Mechanistic Models of Influenza Transmission in Commercial Swine Populations: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Model Methodologies Overview
3.1.1. Metapopulations
3.1.2. Parameterization
3.2. Epidemiological Characteristics
3.2.1. Control Strategies
3.2.2. Swine-Only Models
3.2.3. Swine–Avian Models
3.2.4. Swine–Human Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Terms |
---|---|
Animal | Pig*, swine, porcine, suid |
Disease | Influenza, influenza virus, flu |
Modeling | mathematical model, Markov model, decision tree model, network model, decision tree analysis, metapopulation model, dynamic model, mathematical, mathematics, mathematic, model*, biomathematic, biomathematics, mathemati*, biomath, computational biology, computational, computational statisti*, mechanistic, equatio*, formul*, computer simulatio*, statistical mode*, statistical metho*, stochastic proces*, SIR, SEIR, susceptible-infected-recovered model, susceptible-exposed-infected-recovered model, compartmental, deterministic, stochastic, predictive mode*, predictive modeling, predictio*, epidemic theor*, discrete, continuous, applied math*, tempor* model, spat* model |
Study | Model Structure | Transmission | Parameterization | Data | Model Fitting Method | Sensitivity/Uncertainty Analysis | Model Validation |
---|---|---|---|---|---|---|---|
Allerson, 2013 [44] | SIR model S: Susceptible I: Infected R: Recovered | Within herd | Model fitted to empirical data | Laboratory experiment conducted by authors | Generalized linear model using a log–log link function | None | None |
Andraud, 2023 [45] | MSIVRS metapopulation model. M: Individual with MDA V: Vaccinated | Within herd | Model fitted to empirical data | Laboratory experiment conducted by authors | Bayesian inference performed with Monte Carlo Markov Chains. Maximum Likelihood | None | None |
Cador, 2016 [46] | MSIR metapopulation model. | Within herd | Literature and assumptions | Simulated data | None | Probabilistic uncertainty analysis | None |
Cador, 2017 [47] | MSIVRS model metapopulation model. | Within herd | Literature | Simulated data | None | None | None |
Er, 2021 [4] | SIS model | Between herds | Model fitted to empirical data | Longitudinal seroprevalence data from 2009 to 2020 | Bayesian inference of Bernoulli trials | None | Ground truthed to seroprevalence data from Norwegian herds |
Etbaigha, 2018 [48] | SEIR metapopulation model. E: Exposed class | Within herd | Literature | Simulated data | None | Univariate | None |
Mateus-Anzola, 2019 [49] | SIR model | Between herds | Literature | Simulated data | None | None | None |
Nelson, 2015 [50] | SIR metapopulation model | Between countries | Model fitted to empirical data | Swine pop size, trade information from 146 countries from 1969 to 2010 reported to the Food and Agriculture Organization of the United States | None | Univariate | None |
Pitzer, 2016 [51] | MSIRS model | Within herd | Literature and Model fitted to empirical data | Seroprevalence data from 15 Dutch finishing herds and 14 farrow-to-finish herds running from 290 to 4500 pigs per herd. H1N1, H1N2, and H3N2 | None | Univariate | Ground truthed to seroprevalence data from Dutch herds |
Reynolds, 2014 [52] | SEIR metapopulation model | Within herd | Literature | Simulated data | None | Univariate and probabilistic uncertainty analysis | None |
Romagosa, 2011 [53] | SIR model | Within herd | Model fitted to empirical data | Laboratory experiment conducted by authors | None | None | None |
Toft, 2005 [54] | SIR model | Within herd | Literature | Simulated data | None | None | None |
White, 2017 [55] | SEIRV metapopulation model | Within herd | Literature | Simulated data | None | Latin hypercube sampling and partial rank correlation coefficient | None |
Swine Avian | |||||||
Chen, 2016 [56] | Multispecies SIR model | Within herd | Literature | Simulated data | None | None | None |
Coburn, 2011 [57] | SI-SIJ model J: super-strain infectious class | Within herd | Model fitted to empirical data and Literature | Human data calibrated to the 2004-2005 Thailand strain. Pig parameters were from the Food and Agriculture Organization of the United Nations | Least-squares | None | None |
Zhuang, 2013 [58] | Multispecies SIR model | Within herd | Literature | Simulated data | None | None | None |
Swine Human | |||||||
Adi-Kusumo, 2017 [59] | SEIR-SIRC model C: Cross-immune class | Within herd | Literature | Simulated data | None | None | None |
Dorjee, 2016 [42] | SEIR-SEIR model | Between herds | Literature | Canadian population census and map data | None | None | None |
Kontowicz, 2023 [60] | SEIR-SEIR model | Within herd | Literature | Simulated data | None | Probabilistic uncertainty analysis | None |
Saenz, 2006 [61] | SIR-SIR model | Within herd | Literature | Simulated data | None | Univariate | None |
Widayanti, 2019 [62] | SI-SIR model | Within herd | Literature | Simulated data | None | Univariate | None |
