Cost-benefit analysis of swine influenza a vaccination in wean-to-finish production setting in the United States

March 26, 2026

Abstract

Swine influenza virus A (SIV-A) is endemic in hog farms globally, causing significant economic losses to the swine industry. While vaccination is a recommended control strategy, its economic value in specific production phases remains under-evaluated. This study investigated the economic viability of SIV-A vaccination in a U.S. wean-to-finish commercial farm using a stochastic epidemic model. A cost-benefit analysis was performed to estimate the Benefit-Cost Ratio (BCR), Net Present Value (NPV), and Return on Investment (ROI) of swine vaccination. In the absence of vaccination, the model predicted a high within-farm attack rate of approximately 82.5 % (95 % Confidence Interval (CI): 81 % – 0.84 %). Economic analysis showed that the profitability of SIV-A vaccination was highly dependent on vaccine efficacy. Vaccine efficacy below 70 % was shown to be not economically viable, yielding negative NPV and BCR below 1. Conversely, vaccination with ≥ 80 % efficacy was profitable, with BCRs ranging from 1.54 (95 % CI: 1.538, 1.54) to 4.09 (95 % CI: 4.082, 4.092) and net profits varying from US$0.90 ( 95 % CI: US$0.79, US$1.02) per pig up to US$3.41 (95 %CI: US$3.40, US$3.41) per pig. Vaccination against SIV-A in wean-to-finish settings was shown to be an economically favorable intervention when the vaccine is highly efficacious against the circulating SIV-A subtype. Sensitivity analysis indicated that vaccine efficacy, the cost of vaccination, and influenza-induced mortality rates were the most critical drivers determining the economic success of the program.

Keywords

Swine Influenza A

Cost-Benefit Analysis

Cost-Benefit Ratio

Vaccination

Pig

Wean-to-Finish farm

Mathematical Modeling

1. Introduction

Swine influenza virus A (SIV-A) is a respiratory virus belonging to the Orthomyxoviridae family with several subtypes causing infections in commercial swine: H1N1, H1N2, H3N2, H2N1, H3N1, H2N3, and H4N6 (Brookes et al., 2010, Brown, 2013, Brown, 2000, Janzen et al., 2025, Le Sage et al., 2024, Mancera Gracia et al., 2020). Three of these subtypes – H1N1, H1N2, and H3N2 – are endemic on hog farms globally, posing significant economic and health challenges to the industry (Brown, 2013, Corzo et al., 2013, Gumbert et al., 2020, Moraes et al., 2023, Pittman Ratterree et al., 2024, Song et al., 2010). These SIV-A subtypes are highly contagious, primarily spreading through droplets, direct contact among swine, and fomites (CDC, 2024, Le Sage et al., 2024). While these viruses cause high morbidity but low mortality in pigs, their pathogenicity can vary with subtypes (Lyoo et al., 2014, Ma, 2020) For example, H3N2 often induces acute lung lesions, whereas H1N1 can result in more prolonged infection with substantial pulmonary damage (Lyoo et al., 2014). Clinical signs of swine influenza include nasal discharge, lethargy, fever, reduced appetite, and coughing (Gumbert et al., 2020, Torremorell et al., 2012, Vincent et al., 2008). Influenza infections lead to reduced weight gain in fattening pigs and reproductive issues in sows, like higher abortion rate and return to estrus (Almeida et al., 2017, Er et al., 2016, Er et al., 2014, Er, 2021, Gumbert et al., 2020, VanderWaal and Deen, 2018).

Herd infections with SIV-A impact all ages of swine. In sows, SIV-A infection is associated with higher rates of abortions and stillbirths as well as fewer piglets born (Almeida et al., 2017, Gumbert et al., 2020, Salvesen and Whitelaw, 2021). SIV-A co-infection with other pathogens such as porcine reproductive and respiratory syndrome virus (PRRSV), Porcine Circovirus type 2, and Mycoplasma hyopneumoniae (Haden et al., 2012, Ouyang et al., 2019, Schultz-Cherry et al., 2013) may exacerbate SIV-A-related clinical disease and substantially increase mortality rate.

Vaccination is the primary prophylactic strategy for controlling SIV-A. Sow vaccination may be a very effective control strategy because it could reduce the negative effects influenza has on reproduction, such as fewer abortions and stillbirths, with the added benefit of some immunity being transferred to piglets via maternally derived antibodies (MDA) (Gumbert et al., 2020, Sandbulte et al., 2015). Commercially available vaccines such as the Respiporc Flu range (e.g., Respiporc Flu 3 and Respiporc Flu Pan H1N1) and Flusure XP are inactivated influenza vaccines designed to protect pigs against major SIV-A subtypes including H1N1, H3N2, and H1N2 (Detmer et al., 2013, FluSure XP Product Sheet [WWW Document], n.d, Parys et al., 2022, Ryt-Hansen et al., 2022). These vaccines work by stimulating systemic immunity, reducing clinical signs and viral lung load, with immunity typically lasting 4–6 months (Romagosa et al., 2011, Ryt-Hansen et al., 2022). When available commercial vaccines are not adequate at controlling the Influenza A strain circulating in a herd, an autogenous vaccine can be used as a substitute (Sandbulte et al., 2015). Autogenous vaccines are farm-specific, custom-made types of vaccine created from pathogens isolated from a given herd (Sandbulte et al., 2015, USDA-APHIS, 2021). They differ from commercially available vaccines because the viruses used to manufacture the vaccine come from the farm ordering the custom-made vaccine (Sandbulte et al., 2015, USDA-APHIS, 2021). In contrast, live-attenuated influenza vaccines (LAIVs) are engineered to replicate in a limited way, inducing strong mucosal immune responses in the respiratory tract (Rajao et al., 2023). This mechanism helps block early infection and transmission and has been shown to protect against viral replication and spread (Rajao et al., 2023). Recent strategies also explore two-step vaccination approaches, combining intramuscular viral vector vaccines with intranasal live-attenuated sprays, which enhance both systemic and local immunity (Avanthay et al., 2024). Vaccine administration methods vary with vaccine type; with inactivated vaccines typically administered intramuscularly, while LAIVs are delivered intranasally. These vaccines have been shown to be effective at reducing disease severity and viral shedding, though their efficacy can vary depending on matching the circulating influenza strain (Allerson et al., 2013, Romagosa et al., 2011, Vincent et al., 2010). In a survey of Midwestern veterinarians, the majority indicated the use of vaccination in breeding herds and gilt development (Moraes et al., 2023). Among the herds overseen by the midwestern veterinarians, commercial vaccines were predominant (84.8 %), while autogenous vaccines were employed by 50.0 %.

There are limited peer-reviewed studies that investigate the economic impact of influenza as the primary infection (Calderón Díaz et al., 2020, Moraes et al., 2023). Economic analysis of SIV-A and vaccination with the Teasgasc Pig Production Model revealed that the mean annual net profit for a farm without SIV-A was €356,869 ± 71,006 (90 % Confidence Interval: €243,581–€476,810) (Calderón Díaz et al., 2020). It showed that vaccinated and unvaccinated farms infected with SIV-A experienced a decrease in net profit of 36.7 % and 12.8 % compared to farms without SIV-A (Calderón Díaz et al., 2020). Regarding specific cost estimates, 68.4 % of Midwestern United States veterinarians placed the burden between US$1 and US$5 per market hog, with 45.7 % indicating that their clients were only somewhat concerned about the virus compared to other levels of concern (Moraes et al., 2023). Previous studies have used Cost-Benefit analysis to investigate the economic viability of vaccination for the control of pigs’ respiratory diseases such as Porcine Reproductive and Respiratory Syndrome (Moura et al., 2022, Zhang et al., 2014).

Several mathematical models have been developed to investigate the spread of SIV-A within and between farms (Pittman Ratterree et al., 2024). These models were primarily structured to mimic the farrow-to-finish farm settings (Andraud et al., 2023, Cador et al., 2017, Cador et al., 2016b, Etbaigha et al., 2018, Pitzer et al., 2016, Reynolds et al., 2014, Romagosa et al., 2011, Toft et al., 2005, White et al., 2017) using an SEIR (Susceptible, Exposed, Infected, Recovered) metapopulation model to simulate the spread of SIV-A within farms. These studies investigated the impact of vaccination on SIV-A transmission (Andraud et al., 2023, Reynolds et al., 2014, Romagosa et al., 2011, Toft et al., 2005, White et al., 2017), the role of maternally driven immunity on Influenza transmission (Cador et al., 2016a, Pitzer et al., 2016), and evaluated the risk of emergence of reassortment viruses from swine influenza viruses (Cador et al., 2017), among others.

The objective of this study was to investigate the economic viability of SIV-A vaccination for a wean-to-finish commercial farm in the United States. This aims to inform whether allocating resources towards vaccinating pigs against SIV-A in a wean-to-finish commercial farm would generate a positive net benefit. To achieve this objective, we developed a mathematical model to estimate the epidemiological and economic impact of SIV-A vaccination in a wean-to-finish commercial farm. We used the model to calculate SIV-A vaccination benefit-cost ratio and Net Present Value under uncertain disease transmission parameters and disease-induced costs. The study identified conditions under which it is economically favorable to vaccinate pigs against SIV-A in wean-to-finish commercial farms in the United States.

