the present work.
Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mary G Krauland reports financial support was provided by Centers for Disease Control and Prevention. Richard K Zimmerman reports a relationship with Sanofi Pasteur that includes: funding grants. Jonathan M Raviotta reports a relationship with Sanofi Pasteur that includes: funding grants. John V. Williams reports a relationship with Quidel Corp that includes: board membership. John V. Williams reports a relationship with GlaxoSmithKline that includes: consulting or advisory.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jvacx.2022.100249.
Appendix A. Supplementary material
The following are the Supplementary data to this article:
Data availability
Data will be made available on request.
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Data Availability Statement
Data will be made available on request.