A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

Modeling zoonotic and vector-borne viruses

The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.

Modeling transmission of avian influenza viruses at the human-animal-environment interface in Cuba

Introduction

The increasing geographical spread of highly pathogenic avian influenza viruses (HPAIVs) is of global concern due to the underlying zoonotic and pandemic potential of the virus and its economic impact. An integrated One Health model was developed to estimate the likelihood of Avian Influenza (AI) introduction and transmission in Cuba, which will help inform and strengthen risk-based surveillance activities.

Materials and methods

The spatial resolution used for the model was the smallest administrative district ("Consejo Popular"). The model was parameterised for transmission from wild birds to poultry and pigs (commercial and backyard) and then to humans. The model includes parameters such as risk factors for the introduction and transmission of AI into Cuba, animal and human population densities; contact intensity and a transmission parameter (β).

Results

Areas with a higher risk of AI transmission were identified for each species and type of production system. Some variability was observed in the distribution of areas estimated to have a higher probability of AI introduction and transmission. In particular, the south-western and eastern regions of Cuba were highlighted as areas with the highest risk of transmission.

Discussion

These results are potentially useful for refining existing criteria for the selection of farms for active surveillance, which could improve the ability to detect positive cases. The model results could contribute to the design of an integrated One Health risk-based surveillance system for AI in Cuba. In addition, the model identified geographical regions of particular importance where resources could be targeted to strengthen biosecurity and early warning surveillance.

Influenza A virus vaccine research conducted in swine from 1990 to May 2018: A scoping review

BACKGROUND

Influenza A viruses of swine (IAV-S) are a global zoonotic and economic concern. Primary control is through vaccination yet a formal evidence map summarizing vaccine research conducted in pigs is not available.

OBJECTIVE

Ten characteristics of English language primary IAV-S vaccine research, conducted at the level of the pig or higher, were charted to identify research gaps, topics for systematic review, and coverage across different publication types.

DESIGN

Six online databases and grey literature were searched, without geographic, population, or study type restrictions, and abstracts screened independently and in duplicate for relevant research published between 1990 and May 2018. Full text data was charted by a single reviewer.

RESULTS

Over 11,000 unique citations were screened, identifying 376 for charting, including 175 proceedings from 60 conferences, and 170 journal articles from 51 journals. Reported outcomes were heterogeneous with measures of immunity (86%, n = 323) and virus detection (65%, n = 246) reported far more than production metrics (9%, n = 32). Study of transmissibility under conditions of natural exposure (n = 7), use of mathematical modelling (n = 11), and autogenous vaccine research reported in journals (n = 7), was limited.

CONCLUSIONS

Most research used challenge trials (n = 219) and may have poor field relevance or suitability for systematic review if the purpose is to inform clinical decisions. Literature on vaccinated breeding herds (n = 89) and weaned pigs (n = 136) is potentially sufficient for systematic review. Research under field conditions is limited, disproportionately reported in conference proceedings versus journal articles, and may be insufficient to support systematic review.

Data-driven contact structures: From homogeneous mixing to multilayer networks

The modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population.

Impact of the distribution of recovery rates on disease spreading in complex networks

We study a general epidemic model with arbitrary recovery rate distributions. This simple deviation from the standard setup is sufficient to prove that heterogeneity in the dynamical parameters can be as important as the more studied structural heterogeneity. Our analytical solution is able to predict the shift in the critical properties induced by heterogeneous recovery rates. We find that the critical value of infectivity tends to be smaller than the one predicted by quenched mean-field approaches in the homogeneous case and that it can be linked to the variance of the recovery rates. Our findings also illustrate the role of dynamical-structural correlations, where we allow a power-law network to dynamically behave as a homogeneous structure by an appropriate tuning of its recovery rates. Overall, our results demonstrate that heterogeneity in the recovery rates, eventually in all dynamical parameters, is as important as the structural heterogeneity.