2. Methods

2.1. Epidemiological model

We used a stochastic τ-leap SEIR epidemic model to model the transmission of a generic SIV-A subtype among pigs in a wean-to-finish commercial farm. We defined a commercial farm as a large-scale, business-oriented swine production operation, typically using modern production systems and strict biosecurity protocols (Haley, 2025, USDA-APHIS, 2024). Such a farm generally houses thousands of pigs at a time (Cornelison et al., 2018). Here, a generic Influenza A subtype refers to any of the three endemic SIV-A subtypes H1N1, H3N2, and H1N2.

The model approximated transmission and progression by advancing time in small

increments (τ), modeling the number of transitions for infection (S to E), incubation (E

to I), and recovery (I to R) as Poisson random variables:

Δ N_{i}

∼ Poisson (

λ_{i} τ

). The state variables S, E, I, R are updated at each step (

t + τ

) based on the following:S(t +

τ

) = S(t) –

Δ N_{SE}

E(t +

τ

) = E(t) +

Δ N_{SE}

–

Δ N_{EI}

I(t +

τ

) = I(t) +

Δ N_{EI}

–

Δ N_{IR}

R(t +

τ

) = R(t) +

Δ N_{IR}

Where the rates of the events are defined as:

Δ N_{SE} = Poisson (\frac{β SI}{N} * τ)

Δ N_{EI} = Poisson (σ E * τ)

Δ N_{IR} = Poisson (γ I * τ)

Where the variables

S

E

I

R

represent the number of susceptible, exposed, infectious, and recovered pigs, respectively. β is the per-day transmission rate, N is the number of swine, V is vaccine efficacy, 1/σ is the average latent period in days, 1/γ is the average infection duration in days, and τ is the timestep. The parameter β is calculated as the product of the basic reproduction number (R₀) and the recovery rate (M May, 1991). R₀ is defined as the average number of new infections generated by a single infectious pig during its infectious period in an otherwise susceptible herd (M May, 1991). Epidemiological parameter values are presented in Table 1. In this model formulation, we assume that all pigs were vaccinated when entering the farm facility. We assumed that the vaccine only elicits partial protection against infection in the vaccinated host (Andraud et al., 2023, Fiatsonu et al., 2024). The impact of vaccination was included in our model by modifying the transmission event of the vaccinated host as follows:

Δ N_{SE} = Poisson (\frac{β SI}{N} (1 - V) * τ)

where

V

is the vaccine efficacy.

Table 1. Epidemiological model parameters for SIV-A.

Symbol	Description	Baseline value	Range*	Source
N	Average number of pigs in a wean-to-finish commercial farm	2500		(Cornelison et al., 2018)
R₀	Basic reproduction number	6	2.5–10.66	(Allerson et al., 2013, Romagosa et al., 2011, Rose et al., 2013)
β	Transmission rate (per day)			(Keeling and Rohani, 2007, M May, 1991)
V	Vaccine efficacy	varied	60 % – 100 %	(Allerson et al., 2013, Romagosa et al., 2011, Vincent et al., 2010)
1/σ	Latency period (days)	2	1.4–5.0	(Romagosa et al., 2012, Rose et al., 2013)
1/γ	Infection duration (days)	5	2.4–10.4	(Romagosa et al., 2012, Rose et al., 2013)

*: For the uncertainty and sensitivity analysis, we used uniform distributions across parameter range values to account for the impact of parameter variability on the robustness of our model outcomes. The uncertainty analysis provides information on the mean and 95 % confidence interval values of the model outcome.

Based on laboratory studies on the efficacy of SIV-A vaccines, the efficacy of heterologous vaccination is 62.5 % (Romagosa et al., 2011) and the efficacy of homologous vaccine estimates range from 95 % to 100 % (Allerson et al., 2013, Romagosa et al., 2011, Vincent et al., 2010). A heterologous SIV-A vaccine is a vaccine containing related but antigenically different strain(s) from the strain(s) circulating in a herd, whereas a homologous vaccine contains strain(s) that closely match the circulating strain(s) (Allerson et al., 2013, Romagosa et al., 2011, Vincent et al., 2010). We tested a range of efficacies from 60 % to 95 % to encompass the observed literature values. We specifically considered 5 vaccine efficacy values: 60 %, 70 %, 80 %, 90 %, and 95 %.

Due to the structure of this compartmental model, one of the underlying assumptions was a homogeneously mixing population, which means that every pig has an equal probability of interaction with any other pig in the population. For simplicity, the model assumes no re-infection; individual pigs experienced a single infection during the course of the outbreak. Additionally, for simplicity, the model did not account for natural mortality. The outbreak was simulated by running our model until no infectious individuals remained. Given the low mortality rate of SIV-A infection, influenza-induced mortality was explicitly included in our benefit-cost analysis as it is anticipated to have a minimal impact on within-farm disease transmission. After running the model simulation, unique combinations of vaccine efficacy and cumulative infection counts were identified.

2.2. Economic Model

2.2.1. Benefit-cost analysis

Cost-Benefit Analysis (CBA) involves systematically listing the pros (benefits) and cons (costs) that occur over time, assigning a monetary value to each element of benefits and costs, and then comparing the present values of benefits to the present values of costs (Boardman et al., 2018, Moura et al., 2022, Snell, 2010, Zhang et al., 2014). A status quo policy of not vaccinating swine during this stage of production was compared to a vaccine strategy. The resulting benefits included averted costs related to two key areas: disease-induced reduction of feed efficiency and disease-induced mortality (Alvarez et al., 2015, Er et al., 2016, Er et al., 2014). SIV-A infection among pigs has been shown to result in reduced feed conversion ratios (ratio of feed intake per weight gain) requiring increased feed intake to achieve the desired swine market weight (Er et al., 2016, Er et al., 2014). The reduction in feed conversion ratio is referred to as a reduction in feed efficiency (Er et al., 2016, Er et al., 2014). Influenza-induced mortality was calculated by multiplying the total number of infected pigs by the influenza-induced mortality rate (Table 2). The cost of the intervention was the cost of vaccinating all pigs in the wean-to-finish farm. Discounting benefits and costs to their present values was not necessary because all benefits and costs accrued within a single year.

Table 2. Costs associated with influenza infection in swine from the literature.

Parameter	Average	Range	Source
Feed penalty	4.93 kg	2.3–9.8 kg	(Er et al., 2016, Er et al., 2014, Er, 2024)
Feed cost (US$) per kilogram	$0.29	$0.19 – $0.39	(Cornelison et al., 2018)
Per head cost of raising pigs (US$)	$174.36	$152.24 – $203.50	(Estimated Livestock Returns [WWW Document], 2025)
Influenza-induced mortality	2.2 %	1.5–3.30 %	(Papatsiros et al., 2023, Schaefer et al., 2015)
Per-head lost profit (US$)	$12.86	$3.35-$29.26	(Estimated Livestock Returns [WWW Document], 2025)
Per-head carcass disposal (US$)	$9.07	$7.34–10.66	(Estimated Livestock Returns [WWW Document], 2025)

*The use of a uniform distribution ensures the full range of plausible parameter values was explored, allowing an unbiased assessment of the model’s sensitivity to each parameter, given that we had no prior information on the likely distribution of the parameter values (Briggs et al., 2012).

Three metrics were implemented to assess the financial viability of vaccination for swine influenza A: Benefit-Cost ratio (BCR), Net Present Value (NPV), and Return on Investment (ROI). BCR is a metric used to quantify the economic viability of an intervention (Boardman et al., 2018, Moura et al., 2022, Snell, 2010). The BCR was calculated as the ratio of the benefits occurring from the intervention (costs averted from reduced disease-associated mortality and feed intake) divided by the cost of the intervention (total costs of vaccinating the herd) (Moura et al., 2022, Zhang et al., 2014). If the BCR is greater than 1, the benefits of vaccination exceed the cost, and the intervention is likely economically profitable. If the BCR equals 1, vaccination is considered to have no net impact on profitability. If the BCR is less than 1, vaccination is likely profit-reducing. NPV is a metric used to determine whether the cost of implementing an intervention is higher than the benefit produced by the intervention (Boardman et al., 2018, Snell, 2010). The NPV was calculated as the difference between the benefits of vaccination (costs averted from reduced disease-associated mortality and feed intake) minus the total cost of implementing vaccination. If NPV is positive, vaccination would likely increase profitability. ROI is a metric used to evaluate the profitability of an intervention by comparing the net benefits relative to the cost (Boardman et al., 2018, Snell, 2010). ROI is the percentage benefit return from vaccination computed at the NPV divided by the total cost of implementing vaccination multiplied by 100. ROI results are interpreted as follows: if ROI is 10 %, then a US$1 investment will result in a US$ 1.10 return (Plastina, 2024).