Wong, 2013 [17] | SIR-SEIR model | Within herd | Model fitted to empirical data and Literature | Human infection cases and retrospective cohort investigation from interviews | Successive iterations of the differential equations model | Scenario Analysis | None |
Study | Virus | Location | Model Type | Modeling Objective | Strategies Evaluated | Results | Limitations |
---|---|---|---|---|---|---|---|
Swine Only | |||||||
Allerson, 2013 [44] | H1N1 (IA/04) | Experiment | Stochastic compartmental | Estimate transmission parameters and the reproduction number in the presence of MDA | Vaccination (homologous vaccine: vaccine prepared with the isolate recovered from the specific population in which it will be used; heterologous vaccine: vaccine prepared with isolates distinct from the specific strain in the population) | No vaccination led to all 10 pigs becoming infected in 89% of the simulations Heterologous vaccination led to all pigs becoming infected in 80% of the simulations Homologous vaccination led to all pigs becoming infected in 0.3% of the simulations | Small herd size used in the stochastic simulations Pigs’ antibody titers were uniform and high, but this may not be the case for pigs in a production setting |
Andraud, 2023 [45] | H1N1 | Farrow-to-finish farm | Stochastic compartmental model | Evaluate single dose vaccination in weaned piglets when MDA | Vaccination at weaning age, mass vaccination, batch-to-batch vaccinations | The probability of the virus persisting ranged from 33 to 67% after 5 years post-introduction when sows were vaccinated leading to MDA in piglets No MDA in piglets with one vaccination led to the extinction of the virus periodically after 4 years post-introduction | The model considers dynamics of only one strain of influenza The results of the transmission experiment may not be accurate for transmission in a production setting |
Cador, 2016 [46] | Influenza A | Farrow-to-finish farm | Stochastic compartmental model | Investigate the role of maternally driven immunity (MDA) on Influenza transmission | - | When greater than 66% of the piglets had MDAs, present model outputs were more variable The presence of MDA in piglets allows swine Influenza A to remain endemic in the population MDA lowers the piglet’s susceptibility thereby reducing the number of shedding animals at peak and longer epidemic durations Low airborne transmission did not decrease viral spread within the farm | No quantitative information was available to estimate the between-room airborne transmission rate For simplicity, authors focus on a single influenza strain |
Cador, 2017 [47] | Influenza A | Farrow-to-finish farm | Stochastic compartmental model | Evaluate the risk of emergence of reassortment viruses from swine influenza viruses’ co-infection events | Vaccination, Concurrent export of weaning pig batches | The infection of growing pigs started at the farrowing site spreading to the nursery and finishing facilities. Three co-circulation patterns of subtypes were observed: (a) subtype i infection closely followed by subtype j (or vice versa), allowing brief co-infections; (b) simultaneous infections by both subtypes, resulting in moderate separate infections but many co-infections; (c) subtype i infection in young piglets, followed by subtype j in finishing rooms months later, causing two distinct outbreaks. Time between piglet batches impacts the fade out probability, large intervals favored fade-out. The gap between batches can prevent infection for older piglets to newly birthed ones Export of piglet batches was the most effective measure in facilitating fade-out of the virus, and there was a synergistic effect of disrupting infection dynamics through consecutive exports | No quantitative data was available for the quantity of virus shed by vaccinated animals. The model only included co-infection events because there was insufficient data to parameterize the likelihood of reassortant generation in the swine context |
Er, 2021 [4] | H1N1 pmd09 | Norway | Stochastic compartmental model | Determine if a Discrete-Time Markov Chain (DTMC) using yearly intervals can forecast influenza prevalence in Norwegian pig population | - | Good predictions of the 2010–2016 seroprevalence data when the model was fit using the 2009-2010 data. The data did not track well after 2016 because of changes in the force of infection | Considered an oversimplified model because it is an aggregated representation of all types in pig production Serosurveillance detects antibodies, so positive herds may not indicate new infections from an active virus. This is especially true for pig herds that tested positive in consecutive years |
