In this session of the workshop, issues related to detection and monitoring of swine disease. The session started with a joint presentation by Matthew Branan and Kamina Johnson (Animal and Plant Health Inspection Service [APHIS]) about the work they do on monitoring, surveillance, and modeling for swine disease. Their presentation was followed by a panel of discussants, Lee Schulz (Iowa State), Andrew Lawson (Medical University of South Carolina), and Michael Schweinberger (Rice University), and an open discussion moderated by Eric Slud (University of Maryland and U.S. Census Bureau).
SWINE DISEASE SURVEILLANCE, MONITORING, DETECTION, AND MODELING
Branan gave a general overview about the APHIS systems and programs that are in place to surveil and monitor swine disease. Types of surveillance include active or routine, observational or passive, and to support trade. Active surveillance involves purposeful types of sampling and testing for specific diseases that APHIS has authority to monitor or are part of an eradication program. Passive surveillance involves opportunistic sampling and testing for specific diseases, such as foot-and-mouth disease. Surveillance to support trade might be part of a voluntary type of system such as the trichinellosis certification program. There is no requirement to test for trichinellosis, but some producers test to demonstrate that their hogs are free of disease in order to open or retain a market.
Branan reviewed APHIS information sources. Slaughter surveillance, either part of active or passive surveillance, can come from Food Safety and Inspection Service (FSIS) slaughter condemnations. Samples are sent to a lab and tested for foot-and-mouth disease or pseudorabies, for example. On-farm surveillance may be targeted to operations that are deemed to be high risk for certain diseases such as classical swine fever. These involve diagnostic laboratory submissions. Producers may send in voluntary sick-pig submissions to be tested for various agents or diseases. On-farm investigations are also related to foreign animal disease investigations. For example, Seneca Valley virus has a lesion that looks like foot-and-mouth disease. Presence of such a lesion may trigger an on-farm investigation. If an operator wants to move animals between states or out of the United States, a certificate of veterinary inspection is needed. Animals coming into the United States are required to be inspected at the border.
APHIS has eradication programs for diseases of concern, such as swine brucellosis and pseudorabies. There are surveillance programs for other diseases, such as African swine flu and classical swine fever, even though the latter has been eradicated in the United States. The mandatory reporting for swine coronavirus diseases such as Porcine Epidemic Diarrhea virus (PEDv) and Porcine Deltacoronavirus ended in 2018. The three high-profile foreign animal diseases now are African swine fever, classical swine fever, and foot-and-mouth disease. Branan explained that there are other reportable domestic diseases identified from a list provided by the World Organization for Animal Health (OIE). If a case of such a disease is detected, APHIS informs OIE.
Branan next described three APHIS systems. First, the National Animal Health Reporting System (NAHRS) is used for diseases that are considered reportable, which includes the list from the OIE and the National List of Reportable Animal Diseases. APHIS gets the data through confidential monthly submissions from the states that indicate presence of disease for livestock, poultry, aquaculture, and so on. This information can feed into disease response activities at the state level, the national level, and to support trade.
Second, the National Wildlife Disease Program within the Wildlife Services Group monitors the effects of wildlife on agriculture. For example, there is a program to identify and quantify the types of
damage caused by feral swine. Feral swine can damage crops with their wallowing, tree rubbing, and root-finding activities, and they can also be reservoirs for diseases that can spread into domestic swine or cattle populations. The program has mapping activities to track where feral swine are and where they are spreading, and also where the clusters of pseudorabies virus and swine brucellosis are among feral swine populations. The program also educates producers on how to prevent the spread of disease from feral animals to their farms and domestic populations.
Third, the National Animal Health Monitoring System (NAHMS) conducts national surveys every 5 to 10 years to collect information from operators about animal health and management factors. Surveys are cooperative efforts between APHIS and NASS. There are typically two-phase national-level studies for the swine population. NASS administers a questionnaire during the first phase; the second phase includes a questionnaire, administered by APHIS animal health experts, and biological sampling of swine feces and blood to test for specific agents or diseases. APHIS uses NASS data on inventories and cost, and asks about marketing, production practices, movement of animals, and biosecurity. A sample question may be, “How do you keep your pigs sequestered from wildlife?” APHIS collects biological samples and in the past has tested for a variety of diseases including Porcine Respiratory and Reproductive Syndrome (PRRS), trichinellosis, salmonella, E. coli, and enterococcus. These provide estimates of prevalence and presence of diseases at the national level.
For each of these programs and systems, regulatory authorities give APHIS the ability to collect or act on data. The Animal Health Protection Act provides general authority for APHIS to control, prevent, and monitor various diseases. The Swine Health Protection Act is focused on garbage feeder operations. Other authorities such as quarantine laws allow an operation to be quarantined if certain diseases are found.