2.2.2. Cost information

The cost of feed was adapted from a previous study, which estimated it to be $0.22 per kg in 2017 (Cornelison et al., 2018). Using the Consumer Price Index inflation calculator from the United States Bureau of Labor Statistics, the cumulative inflation rate from 2017 to 2025 was 32 %, which equated to a USD$0.29 cost of hog feed per kg (CPI Inflation Calculator [WWW Document], 2025). Due to uncertainty around the cost of feed, we considered a range of feed prices using a uniform distribution ranging + /- $0.10 based on the daily grain bin reports from Texas (Report-Texas Daily Grain Bids [WWW Document], 2025). Influenza-induced mortality cost was defined as the revenue lost from the death of the pig. Influenza-induced mortality cost was calculated by multiplying the total number of deaths by the per-head cost to raise a pig, per-head lost profit, and per-head carcass disposal (Estimated Livestock Returns [WWW Document], 2025, Papatsiros et al., 2023, Schaefer et al., 2015).

Vaccination cost was defined as the entire cost of administering the vaccine, including labor and the dose of the vaccine. The vaccine cost per dose was considered to vary from US$1.20 to US$2.00 (Flusure XP Swine Vaccine [WWW Document], 2025) and vaccine administration cost scenarios varying from US$0.14 (Vaccination administration via gel saves time, labor and money [WWW Document], 2025) to US$0.70 (DeGraves and Fetrow, 1991, Mayo, 2024). This resulted in a total vaccination cost per dose varying from US$1.34 to US$2.70. We considered 5 vaccine cost scenarios: US$1.34, US$1.68, US$2.02, US$2.36, and US$2.70.

2.2.3. Uncertainty analysis

A Monte Carlo simulation was used to evaluate the economic impact of influenza vaccination in swine under uncertainty, incorporating variability in key input epidemiological and economic parameters (Table 1 & 2). Monte Carlo simulation is a computational method that uses repeated random sampling to estimate the likelihood of different outcomes in a process that involves uncertainty (Karsten et al., 2005). This method has been widely used in modeling studies (Fast et al., 2015, Horst et al., 1999, Karsten et al., 2005, Lothan et al., 2022, McKinnell et al., 2015). For example, in Fast et al. and McKinnel et al. (Fast et al., 2015, McKinnell et al., 2015), Monte Carlo simulation was used to explore uncertainty in outbreak dynamics and intervention costs. In Karsten et al., a Monte Carlo simulation was used to explore uncertainty in the risk of classical swine fever transmission between farms in a geographic region and the impact of preventive measures like preemptive slaughter and controlled movement. In our study, we ran 10,000 simulations for each vaccination cost to capture the range of possible outcomes. Uncertainty analysis results were presented as means and 95 % credible intervals of the possible outcomes.

2.2.4. Sensitivity analysis

A one-way sensitivity analysis was conducted to assess the influence of the economic parameters (Table 2) on our BCR results. To enable direct comparison of effect sizes across parameters measured on different scales, a standardized linear regression approach was applied. The response variable was BCR and the explanatory variables were: vaccine efficacy, vaccination cost, feed penalty, influenza-induced mortality rate, cost to raise a pig, profit lost from death, carcass disposal, and feed cost per kg. The coefficients of the explanatory variables generated by the linear regression represented the relative influence of each variable on BCR. These standardized effects were visualized using a tornado plot, which ranks variables by their impact on BCR variability (Boardman et al., 2018, Carnell, 2025, Carnell, 2022). This sensitivity analysis provides insight into which parameters most strongly influence the economic feasibility of vaccination.

2.2.5. Simulation

The scripts for this project were developed in RStudio using R (version: 2024.9.0.375, Boston, Massachusetts: Posit Software; version 4.5.0, Vienna, Austria: R Core Team). The stochastic epidemiological model was implemented using the adaptivtau, which uses an algorithm for adaptive step sizes to maximize efficiency and accuracy of the tau-leap model (Johnson, 2010). The Data visualization for the simulation results was also performed with the ggplot2 (Wickham et al., 2007), tornado (Carnell, 2022) and patchwork packages (Pedersen, 2019).

3. Results

3.1. Epidemiological model

To perform our baseline analysis, we used the baseline values provided in Table 1 and ran 1000 simulations of our model to capture the range of possible outcome realizations. In the absence of vaccination, an SIV-A outbreak in a wean-to-finish commercial farm resulted in an average attack rate of 82.52 % (95 % CI: 81.35 %, 84.30 %) over the course of the outbreak. In the presence of vaccination, a SIV-A outbreak in a wean-to-finish commercial farm resulted in an attack rate of 50.69 % (95 % CI:48.98 %, 52.40 %), 31.27 % (95 % CI: 29.85 %, 32.69 %), 4.71 % (95 % CI: 4.29 %, 5.14 %), 0.10 % (95 % CI: 0.10 %, 0.11 %), and 0.06 % (95 % CI: 0.053, 0.057) over the course of the outbreak for 60 %, 70 %, 80 %, 90 %, and 95 % vaccine efficacy, respectively (Table 3). The epidemiological curve for the course of infection is presented in Fig. 1.

Table 3. Average attack rate and 95 % confidence interval of the stochastic simulations of the epidemiological model.

Vaccines Efficacy	Mean	95 % Confidence Interval
60 %	50.69 %	(95 % CI:48.98 %, 52.40 %)
50 %	31.27 %	(95 % CI: 29.85 %, 32.69 %)
70 %	4.71 %	(95 % CI: 4.29 %, 5.14 %)
80 %	0.10 %	(95 % CI: 0.10 %, 0.11 %)
90 %	0.06 %	(95 % CI: 0.053, 0.057)

3.2. Economic evaluation

For vaccine efficacies below 80 %, vaccination yielded more costs than benefits for all vaccine costs considered. This was marked by negative NPV values and BCR below 1 (Table 4 & Table 5). For 80 % vaccine efficacy, vaccination resulted in an NPV per pig varying from US$0.90 (95 % CI: US$0.79, US$1.02) to US$2.26 (95 % CI: US$2.15, US$2.38) and an BCR value between 3.10 (95 % CI: 3.101, 3.103) and 1.54 (95 % CI: 1.538, 1.54), as vaccination cost varies from US$2.70 per pig to US$1.34 per pig (Table 3 & S1). For a highly efficacious vaccine, with a 95 % efficacy, vaccination resulted in an NPV from US$2.05 (95 %CI: US$2.04, US$2.05) to US$3.41 (95 %CI: US$3.40, US$3.41) and a BCR value between 4.09 (95 % CI: 4.082, 4.092) and 2.03 (95 % CI: 2.026, 2.031) (Table 4 & Table 5).

Table 4. Net Present Value per pig in USD$ by vaccine efficacy and total cost of vaccination per pig. Values are presented as mean and 95 % Confidence Interval.

Empty Cell	Total vaccination cost per pig
Efficacy	$1.34	$1.68	$2.02	$2.36	$2.70
60 %	-1.26 (-1.48, −1.04)	-1.60 (-1.82, −1.38)	-1.94 (-2.16, −1.72)	-2.28 (-2.50, −2.06)	-2.62 (-2.84, −2.40)
70 %	-0.41 (-0.57, −0.25)	-0.75 (-0.91, –0.59)	-1.09 (-1.25, −0.93)	-1.43 (-1.59, −1.27)	-1.77 (-1.93, −1.61)
80 %	2.26 (2.15, 2.38)	1.92 (1.81, 2.04)	1.58 (1.47, 1.70)	1.24 (1.13, 1.36)	0.90 (0.79, 1.02)
90 %	3.39 (3.38, 3.40)	3.05 (3.04, 3.06)	2.71 (2.70, 2.72)	2.37 (2.36, 2.38)	2.03 (2.02, 2.04)
95 %	3.41 (3.40, 3.41)	3.07 (3.06, 3.07)	2.73 (2.72, 2.73)	2.39 (2.38, 2.39)	2.05 (2.04, 2.05)

Table 5. Benefit Cost Ratio by vaccine efficacy and total cost of vaccination per pig. Values are presented as mean and 95 % Confidence Interval for the Monte Carlo Simulation.