Etbaigha, 2018 [48] | Influenza A | Farrow-to-finish farm, Ontario, Canada | Deterministic compartmental model | Analyze the dynamics of influenza infection (including reinfection) in a farrow-to-finish swine farm and evaluate the effectiveness of control strategies | Vaccination, Reduction of Indirect contact | One infected gilt led to 50% of the gilt population being infected a few days later and oscillation is caused by weekly introduction of susceptible gilts No difference in infection dynamics was observed between growing pigs with and without immunity The vaccination strategies were unable to reduce flu burden experienced by entire farms Reducing indirect contact was also ineffective at reducing virus burden on the farms | No empirical data exist for parameters that impact the behavior of the model such as the vaccine immunity time and the indirect transmission rates For simplicity, authors focus on a single influenza strain. |
Mateus-Anzola, 2019 [49] | Non-specific influenza | Backyard farms, Lerma, Mexico | Stochastic compartmental model | Investigate the impact of backyard farm networks on Influenza transmission dynamics | - | The connectedness of these farms is critical in understanding the dynamics of IAV In a low connectivity scenario, 34 days was sufficient to infect half the backyard farms In a high connectivity scenario, 5 days was sufficient to infect half the backyard farms. | Out of date national census information was utilized in the model Information on animal movement between backyard farms and markets at national, regional, and local levels was limited Information on pig density was limited |
Nelson, 2015 [50] | Influenza A | Global trade network | Stochastic compartmental model | Investigate the global spread and swine influenza viruses and identify geographical hotspots for viral reassortment | - | The analysis shows continuous long-distance transmission of influenza along trade networks since the 1970s The United States and China are not representative of the observed virus diversity from the phylogenetic analysis and more surveillance efforts are required from Asian and Latin American countries | Did not account for within-country dynamics Did not consider the likelihood of initial viral emergence within seed countries The limited number of gene sequences from Asian countries other than China limited the ability to identify viral migration events within Asia |
Pitzer, 2016 [51] | Influenza A | Farrow-to-finish farm, the Netherlands | Stochastic compartmental model | Investigate the effect of farm size and MDA on influenza persistence at the herd level Evaluate herd management strategies | All-in-all-out herd management | Virus persists in finishing herds with 1500 pigs when the R0 is 2, and in herds with at least 3000 pigs when R0 is 1.5 All-in-all-out herd management was the least likely to support IAV persistence If influenza strains are indeed capable of persisting at the individual farm level, their model suggests it may be difficult to detect through conventional surveillance efforts, since prevalence on the farm is predicted to be low | The model considers dynamics of only one strain of influenza Authors suggest that to have a 95% probability of detecting at least one positive pig for each strain at each time point, more than 45 pigs would need to be sampled randomly |
Reynolds, 2014 [52] | H1N1 (IA04) | Experiment | Stochastic compartmental model | Assess the effectiveness of vaccination and demonstrate how partial protection impacts transmission dynamics | Vaccination (homologous and heterologous vaccine) | In the fully naive population, 84% of the simulations led to the entire pen becoming contact infected Heterologous vaccination simulations resulted in 7% having the entire pen becoming infected and 40% with no new cases Homologous pre-farrow vaccination delays the outbreak, lowers the number of infectious pigs at the peak by a third, and increases the number of infectious gilts and sows after the peak compared with no vaccination | Transmission rates could be overestimated due to the inoculation method leading to higher shedding of the virus Transmission parameters were calculated for within pen only and therefore cannot be applied to larger production scenarios |
Romagosa, 2011 [53] | Non-specific influenza | Farrow-to-finish farm | Deterministic compartmental model | Analyze the dynamics of swine influenza infection dynamics at farm level and evaluate the effectiveness of vaccination strategies | Vaccination (homologous and heterologous vaccine) | IAV rapidly spreads after the introduction of one infected gilt through the breeding farm; at peak, more sows are infected than gilts in this section of the farm Breeding farm proportion of infected pigs was higher than the wean-finish farm Endemicity is predicted when influenza is introduced to a breeding farm Endemicity is not often observed in the wean-to-finish as no new susceptibles are added to the population Current vaccination strategies are not sufficient to eliminate IAV The sensitivity analysis showed the model was robust to changes in transmission rates | Limited model to a single influenza strain over limited timescale (40 days) Assumed fully naive population may have led to overestimation of infected pigs |