In the next part of the presentation, Johnson noted the following diseases have specific active or passive surveillance in the United States: classical swine fever, African swine fever, foot-and-mouth disease, pseudorabies, swine brucellosis, and influenza A in swine. Most of the surveillance programs have objectives related to rapid detection of disease occurrences in the United States and/or a demonstration of freedom from disease to support trade. Seneca Valley virus (SVV) is a
disease with similar clinical signs to foot-and-mouth disease. While the foot-and-mouth disease surveillance system is passive, it has been used to examine SVV cases carefully to rule out foot-and-mouth disease. She said that she watched how the reporting for swine enteric coronavirus disease (SECD) came about, was released online, and became widely used. She noted that SVV may be an example of an emerging disease in swine that NASS could consider using in testing its web scraping (see Chapter 6).
One other system that APHIS tracks and monitors uses data from samples of condemnations collected by FSIS. FSIS condemnation data are analyzed on a weekly basis by APHIS. The staff use models to try to figure out which spikes might represent a possible disease event that needs to be investigated.
Most APHIS surveillance systems are set up for active or passive surveillance. Two sets of documents are available online for response to emergency outbreak situations. First, the disease response plans or Red Books provide guidance for responding to a disease outbreak. They have surveillance plans for classical swine fever and foot-and-mouth disease during an outbreak. The second set of documents are disease response strategies, which are available for a few diseases, such as African swine fever.
She reported that oral fluids testing has become widely used in industry for private sale or contracts. Oral fluids testing is done by dropping ropes into a pen of swine. The hogs are trained to chew on the rope. Fluids are later extracted from the rope and sent in for testing. The result is an aggregate sample of pigs in the pen and is much cheaper than individual testing. This testing process is time efficient and cost effective. The process is widely used for PRRS and Coronavirus diseases. It is being explored for use with foreign animal diseases because of the potential cost savings.
Johnson noted that the modeling group with whom she currently works consists mostly of epidemiologists. She is currently the only economist. The group does epidemiologic and economic modeling of potential disease outbreaks in the United States. They focus on transboundary or foreign animal diseases, such as foot-and-mouth disease, African swine fever, and classical swine fever. The models make extensive use of NASS information and APHIS data to estimate locations of affected operations. They use National Animal Health Monitoring System (NAHMS) data for
contact points and prevalence estimates in certain areas to try to determine how diseases spread. They also use some information about indirect contact rates from literature.
APHIS economic impact modeling is a quarterly partial equilibrium model that relies heavily on population data. Johnson showed a graph related to foot-and-mouth disease vaccination scenarios. When they study a disease, they look for different options that can be used to contain it. One goal is to minimize outbreak spread. They also try to develop cost-effective approaches to stopping the spread of disease, including identification of potential producer decisions and incentives. Some producers are not directly affected by an outbreak, but an outbreak typically results in price changes that do impact them. It is a national model, but they are able to develop some regional strategies.
APHIS uses a variety of information sources to develop three types of shock trajectories to explore disease scenarios. First is a production shock scenario: for example, the epidemiological model might determine how many hogs were culled or depopulated because of a disease outbreak. The removal of the animals moves through the model across time. If piglets are depopulated, for example, slaughter numbers will be smaller at the time those piglets would have been big enough to go to market. The second type of trajectory is a trade shock. If there is a disease outbreak, trading partners may impose trade embargos. In this scenario, the question is how much of a flow of live animals and products will be cut off and for how long. The third type of shock is consumer response. For example, a consumer might decide not to eat chicken in response to news about an outbreak of bird flu. This scenario would remove some purchase and consumption of chicken from the marketplace. She noted that APHIS has not used consumer response with a swine disease, but the feature is available in the model.
The model uses the concept of a baseline—the status quo without disease. They use the shocks for each scenario to disrupt market conditions in the baseline of the model and to determine how long and what path it takes to return to equilibrium. A loss or dip is usually seen, followed by a return to baseline or a little above on its way to equilibrium.
Johnson concluded with an example of a different approach that used the same model to look at swine dysentery, a production-level disease. APHIS used prevalence information about swine dysentery in NAHMS.
Otherwise there is no regular federal monitoring for this disease. In her analysis she used the model to measure the impact of reduction of the prevalence of swine dysentery on the marketplace. Although swine dysentery affects a fair number of producers with a fair number of hogs, she found that reducing its prevalence would have little impact on the market prices for hogs. The market found its equilibrium relatively quickly with low-level impacts on a national scale. She said that the impact of eliminating the disease on producers that experienced the disease would have been about a 10 percent increase in net returns. Other producers were not impacted.