Empty Cell	Total vaccination cost per pig
Efficacy	$1.34	$1.68	$2.02	$2.36	$2.70
60 %	0.069 (0.067, 0.071)	0.055 (0.053, 0.056)	0.046 (0.044, 0.047)	0.039 (0.038, 0.040)	0.034 (0.033, 0.035)
70 %	0.800 (0.799, 0.082)	0.638 (0.637,0.639)	0.531 (0.530, 0.532)	0.454 (0.453, 0.455)	0.397 (0.396, 0.398)
80 %	3.102 (3.101, 3.103)	2.474 (2.473, 2.475)	2.057 (2.056, 2.058)	1.761 (1.761, 1.762)	1.539 (1.538, 1.539)
90 %	4.068 (4.065, 4.071)	3.247 (3.245, 3.250)	2.701 (2.699, 2.703)	2.310 (2.308, 2.311)	2.020 (2.018, 2.021)
95 %	4.087 (4.082, 4.092)	3.257 (3.252, 3.261)	2.711 (2.708, 2.715)	2.319 (2.316, 2.322)	2.028 (2.026, 2.031)

To assess the robustness of the BCR results under parameter uncertainty, we generated heatmaps summarizing the number of Monte Carlo simulations in which the BCR was greater than 1 and the average BCR values (Table 5 & Fig. 2). The heatmaps display combinations of vaccine efficacy on the x-axis and vaccine cost per pig on the y-axis. BCR values were shown to be always less than 1 when vaccine efficacy is less than or equal to 70 %, and always greater than 1 with an average value above 2 when vaccine efficacy was greater than or equal to 90 % (Fig. 2A). For a vaccine efficacy of 80 %, the average BCR value was shown to vary from 1.54 to 3.10 as the vaccination cost varies from US$2.70 per pig to US$1.34 per pig (Fig. 2B & Table 5).

In terms of return on investment (ROI), 99.26 % of simulations with BCR > 1 had an ROI greater than 10 %. The percentage of simulations where the ROI was greater than 5 % for a vaccine efficacy of 60 % and 70 % were 7.48 % and 8.92 %, respectively. For a vaccine efficacy of 80 %, the percentage of simulations where ROI was greater than 10 % was shown to vary from 99.97 % to 81.08 % as the vaccination cost varies from US$2.70 per pig to US$1.34 per pig

3.3. Sensitivity analysis

A tornado plot was used to assess the sensitivity of the BCR to uncertainty in input parameters (Fig. 3). Based on the length of the bar, vaccine efficacy had the most influence on the BCR standardized effect range of −1.27 to + 2.38 relative to the baseline BCR. This suggested that small changes in this parameter are associated with comparatively larger changes in the BCR, holding other variables constant. Higher vaccination costs reduced the BCR, indicating diminished investment efficiency, whereas a higher influenza-induced mortality rate increased the BCR, reflecting greater economic benefits of vaccination under a higher disease burden. Conversely, lost profit from death and carcass disposal from influenza-induced mortality had minimal effects on BCR.

4. Discussion

In this study, we conducted a comprehensive cost-benefit analysis of SIV-A vaccination in a wean-to-finish commercial farm in the United States. Wean-to-finish commercial facilities are a major component of the hog production system in the United States. The larger share of pigs in the United States are found in commercial facilities, especially those focusing on the wean-to-finish hog production phase. These facilities account for more than 45 % of all pigs and 70 % of all finished pigs, while the remaining 55 % of pigs are distributed between farrow-to-finish, farrow-to-nursery, farrow-to-wean, and finishing-only farms (MetaFarms and National Pork Board, 2023, United States Department of Agriculture-National Agricultural Statistics Service, 2024). We, therefore, anticipate that our results may be applicable to many commercial farm facilities in the United States.

We demonstrated that in the absence of vaccination, the model predicted high within-farm attack rates, with more than 80 % of pigs being infected during an outbreak. For vaccine efficacies of 70 % and below, the model indicated that vaccination was generally not profitable as the BCR stayed below 1 and the NPV was negative. When the vaccine efficacy was 80 % or higher, the model showed that vaccination was economically viable as BCR was above 1 and the NPV was positive. In scenarios where the BCR was greater than 1, nearly all of the simulations showed an ROI greater than 10 %, meaning that any US$1 investment in SIV-A vaccination will generate a US$ 1.10 return. The sensitivity analysis highlighted vaccine efficacy, vaccination cost, and influenza-induced mortality rate as the primary drivers of economic outcomes, underscoring their importance in guiding future research and policy decisions.

Previous modeling studies have investigated the impact of SIV-A vaccination on disease transmission in facilities with breeding sows (Cador et al., 2017, Etbaigha et al., 2018, Kontowicz et al., 2023, White et al., 2017). Cador et al. evaluated the impact of three vaccination strategies on SIV-A transmission in farrow-to-finish farms: Batch-to-batch vaccination of the breeding sows, Mass vaccination of the breeding sows, and Batch-to-batch vaccination of the breeding sows and growing pigs. It showed that though batch-to-batch vaccination could reduce disease transmission in breeding sows, no vaccination strategies could eradicate SIV-A within a farrow-to-finish farm (Cador et al., 2017). As in Cador et al., Etbaigha et al. showed that vaccination cannot eradicate SIV-A in a finish-to-farrow farm (Etbaigha et al., 2018). White et al. and Kontowicz et al. showed that though vaccination cannot achieve eradication of SIV-A in farrow-to-finish farms, mass swine vaccination with a homologous (highly efficacious) vaccine can reduce SIV-A prevalence by half (Kontowicz et al., 2023, White et al., 2017). While these studies focused on SIV-A eradication and disease prevalence reduction within a farm, they did not investigate the potential economic profitability of swine vaccination against SIV-A infection. We extended the scope of these studies by performing an economic viability analysis which is paramount to informing decisions on implementing disease control measures in a farm setting (McInerney et al., 1992, Wolf and Wolf, 2005). Investigating the economic viability with epidemiological models is critical because biologically efficacious interventions may not be financially sustainable for producers. For example, Nathues et al. used a model of porcine reproduction and respiratory syndrome (PRRS) to evaluate the economic outcomes for interventions like mass vaccination or depopulation (Nathues et al., 2018). The results of the model indicated that the economic feasibility of interventions was not uniform across farm types (Nathues et al., 2018)

Our study only looked at the effects on growing phase swine and did not apply to the vaccination of sows in breeding herds. In endemically infected growing facilities, annual vaccination of hogs was recommended to improve animal welfare and growing efficiency (Sandbulte et al., 2015). The key to good vaccination programs for SIV-A was finding a close antigenic match, meaning there is high similarity between the vaccine’s inactivated SIV-A virus in the vaccine surface proteins and the local circulating strains (Sandbulte et al., 2015). A close antigenic match is difficult with commercially available whole inactivated virus (WIV) vaccines because of uneven geographic surveillance, multiple co-circulating antigenically distinct clusters of H1 and H3 viruses, and manufacturers selecting strains without publishing the HA sequence or routinely sharing (Mancera Gracia et al., 2020, Sandbulte et al., 2015). Autogenous vaccines have been proposed as a solution when commercially viable vaccines provide a poor antigenic match because they are formulated using the strain of the virus circulating in the herd (Mancera Gracia et al., 2020). The limitation of implementing autogenous vaccines is the lengthy production time required to match strains and produce the vaccines (Mancera Gracia et al., 2020). There is also a concern regarding vaccine-associated enhanced respiratory disease, which has been reported in pigs vaccinated with WIV vaccines and subsequently challenged with heterologous strains (Gauger et al., 2012, Mancera Gracia et al., 2020, Petro-Turnquist et al., 2024). These challenges underscored the need for continuous research and development into more effective vaccine platforms.

Our model has a number of limitations. First, the homogeneous mixing assumption of our compartmental SEIR model does not explicitly take into consideration the fact that pigs are generally housed in separate large barns, which may impact the spread of the pathogen within the farm. To address this limitation, a metapopulation or agent-based model may be more appropriate for mimicking the underlying disease transmission dynamics within and between pens in a farm. Second, our model did not consider a range of population sizes (number of pigs in a wean-to-finish commercial farm). However, Ma & Earn have shown that the final attack rate of an SEIR model with frequency-dependent transmission does not depend on the population size (Ma and Earn, 2006). Given that we used a frequency-dependent model, we anticipate that using different population sizes (number of pigs in a commercial wean-to-finish farm) would only have a marginal impact on our model’s outcomes such as attack rates, the NPV per pig, and the BCR. Third, our model did not account for natural mortality. This assumption was made for simplicity, as wide variation has been observed for natural pigs’ mortality rate across wean-to-finish farms in the US (Gebhardt et al., 2020, What and when: Deeper look at wean-to-finish mortality [WWW Document], 2024). This mortality is driven by several factors such as the non-infection-related health status of newly introduced wean pigs, anatomic abnormality, and animal management factors, among others (Gebhardt et al., 2020, What and when: Deeper look at wean-to-finish mortality [WWW Document], 2024). We anticipate that adding natural mortality to our analysis will not have any significant impact on the qualitative nature of our results. However, a future modeling study targeting a specific producer or production facility may explicitly include natural mortality to compute the absolute monetary benefits of SIV-A vaccination in those settings. Fourth, we modeled a generic SIV-A subtype because of limited subtype-specific epidemiological data required to consider a subtype-specific model. So, our model does not account for subtype-specific factors such as subtype-specific infectivity and pathogenicity. Further work on subtype-specific epidemiological parameters is needed to refine our analysis. These should ideally be subtype/strain-specific large-scale experimental studies, as previous experimental studies have generally only considered small sample sizes (Allerson et al., 2013, Romagosa et al., 2011, Rose et al., 2013). These studies should measure epidemiological parameters such as subtype-specific incubation period, infectious period, and transmission rates of different transmission routes, such as aerosol, fomites, and direct pig-to-pig contact. Finally, due to limited publicly available economic data on vaccine cost and bulk hog feed, we used uniform distributions to quantify uncertainties in parameter values. For each parameter, the upper and lower bounds of the uniform distribution were determined by the highest and lowest values identified in the literature.