A review of knowledge discovery process in control and mitigation of avian influenza

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

Simulation modeling of influenza transmission through backyard pig trade networks in a wildlife/livestock interface area

Influenza constitutes a challenge to animal and human health. It is a highly contagious disease with wildlife reservoirs and considered as endemic among swine populations. Pigs are crucial in the disease dynamics due to their capacity to generate new reassortant viruses. The risk of informal animal trade in the spread of zoonotic diseases is well recognized worldwide. Nevertheless, the contribution of the backyard pig trade network in the transmission of influenza in a wildlife/livestock interface area is unknown. This study provides the first simulation of influenza transmission based on backyard farm connections in Mexico. A susceptible-infectious-recovered (SIR) model was implemented using the Epimodel software package in R, and 260 backyard farms were considered as nodes. Three different scenarios of connectivity (low, medium, and high) mediated by trade were generated and compared. Our results suggest that half of the pig population were infected within 5 days in the high connectivity scenario and the number of infected farms was approximately 65-fold higher compared to the low connected one. The consequence of connectivity variations directly influenced both time and duration of influenza virus transmission. Therefore, high connectivity driven by informal trade constitutes a significant risk to animal health. Trade patterns of animal movements are complex. This approach emphasizes the importance of pig movements and spatial dynamics among backyard production, live animal markets, and wildlife.

Disease persistence on temporal contact networks accounting for heterogeneous infectious periods

The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts-the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.

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

Influenza A virus (IAV) in swine is a pathogen that causes a threat to the health as well as to the production of swine. Moreover, swine can spread this virus to other species including humans. The virus persists in different types of swine farms as evident in a number of studies. The core objectives of this study are (i) to analyze the dynamics of influenza infection of a farrow-to-finish swine farm, (ii) to explore the reinfection at the farm level, and finally (iii) to examine the effectiveness of two control strategies: vaccination and reduction of indirect contact. The analyses are conducted using a deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) model. Simulation results show that the disease is maintained in gilts and piglets because of new susceptible pigs entering the population on a weekly basis. A sensitivity analysis shows that the results are not sensitive to variation in the parameters. The results of the reinfection simulation indicate that the virus persists in the entire farm. The control strategies studied in this work are not successful in eliminating the virus within the farm.

High turnover drives prolonged persistence of influenza in managed pig herds

Pigs have long been hypothesized to play a central role in the emergence of novel human influenza A virus (IAV) strains, by serving as mixing vessels for mammalian and avian variants. However, the key issue of viral persistence in swine populations at different scales is ill understood. We address this gap using epidemiological models calibrated against seroprevalence data from Dutch finishing pigs to estimate the ‘critical herd size’ (CHS) for IAV persistence. We then examine the viral phylogenetic evidence for persistence by comparing human and swine IAV. Models suggest a CHS of approximately 3000 pigs above which influenza was likely to persist, i.e. orders of magnitude lower than persistence thresholds for IAV and other acute viruses in humans. At national and regional scales, we found much stronger empirical signatures of prolonged persistence of IAV in swine compared with human populations. These striking levels of persistence in small populations are driven by the high recruitment rate of susceptible piglets, and have significant implications for management of swine and for overall patterns of genetic diversity of IAV.

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

Effect of Intermediate Hosts on Emerging Zoonoses

Most emerging zoonotic pathogens originate from animals. They can directly infect humans through natural reservoirs or indirectly through intermediate hosts. As a bridge, an intermediate host plays different roles in the transmission of zoonotic pathogens. In this study, we present three types of pathogen transmission to evaluate the effect of intermediate hosts on emerging zoonotic diseases in human epidemics. These types are identified as follows: TYPE 1, pathogen transmission without an intermediate host for comparison; TYPE 2, pathogen transmission with an intermediate host as an amplifier; and TYPE 3, pathogen transmission with an intermediate host as a vessel for genetic variation. In addition, we established three mathematical models to elucidate the mechanisms underlying zoonotic disease transmission according to these three types. Stability analysis indicated that the existence of intermediate hosts increased the difficulty of controlling zoonotic diseases because of more difficult conditions to satisfy for the disease to die out. The human epidemic would die out under the following conditions: TYPE 1: [Formula: see text] and [Formula: see text]; TYPE 2: [Formula: see text], [Formula: see text], and [Formula: see text]; and TYPE 3: [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] Simulation with similar parameters demonstrated that intermediate hosts could change the peak time and number of infected humans during a human epidemic; intermediate hosts also exerted different effects on controlling the prevalence of a human epidemic with natural reservoirs in different periods, which is important in addressing problems in public health. Monitoring and controlling the number of natural reservoirs and intermediate hosts at the right time would successfully manage and prevent the prevalence of emerging zoonoses in humans.