Toft, 2005 [54] | Non-specific influenza | Fattening unit | Stochastic compartmental model | Create a framework to support decision making that optimizes control strategies for infectious diseases in pigs to maximize pig production | Vaccination, Treatment | Treatment was ineffective as it only reduced the contact rate by 40% compared with vaccination Vaccination is expensive and unprofitable to use in numerous scenarios assessed | Markov decision process models become large with complex decision problems Considered one section of animals despite production facilities having multiple sections that have the potential to infect each other Model assumes the number of infectious pigs is known and that there is no incubation period |
White, 2017 [55] | H1N1 | Farrow-to-finish farm | Stochastic compartmental model | To explore transmission dynamics of IAV under 14 different management interventions independently and in combination | Gilt introduction timing, Gilt separation, Gilt vaccination, Early weaning, Vaccination | Gilt introduction: lowered mean endemic prevalence compared to no control strategy, but the introduction of new gilts still caused an outbreak Gilt separations: minimal effects on endemic prevalence Gilt vaccination: minimally effective for disease control Early weaning: insignificant effect on max prevalence Vaccine strategy/efficacy: homologous mass vaccination every 2 months was the best Younger animals play a pivotal role in maintaining IAV in swine Overall vaccination only had a minimal effect | Did not evaluate different sized farms with all interventions Seasonality’s effects on transmission or introductions were not considered Did not account for differences in latency period and infectious period between piglets with and without MDA Model did not account for new strains being introduced, mutation of the virus, or partial immunity from past infections |
Swine Avian | |||||||
Chen, 2016 [56] | Non-specific influenza | Purely theoretical | Deterministic compartmental model | Analyze influenza transmission between three species | - | Pigs as an intermediate host are more important than birds (the natural reservoir) in the role of human epidemics The per capita incidence rate from birds to pigs has minimal impact on the human epidemic | How species meet each other is not well defined Significant uncertainty in model parameters and results arises from calibration to generic settings |
Coburn, 2011 [57] | Non-specific influenza | Thailand | Deterministic compartmental model | Analyze the dynamics of emergent influenza strain from recombination of avian and human strains | - | The emergence of super strains causes either: periodic outbreaks, a super strain that sustains at low levels without causing a big outbreak, or strong initial epidemic followed by weaker epidemics. Simulations showed recurrent outbreaks Super-strain emerging from pigs to humans usually had the highest mortality for humans at the first outbreak Cross-specie interaction makes the continuous introduction of super strains to humans inevitable | Limited data on influenza pigs meant authors had to make assumptions on parameters Model does not account for reassortment of the virus that could change transmission dynamics over the long term |
Zhuang, 2013 [58] | Non-specific influenza | Farm with both swine and poultry | Stochastic compartmental model | Model multispecies influenza dynamics on a farm with swine and poultry | - | As the contact between animals increases, the infection in both populations reach their peak earlier and have higher values; this was more obvious in the pig population | Significant uncertainty in model parameters and results arises from calibration to generic settings Epidemic dynamics for birth, death, and immigration/emigration parameters were not considered |
Swine Human | |||||||
Adi-Kusumo, 2017 [59] | Non-specific influenza | Purely theoretical | Deterministic compartmental model | Investigate infection dynamics between animals and humans | Reduced swine–human interaction | Higher risk of disease in the human population was observed when the virus was endemic in the swine population Viral persistence in the human population was shown to depend on the frequency of contact between humans and pigs | How swine and humans come into contact with each other is not well defined For simplicity, authors focus on a single influenza strain Significant uncertainty in model parameters and results arises from calibration to generic settings |
Dorjee, 2016 [42] | pH1N1 | Ontario, Canada | Agent-based model | Identify transmission parameters that impact the spread of disease most Investigate the vaccination’s impact on an epidemic | Human vaccination | It is critical to reduce influenza transmissibility at the swine–human interface as it prevents large outbreaks in the human population Targeted vaccination campaigns for swine workers were shown to reduce the proportion of swine worker households infected between 13%–68% Once the infection is introduced in the rural/urban human populations, the virus would spread independent of its spread at the swine–human interface | The unit of the simulation is farms and households Model could not incorporate or explore the effect of different contact network structures The personal computer version of North American Animal Disease Spread Model takes a significant amount of time to run With NAADSM, it is not possible to assign more than a single location to each unit |