Our study provides a conservative estimate of the economic viability of pig vaccination for controlling the transmission and burden of SIV-A infection in wean-to-finish commercial farms. This is primarily due to the fact that our model only accounts for the impact of vaccines on reducing the susceptibility of vaccinated pigs to infection. However, as it has been observed with other respiratory viruses such as PRRS, vaccines can also reduce the risk of disease-induced mortality and the impact of infection on feed conversion ratio for vaccinated pigs relative to unvaccinated (Augusto et al., 2020, Zhou et al., 2021). These factors, which can greatly improve the economic viability of a pig vaccine, were not included in our model for lack of empirical data on the impact of SIV-A vaccines on influenza-induced mortality and feed conversion ratio. Future work should include these factors as empirical data becomes available.

The results of our study are generalizable to large-scale wean-to-finish farms in the United States. This generalizability was established not only by the use of a representative/average farm size and focus on the wean-to-finish production phase, which is a common farm type in the United States swine industry, but also the use of a modeling framework whose attack rate results (primary epidemiological outcome of interest) are independent of the population size (Ma and Earn, 2006). By considering a wide range of values for the epidemiological and economic parameters, our analysis provided robust results that are applicable to various epidemiological settings (e.g. different SIV-A subtypes/strains) and swine production operations in the US. The modeling framework serves as a scalable decision-making tool for commercial operations to evaluate the financial impact of SIV-A vaccination on influenza-induced mortality and reduced feed efficacy.

This study showed that vaccination against SIV-A is economically profitable for wean-to-finish commercial farms in the United States, when the vaccine is highly efficacious against the circulating/dominating SIV-A subtype. The magnitude of the benefits derived from vaccination was shown to be most sensitive to vaccine efficacy, vaccination cost, and influenza-induced mortality rate. To estimate their expected actual economic benefits, commercial farmers should balance their perceived costs of SIV-A infection against vaccine efficacy. This study provides valuable insights into the economic viability and broader implications of vaccination as a single or part of combination strategies for SIV-A control in swine commercial facilities.

Funding

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This work is funded through a grant from the United States Department of Agriculture (APHIS USDA AP23OA000000C013) awarded to SD and MLNM and seed funding from Texas A&M AgriLife awarded to MLNM.

CRediT authorship contribution statement

Robert Ohsfeldt: Writing – review & editing, Writing – original draft, Validation, Methodology. Pittman Ratterree Dana Christine: Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Formal analysis. Ndeffo-Mbah Martial Loth: Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Methodology, Funding acquisition, Conceptualization. Sapna Chitlapilly Dass: Writing – review & editing, Funding acquisition, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Data availability

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author/s. The code can be found here: https://github.com/dana-pittman/SIV-A-Cost-Benefit-Analysis

References

Allerson et al., 2013

M. Allerson, J. Deen, S.E. Detmer, M.R. Gramer, H.S. Joo, A. Romagosa, M. Torremorell

The impact of maternally derived immunity on influenza A virus transmission in neonatal pig populations

Vaccine, 31 (2013), pp. 500-505, 10.1016/j.vaccine.2012.11.023

View PDF View article View in Scopus Google Scholar
Almeida et al., 2017

H.M. de S. Almeida, G.Y. Storino, D.A. Pereira, I.R.H. Gatto, L.A. Mathias, H.J. Montassier, L.G. de Oliveira

A cross-sectional study of swine influenza in intensive and extensive farms in the northeastern region of the state of São Paulo, Brazil

Trop. Anim. Health Prod., 49 (2017), pp. 25-30, 10.1007/s11250-016-1153-z

View in Scopus Google Scholar
Alvarez et al., 2015

J. Alvarez, J. Sarradell, B. Kerkaert, D. Bandyopadhyay, M. Torremorell, R. Morrison, A. Perez

Association of the presence of influenza A virus and porcine reproductive and respiratory syndrome virus in sow farms with post-weaning mortality

Prev. Vet. Med., 121 (2015), pp. 240-245, 10.1016/j.prevetmed.2015.07.003

View PDF View article View in Scopus Google Scholar
Andraud et al., 2023

M. Andraud, S. Hervé, S. Gorin, N. Barbier, S. Quéguiner, F. Paboeuf, G. Simon, N. Rose

Evaluation of early single dose vaccination on swine influenza A virus transmission in piglets: From experimental data to mechanistic modelling

Vaccine, 41 (2023), pp. 3119-3127, 10.1016/j.vaccine.2023.04.018

View PDF View article View in Scopus Google Scholar
Augusto et al., 2020

C. Augusto, A. Moura, D.C.L. Linhares, J. Holtkamp, A. Ramirez, A. O’connor, C. Wang

Effect of porcine reproductive and respiratory syndrome (PRRS) modified-live virus (MLV) interventions on growth performance of infected growing pigs under field conditions

Iowa State University (2020)

Google Scholar
Avanthay et al., 2024

R. Avanthay, O. Garcia-Nicolas, N. Ruggli, L. Grau-Roma, E. Párraga-Ros, A. Summerfield, G. Zimmer

Evaluation of a novel intramuscular prime/intranasal boost vaccination strategy against influenza in the pig model

PLoS Pathog., 20 (2024), Article e1012393, 10.1371/journal.ppat.1012393

View in Scopus Google Scholar
Boardman et al., 2018

A.E. Boardman, D.H. Greenberg, A.R. Vining, D.L. Weimer

Cost-benefit analysis (5th ed), Cambridge University Press, Cambridge, England (2018), 10.1017/9781108235594

Google Scholar
Briggs et al., 2012

A.H. Briggs, M.C. Weinstein, E.A.L. Fenwick, J. Karnon, M.J. Sculpher, A.D. Paltiel, ISPOR-SMDM Modeling Good Research Practices Task Force

Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–6

Value Health, 15 (2012), pp. 835-842, 10.1016/j.jval.2012.04.014

View PDF View article View in Scopus Google Scholar
Brookes et al., 2010

S.M. Brookes, A. Núñez, B. Choudhury, M. Matrosovich, S.C. Essen, D. Clifford, M.J. Slomka, G. Kuntz-Simon, F. Garcon, B. Nash, A. Hanna, P.M.H. Heegaard, S. Quéguiner, C. Chiapponi, M. Bublot, J.M. Garcia, R. Gardner, E. Foni, W. Loeffen, L. Larsen, K. Van Reeth, J. Banks, R.M. Irvine, I.H. Brown

Replication, pathogenesis and transmission of pandemic (H1N1) 2009 virus in non-immune pigs

PLoS One, 5 (2010), Article e9068, 10.1371/journal.pone.0009068

View in Scopus Google Scholar
Brown, 2000

I.H. Brown

The epidemiology and evolution of influenza viruses in pigs

Vet. Microbiol, 74 (2000), pp. 29-46, 10.1016/s0378-1135(00)00164-4

View PDF View article View in Scopus Google Scholar
Brown, 2013

I.H. Brown

History and Epidemiology of Swine Influenza in Europe

J.A. Richt, R.J. Webby (Eds.), Swine Influenza, Springer Berlin Heidelberg, Berlin, Heidelberg (2013), pp. 133-146, 10.1007/82_2011_194

View in Scopus Google Scholar
Cador et al., 2016a

C. Cador, S. Hervé, M. Andraud, S. Gorin, F. Paboeuf, N. Barbier, S. Quéguiner, C. Deblanc, G. Simon, N. Rose

Maternally-derived antibodies do not prevent transmission of swine influenza A virus between pigs

Vet. Res., 47 (2016), p. 86, 10.1186/s13567-016-0365-6

View in Scopus Google Scholar
Cador et al., 2016b

C. Cador, N. Rose, L. Willem, M. Andraud

Maternally Derived Immunity Extends Swine Influenza A Virus Persistence within Farrow-to-Finish Pig Farms: Insights from a Stochastic Event-Driven Metapopulation Model

PLoS One, 11 (2016), Article e0163672, 10.1371/journal.pone.0163672

View in Scopus Google Scholar
Cador et al., 2017

C. Cador, M. Andraud, L. Willem, N. Rose

Control of endemic swine flu persistence in farrow-to-finish pig farms: a stochastic metapopulation modeling assessment

Vet. Res., 48 (2017), p. 58, 10.1186/s13567-017-0462-1

View in Scopus Google Scholar
Calderón Díaz et al., 2020

J.A. Calderón Díaz, R.M. Fitzgerald, L. Shalloo, M. Rodrigues da Costa, J. Niemi, F.C. Leonard, I. Kyriazakis, E. García Manzanilla

Financial analysis of herd status and vaccination practices for porcine reproductive and respiratory syndrome virus, swine influenza virus, and Mycoplasma hyopneumoniae in farrow-to-finish pig farms using a bio-economic simulation model

Front. Vet. Sci., 7 (2020), Article 556674, 10.3389/fvets.2020.556674

View in Scopus Google Scholar
Carnell, 2022

R. Carnell

tornado: Plots for Model Sensitivity and Variable Importance

CRAN Contrib. Packages (2022), 10.32614/cran.package.tornado

Google Scholar
Carnell, 2025
Carnell, R., 2025. Plots for Model Sensitivity and Variable Importance [WWW Document]. URL 〈https://cran.r-project.org/web/packages/tornado/tornado.pdf〉 (accessed 1.27.26).