Dynamics of Virus Distribution in a Defined Swine Production Network Using Enteric Viruses as Molecular Markers

Modern swine production systems represent complex and dynamic networks involving numerous stakeholders. For instance, livestock transporters carry live animals between fattening sites, abattoirs, and other premises on a daily basis. This interconnected system may increase the risk of microbial spread within and between networks, although little information is available in that regard. In the present study, a swine network composed of 10 finishing farms, one abattoir, and three types of stakeholders (veterinarians, livestock transporters, and nutritional technicians) in Quebec, Canada, was selected to investigate specific vectors and reservoirs of enteric viruses. Environmental samples were collected from the premises over a 12-month period. Samples were screened using targeted reverse transcription-PCR and sequencing of two selected viral markers, group A rotaviruses (RVA) and porcine astroviruses (PoAstV), both prevalent and genetically heterogeneous swine enteric viruses. The results revealed frequent contamination of farm sites (21.4 to 100%), livestock transporter vehicles (30.6 to 68.8%) and, most importantly, the abattoir yard (46.7 to 94.1%), depending on the sample types. Although high levels of strain diversity for both viruses were found, identical PoAstV and RVA strains were detected in specific samples from farms, the abattoir yard, and the livestock transporter vehicle, suggesting interconnections between these premises and transporters. Overall, the results from this study underscore the potential role of abattoirs and livestock transport as a reservoir and transmission route for enteric viruses within and between animal production networks, respectively.

IMPORTANCE

Using rotaviruses and astroviruses as markers of enteric contamination in a swine network has revealed the potential role of abattoirs and livestock transporters as a reservoir and vectors of enteric pathogens. The results from this study highlight the importance of tightening biosecurity measures. For instance, implementing sanitary vacancy between animal batches and emphasizing washing, disinfection, and drying procedures on farms and for transportation vehicles, as well as giving limited access and circulation of vehicles throughout the production premises, are some examples of measures that should be applied properly. The results also emphasize the need to closely monitor the dynamics of enteric contamination in the swine industry in order to better understand and potentially prevent the spread of infectious diseases. This is especially relevant when a virulent and economically damaging agent is involved, as seen with the recent introduction of the porcine epidemic diarrhea virus in the country.

Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability

Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only.

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

Recent modelling and empirical work on influenza A virus (IAV) suggests that piglets play an important role as an endemic reservoir. The objective of this study is to test intervention strategies aimed at reducing the incidence of IAV in piglets and ideally, preventing piglets from becoming exposed in the first place. These interventions include biosecurity measures, vaccination, and management options that swine producers may employ individually or jointly to control IAV in their herds. We have developed a stochastic Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIRV) model that reflects the spatial organization of a standard breeding herd and accounts for the different production classes of pigs therein. Notably, this model allows for loss of immunity for vaccinated and recovered animals, and for vaccinated animals to have different latency and infectious periods from unvaccinated animals as suggested by the literature. The interventions tested include: (1) varied timing of gilt introductions to the breeding herd, (2) gilt separation (no indirect transmission to or from the gilt development unit), (3) gilt vaccination upon arrival to the farm, (4) early weaning, and (5) vaccination strategies of sows with different timing (mass and pre-farrow) and efficacy (homologous vs. heterologous). We conducted a Latin Hypercube Sampling and Partial Rank Correlation Coefficient (LHS-PRCC) analysis combined with a random forest analysis to assess the relative importance of each epidemiological parameter in determining epidemic outcomes. In concert, mass vaccination, early weaning of piglets (removal 0-7days after birth), gilt separation, gilt vaccination, and longer periods between introductions of gilts (6 months) were the most effective at reducing prevalence. Endemic prevalence overall was reduced by 51% relative to the null case; endemic prevalence in piglets was reduced by 74%; and IAV was eliminated completely from the herd in 23% of all simulations. Importantly, elimination of IAV was most likely to occur within the first few days of an epidemic. The latency period, infectious period, duration of immunity, and transmission rate for piglets with maternal immunity had the highest correlation with three separate measures of IAV prevalence; therefore, these are parameters that warrant increased attention for obtaining empirical estimates. Our findings support other studies suggesting that piglets play a key role in maintaining IAV in breeding herds. We recommend biosecurity measures in combination with targeted homologous vaccination or vaccines that provide wider cross-protective immunity to prevent incursions of virus to the farm and subsequent establishment of an infected piglet reservoir.

Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings

Background

The homogeneous mixing assumption is widely adopted in epidemic modelling for its parsimony and represents the building block of more complex approaches, including very detailed agent-based models. The latter assume homogeneous mixing within schools, workplaces and households, mostly for the lack of detailed information on human contact behaviour within these settings. The recent data availability on high-resolution face-to-face interactions makes it now possible to assess the goodness of this simplified scheme in reproducing relevant aspects of the infection dynamics.

Methods

We consider empirical contact networks gathered in different contexts, as well as synthetic data obtained through realistic models of contacts in structured populations. We perform stochastic spreading simulations on these contact networks and in populations of the same size under a homogeneous mixing hypothesis. We adjust the epidemiological parameters of the latter in order to fit the prevalence curve of the contact epidemic model. We quantify the agreement by comparing epidemic peak times, peak values, and epidemic sizes.

Results

Good approximations of the peak times and peak values are obtained with the homogeneous mixing approach, with a median relative difference smaller than 20 % in all cases investigated. Accuracy in reproducing the peak time depends on the setting under study, while for the peak value it is independent of the setting. Recalibration is found to be linear in the epidemic parameters used in the contact data simulations, showing changes across empirical settings but robustness across groups and population sizes.

Conclusions

An adequate rescaling of the epidemiological parameters can yield a good agreement between the epidemic curves obtained with a real contact network and a homogeneous mixing approach in a population of the same size. The use of such recalibrated homogeneous mixing approximations would enhance the accuracy and realism of agent-based simulations and limit the intrinsic biases of the homogeneous mixing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2003-3) contains supplementary material, which is available to authorized users.

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

Swine Influenza A Viruses (swIAVs) have been shown to persist in farrow-to-finish pig herds with repeated outbreaks in successive batches, increasing the risk for respiratory disorders in affected animals and being a threat for public health. Although the general routes of swIAV transmission (i.e. direct contact and exposure to aerosols) were clearly identified, the transmission process between batches is still not fully understood. Maternally derived antibodies (MDAs) were stressed as a possible factor favoring within-herd swIAV persistence. However, the relationship between MDAs and the global spread among the different subpopulations in the herds is still lacking. The aim of this study was therefore to understand the mechanisms induced by MDAs in relation with swIAV spread and persistence in farrow-to-finish pig herds. A metapopulation model has been developed representing the population dynamics considering two subpopulations—breeding sows and growing pigs—managed according to batch-rearing system. This model was coupled with a swIAV-specific epidemiological model, accounting for partial passive immunity protection in neonatal piglets and an immunity boost in re-infected animals. Airborne transmission was included by a between-room transmission rate related to the current prevalence of shedding pigs. Maternally derived partial immunity in piglets was found to extend the duration of the epidemics within their batch, allowing for efficient between-batch transmission and resulting in longer swIAV persistence at the herd level. These results should be taken into account in the design of control programmes for the spread and persistence of swIAV in swine herds.

Metagenomic analysis demonstrates the diversity of the fecal virome in asymptomatic pigs in East Africa

Pigs harbor a variety of viruses that are closely related to human viruses and are suspected to have zoonotic potential. Little is known about the presence of viruses in smallholder farms where pigs are in close contact with humans and wildlife. This study provides insight into viral communities and the prevalence and characteristics of enteric viral co-infections in smallholder pigs in East Africa. Sequence-independent amplification and high-throughput sequencing were applied to the metagenomics analysis of viruses in feces collected from asymptomatic pigs. A total of 47,213 de novo-assembled contigs were constructed and compared with sequences from the GenBank database. Blastx search results revealed that 1039 contigs (>200 nt) were related to viral sequences in the GenBank database. Of the 1039 contigs, 612 were not assigned to any viral taxa because they had little similarity to known viral genomic or protein sequences, while 427 contigs had a high level of sequence similarity to known viruses and were assigned to viral taxa. The most frequent contigs related to mammalian viruses resembling members of the viral genera Astrovirus, Rotavirus, Bocavirus, Circovirus, and Kobuvirus. Other less abundant contigs were related to members of the genera Sapelovirus, Pasivirus, Posavirus, Teschovirus and Picobirnavirus. This is the first report on the diversity of the fecal virome of pig populations in East Africa. The findings of the present study help to elucidate the etiology of diarrheal diseases in pigs and identify potential zoonotic and emerging viruses in the region. Further investigations are required to compare the incidence of these viruses in healthy and diseased pigs in order to better elucidate their pathogenic role.