Kontowicz, 2023 [60] | Non-specific influenza | Commercial growing unit, Midwest United States | Stochastic compartmental model | Quantify the effectiveness of IAV control measures targeting workforce and hogs in a single facility | Vaccination, Isolation of infected pig, Alternative worker routine, Personal protective equipment (PPE) for workers | No interventions or maternal immunity led to 90% outbreaks for the simulations; this was reduced to 37.3% with MDA Vaccination is helpful, despite being suboptimal In the cases of new strains emerging, early identification and isolation of pigs can reduce the number of total infections and probability of infecting workers A routine of working from the youngest batch of pigs to the oldest may reduce the risk of transmission in pigs | Because of limited empirical data, the between-room transmission parameter combines both fomite and aerosol transmission The model may be using an underestimation of interspecies transmission and days to first workforce infection, because they were calculated from a limited number of outbreak studies at outdoor agricultural fairs or research farms with a lower pig to worker ratio |
Saenz, 2006 [61] | Non-specific influenza | Concentrated Animal Feeding Operation | Deterministic compartmental model | Investigate the transmission dynamics of a new influenza strain among three linked populations (swine, farm workers, general population) | Human vaccination | The confined animal feeding operations (CAFO) workers serve as the bridging population for interspecies transmission, in the sense that only CAFO workers can infect and be infected by both humans and the CAFO species The extent of the influenza epidemic in humans was amplified by 42%–86% as the percentage of CAFO workers in the local community varied from 15% to 45% Higher successful vaccination levels lead to decreases in the size of the human epidemic. Vaccination of CAFO workers would be an effective use of a vaccine against a new influenza virus | Control measures were only considered for the worker population The basic reproductive number (R0) for humans was an educated guess based on large historical outbreaks |
Widayanti, 2019 [62] | A-H1N1 | Purely theoretical | Deterministic compartmental model | Understand transmission dynamics between pigs and humans | Human vaccination | If the parameters for death are smaller than the parameters for pig mobility, the chance of success of susceptible pigs becoming infected and coefficient of periodic transmission then the virus will become endemic in pig population | Significant uncertainty in model parameters and results arises from calibration to generic settings |
Wong, 2013 [17] | H3N2v | County fair | Stochastic compartmental and Deterministic compartmental model | Simulation outbreak to estimate the number of infections among county fair attendees | - | Of the nearly 15,000 people in attendance, it is estimated 80 people younger than 20 years old contracted the infection and 58 people 20 years and older became infected Model estimates the probability of infection as 0.024 for each minute a person spent in contact with the infectious swine population at Fair A Low attack rate seen in this model; despite this, several infections can be expected from fair | Data used to fit the model came from suspected cases not confirmed cases The estimates of how long cohort members and other Fair A attendees spent in contact with swine may not be accurate The model did not account for different types of contact such as observing pigs at a distance or directly handling the animals, which could vary between cohort members and other fair attendees R0 was calculated from experimental studies and used to determine the number of infectious swine |
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Pittman Ratterree, D.C.; Dass, S.C.; Ndeffo-Mbah, M.L. Mechanistic Models of Influenza Transmission in Commercial Swine Populations: A Systematic Review. Pathogens 2024, 13, 746. https://doi.org/10.3390/pathogens13090746
Pittman Ratterree DC, Dass SC, Ndeffo-Mbah ML. Mechanistic Models of Influenza Transmission in Commercial Swine Populations: A Systematic Review. Pathogens. 2024; 13(9):746. https://doi.org/10.3390/pathogens13090746
Chicago/Turabian StylePittman Ratterree, Dana C., Sapna Chitlapilly Dass, and Martial L. Ndeffo-Mbah. 2024. "Mechanistic Models of Influenza Transmission in Commercial Swine Populations: A Systematic Review" Pathogens 13, no. 9: 746. https://doi.org/10.3390/pathogens13090746