Google Scholar
CDC, 2024
CDC, 2024. About Influenza in Swine (Pigs) [WWW Document]. Swine / Variant Influenza. URL 〈https://www.cdc.gov/swine-flu/about/influenza-in-swine.html〉 (accessed 1.26.26).

Google Scholar
Cornelison et al., 2018

A.S. Cornelison, L.A. Karriker, N.H. Williams, B.J. Haberl, K.J. Stalder, L.L. Schulz, J.F. Patience

Impact of health challenges on pig growth performance, carcass characteristics, and net returns under commercial conditions

Transl. Anim. Sci., 2 (2018), pp. 50-61, 10.1093/tas/txx005

View in Scopus Google Scholar
Corzo et al., 2013

C.A. Corzo, M. Culhane, S. Dee, R.B. Morrison, M. Torremorell

Airborne detection and quantification of swine influenza a virus in air samples collected inside, outside and downwind from swine barns

PLoS One, 8 (2013), Article e71444, 10.1371/journal.pone.0071444

View in Scopus Google Scholar
CPI Inflation Calculator [WWW Document], 2025
CPI Inflation Calculator [WWW Document], 2025. Bureau of Labor Statistics. URL 〈https://www.bls.gov/data/inflation_calculator.htm〉 (accessed 6.16.25).

Google Scholar
DeGraves and Fetrow, 1991

F.J. DeGraves, J. Fetrow

Partial budget analysis of vaccinating dairy cattle against coliform mastitis with an Escherichia coli J5 vaccine

J. Am. Vet. Med. Assoc., 199 (1991), pp. 451-455

Crossref View in Scopus Google Scholar
Detmer et al., 2013

S.E. Detmer, M.R. Gramer, V.L. King, S. Mathur, V.J. Rapp-Gabrielson

In vivo evaluation of vaccine efficacy against challenge with a contemporary field isolate from the α cluster of H1N1 swine influenza virus

Can. J. Vet. Res., 77 (2013), pp. 24-32

View in Scopus Google Scholar
Er et al., 2014

C. Er, B. Lium, S. Tavornpanich, P.O. Hofmo, H. Forberg, A.G. Hauge, C.A. Grøntvedt, T. Framstad, E. Brun

Adverse effects of Influenza A(H1N1)pdm09 virus infection on growth performance of Norwegian pigs – a longitudinal study at a boar testing station

BMC Vet. Res., 10 (2014), p. 284, 10.1186/s12917-014-0284-6

View in Scopus Google Scholar
Er et al., 2016

C. Er, E. Skjerve, E. Brun, P.O. Hofmo, T. Framstad, B. Lium

Production impact of influenza A(H1N1)pdm09 virus infection on fattening pigs in Norway

J. Anim. Sci., 94 (2016), pp. 751-759, 10.2527/jas.2015-9251

View in Scopus Google Scholar
Er, 2021

J.C. Er

Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

Infect. Dis. Rep., 13 (2021), pp. 748-756, 10.3390/idr13030070

View in Scopus Google Scholar
Er, 2024

J.C. Er

A Norwegian observational study of feed conversion efficiency in Duroc and Landrace grower pigs seropositive for influenza A (H1N1) pdm09 virus

J. Swine Health Prod. (2024), 10.54846/jshap/1395

Google Scholar
Estimated Livestock Returns [WWW Document], 2025
Estimated Livestock Returns [WWW Document], 2025. Estimated Livestock Returns. URL 〈https://estimatedreturns.econ.iastate.edu/〉 (accessed 6.13.25).

Google Scholar
Etbaigha et al., 2018

F. Etbaigha, A. R Willms, Z. Poljak

An SEIR model of influenza A virus infection and reinfection within a farrow-to-finish swine farm

PLoS One, 13 (2018), Article e0202493, 10.1371/journal.pone.0202493

View in Scopus Google Scholar
Fast et al., 2015

S.M. Fast, M.C. González, N. Markuzon

Cost-effective control of infectious disease outbreaks accounting for societal reaction

PLoS One, 10 (2015), Article e0136059, 10.1371/journal.pone.0136059

View in Scopus Google Scholar
Fiatsonu et al., 2024

E. Fiatsonu, A. Deka, M.L. Ndeffo-Mbah

Assessing the impact of a potential canine vaccine for the control of Chagas disease: a mathematical modeling study

Front. Trop. Dis., 5 (2024), 10.3389/fitd.2024.1421669

Google Scholar
FluSure XP Product Sheet [WWW Document], n.d
FluSure XP Product Sheet [WWW Document], n.d. URL 〈https://www.zoetisus.com/content/_assets/docs/vmips/package-inserts/flusure-xp-product-information.pdf〉 (accessed 11.28.16).

Google Scholar
Flusure XP Swine Vaccine [WWW Document], 2025
Flusure XP Swine Vaccine [WWW Document], 2025. URL 〈https://pipevet.com/flusure-xp-swine-vaccine〉 (accessed 12.23.25).

Google Scholar
Gauger et al., 2012

P.C. Gauger, A.L. Vincent, C.L. Loving, J.N. Henningson, K.M. Lager, B.H. Janke, M.E. Kehrli, Jr, J.A. Roth

Kinetics of lung lesion development and pro-inflammatory cytokine response in pigs with vaccine-associated enhanced respiratory disease induced by challenge with pandemic (2009) A/H1N1 influenza virus

Vet. Pathol., 49 (2012), pp. 900-912, 10.1177/0300985812439724

View in Scopus Google Scholar
Gebhardt et al., 2020

J.T. Gebhardt, M.D. Tokach, S.S. Dritz, J.M. DeRouchey, J.C. Woodworth, R.D. Goodband, S.C. Henry

Postweaning mortality in commercial swine production. I: review of non-infectious contributing factors

Transl. Anim. Sci., 4 (2020), Article txaa068, 10.1093/tas/txaa068

Google Scholar
Gumbert et al., 2020

S. Gumbert, S. Froehlich, A. Rieger, J. Stadler, M. Ritzmann, S. Zoels

Reproductive performance of pandemic influenza A virus infected sow herds before and after implementation of a vaccine against the influenza A (H1N1)pdm09 virus

Porc. Health Manag, 6 (2020), p. 4, 10.1186/s40813-019-0141-x

View in Scopus Google Scholar
Haden et al., 2012

C. Haden, T. Painter, T. Fangman, D. Holtkamp

Assessing production parameters and economic impact of swine influenza, PRRS and Mycoplasma hyopneumoniae on finishing pigs in a large production system

I.A. Perry (Ed.), Presented at the American Association of Swine Veterinarians Annual Meeting (2012), pp. 75-76

Google Scholar
Haley, 2025
Haley, M., 2025. Hogs & Pork – Sector at a Glance [WWW Document]. URL 〈https://www.ers.usda.gov/topics/animal-products/hogs-pork/sector-at-a-glance〉 (accessed 1.27.26).

Google Scholar
Horst et al., 1999

H.S. Horst, A.A. Dijkhuizen, R.B. Huirne, M.P. Meuwissen

Monte Carlo simulation of virus introduction into the Netherlands

Prev. Vet. Med., 41 (1999), pp. 209-229, 10.1016/s0167-5877(99)00038-0

View PDF View article View in Scopus Google Scholar
Janzen et al., 2025

G.M. Janzen, B.T. Inderski, J. Chang, Z.W. Arendsee, A. Janas-Martindale, M.K. Torchetti, A.L. Baker, T.K. Anderson

Sources and sinks of influenza A virus genomic diversity in swine from 2009 to 2022 in the United States

J. Virol., 99 (2025), Article e0054125, 10.1128/jvi.00541-25

View in Scopus Google Scholar
Johnson, 2010

P. Johnson

adaptivetau: Tau-Leaping Stochastic Simulation

CRAN Contrib. Packages (2010), 10.32614/cran.package.adaptivetau

Google Scholar
Karsten et al., 2005

S. Karsten, G. Rave, J. Krieter

Monte Carlo simulation of classical swine fever epidemics and control

I. Gen. Concepts Descr. Model. Vet. Microbiol, 108 (2005), pp. 187-198, 10.1016/j.vetmic.2005.04.009

View PDF View article View in Scopus Google Scholar
Keeling and Rohani, 2007

M.J. Keeling, P. Rohani

Modeling infectious diseases in humans and animals

Princeton University Press, Princeton, NJ (2007)