Detection and molecular characterization of zoonotic viruses in swine fecal samples in Italian pig herds

Gastrointestinal disease is frequent in pigs, and among the different etiological agents involved, viruses are considered the leading cause of infection in this animal species. Furthermore, about half of the newly identified swine pathogens are viruses, many of which may be transmitted to humans by direct contact or by indirect transmission pathways. In this study, the prevalence of astrovirus (AstV), group A rotavirus (RVA), norovirus (NoV) and hepatitis E virus (HEV) infections in pigs was investigated. During 2012-2014, 242 fecal samples were collected from pigs at different production stages (5 to 220 days old) on eight swine farms located in northern, central and southern Italy. Seven out of eight farms analyzed were positive for AstV, which was detected in 163 out of 242 (67.4%) samples and was the most prevalent virus; 61 of the 163 AstV-positive animals (37.4%) had diarrhea. HEV was detected on six farms and in 45 (18.6%) of the 242 samples analyzed. Twenty-three HEV-infected pigs had diarrhea (51.1%). A lower prevalence was observed for RVA, which was found in 10 of the 242 samples (4.1%) from three positive farms, and diarrhea was present only in six infected pigs (60.0%). No swine samples were found to be positive for NoV. Genetic diversity and phylogenetic relationships of some strains representative of the different viruses detected were investigated, confirming a wide heterogeneity of viral strains circulating among pigs.

Animals Used in Research and Education, 1966-2016: Evolving Attitudes, Policies, and Relationships

Since the inception of the Association of American Veterinary Medical Colleges (AAVMC), the use of animals in research and education has been a central element of the programs of member institutions. As veterinary education and research programs have evolved over the past 50 years, so too have societal views and regulatory policies. AAVMC member institutions have continually responded to these events by exchanging best practices in training their students in the framework of comparative medicine and the needs of society. Animals provide students and faculty with the tools to learn the fundamental knowledge and skills of veterinary medicine and scientific discovery. The study of animal models has contributed extensively to medicine, veterinary medicine, and basic sciences as these disciplines seek to understand life processes. Changing societal views over the past 50 years have provided active examination and continued refinement of the use of animals in veterinary medical education and research. The future use of animals to educate and train veterinarians will likely continue to evolve as technological advances are applied to experimental design and educational systems. Natural animal models of both human and animal health will undoubtedly continue to serve a significant role in the education of veterinarians and in the development of new treatments of animal and human disease. As it looks to the future, the AAVMC as an organization will need to continue to support and promote best practices in the humane care and appropriate use of animals in both education and research.

One-Health Simulation Modelling: Assessment of Control Strategies Against the Spread of Influenza between Swine and Human Populations Using NAADSM

Simulation models implemented using a range of parameters offer a useful approach to identifying effective disease intervention strategies. The objective of this study was to investigate the effects of key control strategies to mitigate the simultaneous spread of influenza among and between swine and human populations. We used the pandemic H1N1 2009 virus as a case study. The study population included swine herds (488 herds) and households-of-people (29,707 households) within a county in Ontario, Canada. Households were categorized as: (i) rural households with swine workers, (ii) rural households without swine workers and (iii) urban households without swine workers. Seventy-two scenarios were investigated based on a combination of the parameters of speed of detection and control strategies, such as quarantine strategy, effectiveness of movement restriction and ring vaccination strategy, all assessed at three levels of transmissibility of the virus at the swine-human interface. Results showed that the speed of detection of the infected units combined with the quarantine strategy had the largest impact on the duration and size of outbreaks. A combination of fast to moderate speed of the detection (where infected units were detected within 5-10 days since first infection) and quarantine of the detected units alone contained the outbreak within the swine population in most of the simulated outbreaks. Ring vaccination had no added beneficial effect. In conclusion, our study suggests that the early detection (and therefore effective surveillance) and effective quarantine had the largest impact in the control of the influenza spread, consistent with earlier studies. To our knowledge, no study had previously assessed the impact of the combination of different intervention strategies involving the simultaneous spread of influenza between swine and human populations.