Google Scholar
Kontowicz et al., 2023

E. Kontowicz, M. Moreno-Madriñan, D. Ragland, W. Beauvais

A stochastic compartmental model to simulate intra- and inter-species influenza transmission in an indoor swine farm

PLoS One, 18 (2023), Article e0278495, 10.1371/journal.pone.0278495

View in Scopus Google Scholar
Le Sage et al., 2024

V. Le Sage, N.C. Rockey, A.J. French, R. McBride, K.R. McCarthy, L.H. Rigatti, M.J. Shephard, J.E. Jones, S.G. Walter, J.D. Doyle, L. Xu, D.J. Barbeau, S. Wang, S.A. Frizzell, M.M. Myerburg, J.C. Paulson, A.K. McElroy, T.K. Anderson, A.L. Vincent Baker, S.S. Lakdawala

Potential pandemic risk of circulating swine H1N2 influenza viruses

Nat. Commun., 15 (2024), p. 5025, 10.1038/s41467-024-49117-z

View in Scopus Google Scholar
Lothan et al., 2022

R. Lothan, N. Gutman, D. Yamin

Country versus pharmaceutical company interests for hepatitis C treatment

Health Care Manag. Sci., 25 (2022), pp. 725-749, 10.1007/s10729-022-09607-2

View in Scopus Google Scholar
Lyoo et al., 2014

K.-S. Lyoo, J.-K. Kim, K. Jung, B.-K. Kang, D. Song

Comparative pathology of pigs infected with Korean H1N1, H1N2, or H3N2 swine influenza A viruses

Virol. J., 11 (2014), p. 170, 10.1186/1743-422X-11-170

View in Scopus Google Scholar
M May, 1991

A.R. M May

Infectious diseases of humans: dynamics and control

Oxford University Press, Oxford (1991)

Google Scholar
Ma and Earn, 2006

J. Ma, D.J.D. Earn

Generality of the final size formula for an epidemic of a newly invading infectious disease

Bull. Math. Biol., 68 (2006), pp. 679-702, 10.1007/s11538-005-9047-7

View in Scopus Google Scholar
Ma, 2020

W. Ma

Swine influenza virus: Current status and challenge

Virus Res, 288 (2020), Article 198118, 10.1016/j.virusres.2020.198118

View PDF View article View in Scopus Google Scholar
Mancera Gracia et al., 2020

J.C. Mancera Gracia, D.S. Pearce, A. Masic, M. Balasch

Influenza A Virus in Swine: Epidemiology, Challenges and Vaccination Strategies

Front Vet. Sci., 7 (2020), p. 647, 10.3389/fvets.2020.00647

View in Scopus Google Scholar
Mayo, 2024
Mayo, D., 2024. 2024 Average Farmland Rental Rates and Worker Wages Summary [WWW Document]. URL 〈https://nwdistrict.ifas.ufl.edu/phag/2024/12/20/2024-average-farmland-rental-rates-and-worker-wages-summary/〉 (accessed 12.26.25).

Google Scholar
McInerney et al., 1992

J.P. McInerney, K.S. Howe, J.A. Schepers

A framework for the economic analysis of disease in farm livestock

Prev. Vet. Med., 13 (1992), pp. 137-154, 10.1016/0167-5877(92)90098-z

View PDF View article View in Scopus Google Scholar
McKinnell et al., 2015

J.A. McKinnell, S.M. Bartsch, B.Y. Lee, S.S. Huang, L.G. Miller

Cost-benefit analysis from the hospital perspective of universal active screening followed by contact precautions for methicillin-resistant Staphylococcus aureus carriers

Infect. Control Hosp. Epidemiol., 36 (2015), pp. 2-13, 10.1017/ice.2014.1

View in Scopus Google Scholar
MetaFarms and National Pork Board, 2023
MetaFarms & National Pork Board, 2023. Production AnalysisSummary for U.S. PorkIndustry: 2019-2023 [WWW Document]. URL 〈https://porkcheckoff.org/wp-content/uploads/2024/07/2024-Production-Analysis-Full-Report-1.pdf〉 (accessed 12.26.25).

Google Scholar
Moraes et al., 2023

D.C.A. Moraes, A. L Vincent Baker, X. Wang, Z. Zhu, E. Berg, G. Trevisan, J. Zhang, S. Jayaraman, D.C.L. Linhares, P.C. Gauger, G. S Silva

Veterinarian perceptions and practices in prevention and control of influenza virus in the Midwest United States swine farms

Front. Vet. Sci., 10 (2023), Article 1089132, 10.3389/fvets.2023.1089132

View in Scopus Google Scholar
Moura et al., 2022

C.A.A. Moura, R. Philips, G.S. Silva, D.J. Holtkamp, D.C.L. Linhares

Comparison of virus detection, productivity, and economic performance between lots of growing pigs vaccinated with two doses or one dose of PRRS MLV vaccine, under field conditions

Prev. Vet. Med., 204 (2022), Article 105669, 10.1016/j.prevetmed.2022.105669

View PDF View article View in Scopus Google Scholar
Nathues et al., 2018

H. Nathues, P. Alarcon, J. Rushton, R. Jolie, K. Fiebig, M. Jimenez, V. Geurts, C. Nathues

Modelling the economic efficiency of using different strategies to control Porcine Reproductive & Respiratory Syndrome at herd level

Prev. Vet. Med., 152 (2018), pp. 89-102, 10.1016/j.prevetmed.2018.02.005

View PDF View article View in Scopus Google Scholar
Ouyang et al., 2019

T. Ouyang, X. Zhang, X. Liu, L. Ren

Co-infection of swine with porcine Circovirus type 2 and other swine viruses

Viruses, 11 (2019), p. 185, 10.3390/v11020185

View in Scopus Google Scholar
Papatsiros et al., 2023

V.G. Papatsiros, G.I. Papakonstantinou, E. Meletis, K. Koutoulis, Z. Athanasakopoulou, G. Maragkakis, G. Labronikou, I. Terzidis, P. Kostoulas, C. Billinis

Seroprevalence of swine influenza A virus (swIAV) infections in commercial farrow-to-finish pig farms in Greece

Vet. Sci., 10 (2023), p. 599, 10.3390/vetsci10100599

View in Scopus Google Scholar
Parys et al., 2022

A. Parys, E. Vandoorn, K. Chiers, K. Van Reeth

Alternating 3 different influenza vaccines for swine in Europe for a broader antibody response and protection

Vet. Res., 53 (2022), p. 44, 10.1186/s13567-022-01060-x

View in Scopus Google Scholar
Pedersen, 2019

T.L. Pedersen

patchwork: The Composer of Plots

CRAN Contrib. Packages (2019), 10.32614/cran.package.patchwork

Google Scholar
Petro-Turnquist et al., 2024

E. Petro-Turnquist, M.J. Pekarek, E.A. Weaver

Swine influenza A virus: challenges and novel vaccine strategies

Front. Cell. Infect. Microbiol, 14 (2024), Article 1336013, 10.3389/fcimb.2024.1336013

View in Scopus Google Scholar
Pittman Ratterree et al., 2024

D.C. Pittman Ratterree, S.C. Dass, M.L. Ndeffo-Mbah

Mechanistic models of influenza transmission in commercial swine populations: A systematic review

Pathogens, 13 (2024), p. 746, 10.3390/pathogens13090746

View in Scopus Google Scholar
Pitzer et al., 2016

V.E. Pitzer, R. Aguas, S. Riley, W.L.A. Loeffen, J.L.N. Wood, B.T. Grenfell

High turnover drives prolonged persistence of influenza in managed pig herds

J. R. Soc. Interface, 13 (2016), 10.1098/rsif.2016.0138

Google Scholar
Plastina, 2024
Plastina, A., 2024. Financial Performance Measures for Iowa Farms [WWW Document]. URL 〈https://www.extension.iastate.edu/agdm/wholefarm/html/c3-55.html〉 (accessed 1.7.26).

Google Scholar
Rajao et al., 2023

D.S. Rajao, G.C. Zanella, M. Wymore Brand, S. Khan, M.E. Miller, L.M. Ferreri, C.J. Caceres, S. Cadernas-Garcia, C.K. Souza, T.K. Anderson, P.C. Gauger, A.L. Vincent Baker, D.R. Perez

Live attenuated influenza A virus vaccine expressing an IgA-inducing protein protects pigs against replication and transmission

Front. Virol., 3 (2023), 10.3389/fviro.2023.1042724

Google Scholar
Report-Texas Daily Grain Bids [WWW Document], 2025
Report-Texas Daily Grain Bids [WWW Document], 2025. URL 〈https://mymarketnews.ams.usda.gov/viewReport/2711〉 (accessed 6.23.25).