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

Influenza A virus infections are widespread in swine herds across the world. Influenza negatively affects swine health and production, and represents a significant threat to public health due to the risk of zoonotic infections. Swine herds can act as reservoirs for potentially pandemic influenza strains. In this study, we develop mathematical models based on experimental data, representing typical breeding and wean-to-finish swine farms. These models are used to explore and describe the dynamics of influenza infection at the farm level, which are at present not well understood. In addition, we use the models to assess the effectiveness of vaccination strategies currently employed by swine producers, testing both homologous and heterologous vaccines. An important finding is that following an influenza outbreak in a breeding herd, our model predicts a persistently high level of infectious piglets. Sensitivity analysis indicates that this finding is robust to changes in both transmission rates and farm size. Vaccination does not eliminate influenza throughout the breeding farm population. In the wean-to-finish herd, influenza infection may persist in the population only if recovered individuals become susceptible to infection again. A homologous vaccine administered to the entire wean-to-finish population after the loss of maternal antibodies eliminates influenza, but a vaccine that only induces partial protection (heterologous vaccine) has little effect on influenza infection levels. Our results have important implications for the control of influenza in swine herds, which is crucial in order to reduce both losses for swine producers and the risk to public health.

VIRAPOPS2 supports the influenza virus reassortments

BACKGROUND

For over 400 years, due to the reassortment of their segmented genomes, influenza viruses evolve extremely quickly and cause devastating epidemics. This reassortment arises because two flu viruses can infect the same cell and therefore the new virions' genomes will be composed of segment reassortments of the two parental strains. A treatment developed against parents could then be ineffective if the virions' genomes are different enough from their parent's genomes. It is therefore essential to simulate such reassortment phenomena to assess the risk of apparition of new flu strain.

FINDINGS

So we decided to upgrade the forward simulator VIRAPOPS, containing already the necessary options to handle non-segmented viral populations. This new version can mimic single or successive reassortments, in birds, humans and/or swines. Other options such as the ability to treat populations of positive or negative sense viral RNAs, were also added. Finally, we propose output options giving statistics of the results.

CONCLUSION

In this paper we present a new version of VIRAPOPS which now manages the viral segment reassortments and the negative sense single strain RNA viruses, these two issues being the cause of serious public health problems.

Influenza forecasting in human populations: a scoping review

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

One-Health Simulation Modelling: A Case Study of Influenza Spread between Human and Swine Populations using NAADSM

The circulation of zoonotic influenza A viruses including pH1N1 2009 and H5N1 continue to present a constant threat to animal and human populations. Recently, an H3N2 variant spread from pigs to humans and between humans in limited numbers. Accordingly, this research investigated a range of scenarios of the transmission dynamics of pH1N1 2009 virus at the swine-human interface while accounting for different percentages of swine workers initially immune. Furthermore, the feasibility of using NAADSM (North American Animal Disease Spread Model) applied as a one-health simulation model was assessed. The study population included 488 swine herds and 29, 707 households of people within a county in Ontario, Canada. Households were categorized as follows: (i) rural households with swine workers, (ii) rural households without swine workers, and (iii) urban households without swine workers. Forty-eight scenarios were investigated, based on the combination of six scenarios around the transmissibility of the virus at the interface and four vaccination coverage levels of swine workers (0-60%), all under two settings of either swine or human origin of the virus. Outcomes were assessed in terms of stochastic 'die-out' fraction, size and time to peak epidemic day, overall size and duration of the outbreaks. The modelled outcomes indicated that minimizing influenza transmissibility at the interface and targeted vaccination of swine workers had significant beneficial effects. Our results indicate that NAADSM can be used as a framework to model the spread and control of contagious zoonotic diseases among animal and human populations, under certain simplifying assumptions. Further evaluation of the model is required. In addition to these specific findings, this study serves as a benchmark that can provide useful input to a future one-health influenza modelling studies. Some pertinent information gaps were also identified. Enhanced surveillance and the collection of high-quality information for more accurate parameterization of such models are encouraged.

The pandemic H1N1 influenza experience

The pandemic H1N1 influenza that began in Mexico in the spring of 2009 spread rapidly to southern California within days and around the world within a few months. Because the genetic make-up of the new virus was novel, several months of lead-in time were required before a suitable vaccine for human use could be produced and distributed. The effort to confront the virus on the part of the World Health Organization which included almost every nation on earth and a vast array of scientists and public health officials was extensive and timely. However, it was the moderate severity of the virus itself that saved global public health from catastrophe. Because of the extensive publicity and research that occurred during the H1N1 pandemic, many lessons were learned that will be useful in confronting future influenza pandemics. A "One Health" approach to prevent, detect, and combat future pandemics is essential.