Google Scholar
Reynolds et al., 2014

J.J.H. Reynolds, M. Torremorell, M.E. Craft

Mathematical modeling of influenza A virus dynamics within swine farms and the effects of vaccination

PLoS One, 9 (2014), Article e106177, 10.1371/journal.pone.0106177

View in Scopus Google Scholar
Romagosa et al., 2011

A. Romagosa, M. Allerson, M. Gramer, H.S. Joo, J. Deen, S. Detmer, M. Torremorell

Vaccination of influenza a virus decreases transmission rates in pigs

Vet. Res., 42 (2011), p. 120, 10.1186/1297-9716-42-120

View in Scopus Google Scholar
Romagosa et al., 2012

A. Romagosa, M. Gramer, H.S. Joo, M. Torremorell

Sensitivity of oral fluids for detecting influenza A virus in populations of vaccinated and non-vaccinated pigs

Influenza Other Respi. Virus, 6 (2012), pp. 110-118, 10.1111/j.1750-2659.2011.00276.x

View in Scopus Google Scholar
Rose et al., 2013

N. Rose, S. Hervé, E. Eveno, N. Barbier, F. Eono, V. Dorenlor, M. Andraud, C. Camsusou, F. Madec, G. Simon

Dynamics of influenza A virus infections in permanently infected pig farms: evidence of recurrent infections, circulation of several swine influenza viruses and reassortment events

Vet. Res., 44 (2013), p. 72, 10.1186/1297-9716-44-72

View in Scopus Google Scholar
Ryt-Hansen et al., 2022

P. Ryt-Hansen, H.G. Nielsen, S.S. Sørensen, I. Larsen, C.S. Kristensen, L.E. Larsen

The role of gilts in transmission dynamics of swine influenza virus and impacts of vaccination strategies and quarantine management

Porc. Health Manag, 8 (2022), p. 19, 10.1186/s40813-022-00261-2

View in Scopus Google Scholar
Salvesen and Whitelaw, 2021

H.A. Salvesen, C.B.A. Whitelaw

Current and prospective control strategies of influenza A virus in swine

Porc. Health Manag, 7 (2021), p. 23, 10.1186/s40813-021-00196-0

View in Scopus Google Scholar
Sandbulte et al., 2015

M.R. Sandbulte, A.R. Spickler, P.K. Zaabel, J.A. Roth

Optimal use of vaccines for control of influenza A virus in swine

Vaccin. (Basel), 3 (2015), pp. 22-73, 10.3390/vaccines3010022

View in Scopus Google Scholar
Schaefer et al., 2015

R. Schaefer, R.R. Rech, D. Gava, M.E. Cantão, M.C. da Silva, S. Silveira, J.R.C. Zanella

A human-like H1N2 influenza virus detected during an outbreak of acute respiratory disease in swine in Brazil

Arch. Virol., 160 (2015), pp. 29-38, 10.1007/s00705-014-2223-z

View in Scopus Google Scholar
Schultz-Cherry et al., 2013

S. Schultz-Cherry, C.W. Olsen, B.C. Easterday

History of Swine Influenza

J.A. Richt, R.J. Webby (Eds.), Swine Influenza, Springer Berlin Heidelberg, Berlin, Heidelberg (2013), pp. 21-27, 10.1007/82_2011_197

View in Scopus Google Scholar
Snell, 2010

M. Snell

Cost-Benefit Analysis Second edition: A Practical Guide (2nd ed.), Thomas Telford, London, England (2010), 10.1680/cba.41349

Google Scholar
Song et al., 2010

X.-H. Song, H. Xiao, Y. Huang, G. Fu, B. Jiang, Y. Kitamura, W. Liu, D. Liu, G.F. Gao

Serological surveillance of influenza A virus infection in swine populations in Fujian province, China: no evidence of naturally occurring H5N1 infection in pigs

Zoonoses Public Health, 57 (2010), pp. 291-298, 10.1111/j.1863-2378.2009.01270.x

View in Scopus Google Scholar
Toft et al., 2005

N. Toft, A.R. Kristensen, E. Jørgensen

A framework for decision support related to infectious diseases in slaughter pig fattening units

Agric. Syst., 85 (2005), pp. 120-137, 10.1016/j.agsy.2004.07.017

View PDF View article View in Scopus Google Scholar
Torremorell et al., 2012

M. Torremorell, M. Allerson, C. Corzo, A. Diaz, M. Gramer

Transmission of influenza A virus in pigs

Transbound. Emerg. Dis., 59 (1) (2012), pp. 68-84, 10.1111/j.1865-1682.2011.01300.x

View in Scopus Google Scholar
United States Department of Agriculture-National Agricultural Statistics Service, 2024
United States Department of Agriculture-National Agricultural Statistics Service, 2024. USDA (2024) 2022 Census of Agriculture, Summary and State Data.

Google Scholar
USDA-APHIS, 2021
USDA-APHIS, 2021. VS Memorandum (VSM) 800.69 [WWW Document]. URL 〈https://www.aphis.usda.gov/sites/default/files/memo800-69.pdf〉 (accessed 1.26.26).

Google Scholar
USDA-APHIS, 2024
USDA-APHIS, 2024. Biosecurity on U.S. Swine Operations NAHMS Swine 2021 Study [WWW Document]. URL 〈https://www.aphis.usda.gov/sites/default/files/swine-large-ent-biosecurity-2021-infobrief.pdf〉 (accessed 1.27.26).

Google Scholar
Vaccination administration via gel saves time, labor and money [WWW Document], 2025
Vaccination administration via gel saves time, labor and money [WWW Document], 2025. URL 〈https://bi-animalhealth.com/swine/articles/vaccination-administration-via-gel-saves-time-labor-and-money〉 (accessed 12.23.25).

Google Scholar
VanderWaal and Deen, 2018

K. VanderWaal, J. Deen

Global trends in infectious diseases of swine

Proc. Natl. Acad. Sci. U. S. A., 115 (2018), pp. 11495-11500, 10.1073/pnas.1806068115

View in Scopus Google Scholar
Vincent et al., 2008

A.L. Vincent, K.M. Lager, B.H. Janke, M.R. Gramer, J.A. Richt

Failure of protection and enhanced pneumonia with a US H1N2 swine influenza virus in pigs vaccinated with an inactivated classical swine H1N1 vaccine

Vet. Microbiol, 126 (2008), pp. 310-323, 10.1016/j.vetmic.2007.07.011

View PDF View article View in Scopus Google Scholar
Vincent et al., 2010

A.L. Vincent, J.R. Ciacci-Zanella, A. Lorusso, P.C. Gauger, E.L. Zanella, M.E. Kehrli, Jr, B.H. Janke, K.M. Lager

Efficacy of inactivated swine influenza virus vaccines against the 2009 A/H1N1 influenza virus in pigs

Vaccine, 28 (2010), pp. 2782-2787, 10.1016/j.vaccine.2010.01.049

View PDF View article View in Scopus Google Scholar
What and when: Deeper look at wean-to-finish mortality [WWW Document], 2024
What and when: Deeper look at wean-to-finish mortality [WWW Document], 2024. National Hog Farmer. URL 〈https://www.nationalhogfarmer.com/livestock-management/what-and-when-deeper-look-at-wean-to-finish-mortality〉 (accessed 1.27.26).

Google Scholar
White et al., 2017

L.A. White, M. Torremorell, M.E. Craft

Influenza A virus in swine breeding herds: Combination of vaccination and biosecurity practices can reduce likelihood of endemic piglet reservoir

Prev. Vet. Med., 138 (2017), pp. 55-69, 10.1016/j.prevetmed.2016.12.013

View PDF View article View in Scopus Google Scholar
Wickham et al., 2007

H. Wickham, W. Chang, L. Henry, T.L. Pedersen, K. Takahashi, C. Wilke, K. Woo, H. Yutani, D. Dunnington, T. van den Brand

Ggplot2: Create elegant data visualisations using the grammar of graphics

CRAN Contrib. Packages (2007), 10.32614/cran.package.ggplot2

Google Scholar
Wolf and Wolf, 2005

C.A. Wolf, C.A. Wolf

Prod. Livest. Dis. Manag. Incent. Decis. (2005), 10.22004/AG.ECON.8179

Google Scholar
Zhang et al., 2014

H. Zhang, H. Kono, S. Kubota

An integrated epidemiological and economic analysis of vaccination against highly pathogenic porcine reproductive and respiratory syndrome (PRRS) in thua Thien hue province, Vietnam

AsianAustralas. J. Anim. Sci., 27 (2014), pp. 1499-1512, 10.5713/ajas.2014.14060

View in Scopus Google Scholar
Zhou et al., 2021

L. Zhou, X. Ge, H. Yang

Porcine reproductive and respiratory syndrome modified live virus vaccine: A “leaky” vaccine with debatable efficacy and safety

Vaccin. (Basel), 9 (2021), p. 362, 10.3390/vaccines9040362

View in Scopus Google Scholar

USA – Default

USA – Default

Cost-benefit analysis of swine influenza a vaccination in wean-to-finish production setting in the United States

Abstract

Keywords

1. Introduction