Schulz asked whether APHIS and NASS could collaborate and share information in real time, such as the results from web scraping. Johnson replied APHIS would be interested in how to format FSIS information to make it most useful to NASS. She added that APHIS did more web scraping in the past. She agreed with the importance of collaboration and noted APHIS already has a close working relationship with NASS. Linda Young added efforts are being made to share information across U.S. Department of Agriculture agencies. She said she planned to explore potential collaborations.
Slud noted that APHIS has streams of disease monitoring, each of which might be in the form of occasional presence/absence, but many do not have a regular format for reporting. For input into a model, the information would need to be recoded into a general indicator. It might not report all streams all the time, but might indicate when something crosses a threshold. It does not necessarily need to be directly predictive, he said, but to be correlated with important variables and to have a spatial quality.
Johnson noted that APHIS has a risk identification group that looks at international outbreak situations to try to assess threat or risk levels for incursion into the United States. It also tracks and monitors situations happening within the country. It sends reports to the secretary of agriculture’s office. NASS may be another potential USDA customer for their products and reports. Young noted that often NASS can more easily share information with other USDA agencies than outside the department.
Katherine Ensor asked whether NASS and APHIS could develop a regularly shared shock indicator suitable for input to models. For example, if the agencies jointly identified the current or potential shocks of interest with some type of modeling to provide the probabilities of occurrence, this effort might be a spin-off from what the agencies are already doing. Lawson replied that there have been similar indicators developed on the human side, a few of which he developed. For example, because of the interest in biosecurity after 9/11, Lawson and colleagues developed Bayesian models and posterior functionals that identify changes in the system, such as shocks. They developed a surveillance conditional predictive ordinate, which allows for early detection of shocks. In the realm of syndromic surveillance, Kullback Leibler measures have been tested in a number of papers. They can be incorporated in Kalman filter models and are available in Bayesian models. They are relatively easy to compute, he added, and are based on predictive distributions from previous times to current times.
Researchers have been modeling human population epidemics to consider two components, Lawson said. One is an endemic component, which is essentially what NASS is calling equilibrium; the other is an epidemic component, which is the shock. The two submodels run together, and they are added together, each weighted by its posterior probability. They provide information for detecting when the epidemic component starts, carries on, and ends. These are state-space models.1
Lawson referred to comments in earlier sessions about going down to the unit level in spatial modeling. In his view, the most sensitive modeling of disease spread might be at the unit level. However, state-level modeling is still possible. It would require modeling the neighborhoods, the states that are neighbors to a state, and the cross-boundary flows in a fully developed spatial model.
Slud asked whether the weekly accession counts could be developed into a leading indicator of an epidemic, perhaps by way of an interaction term or something that indicates the accession counts are important only if something else happens. There is a spatial distribution to accession count reporting. Lawson responded that he was talking about conditional predictive ordinates. These ordinates are used to predict what will
happen during the next time period, and that prediction is compared to the observed value at that time period. Accessions might provide input to a model, he said.
Ensor asked whether APHIS has modeled the patterns of outbreaks for swine. If there are typical patterns, she queried whether there is statistical information in the patterns themselves. Johnson replied that epidemiologists have explained that any pattern depends on where the outbreak starts, both in terms of geography and the type of operation, as well as direct and indirect contact points, how hogs are moving, and how feed trucks are moving on and off operations. An important consideration is compartmentalization, for example, how feed truck networks move through the system. An outbreak in a commercial operation raising piglets in North Carolina will have a very different outbreak response from a backyard operation in Colorado. Patterns depend roughly on location and type of operation.
Lawson discussed foot-and-mouth disease in the United Kingdom, in which a very large outbreak occurred in one area. A vehicle ban was put in place to stop the spread. It completely knocked down the original epidemic but then the disease jumped to other places. Predicting the jumps is extremely difficult because it is purely random, as occurs with other diseases such as HIV. That said, some patterns are fairly predictable. There is typically a shock with a long tail. He noted that Bayesian models have been used to predict the ends of epidemics, as well as the starts. Predicting the end could be very important from a veterinary point of view.
Schweinberger provided a related idea. Instead of spatial structure, he asked about network structure. He noted two questions to answer: (1) Is there a shock? (Schweinberger said that Lawson had described some Bayesian approaches for answering that question); and (2) If there is a shock, how large will its impact be? For example, a shock in the form of an epidemic could in principle wipe out the entire U.S. population of hogs. However, this possibility is very unlikely because infectious diseases are transmitted by contact and the network of contacts among hogs constrains the spread of infectious diseases. For example, an outbreak of an infectious disease on a farm in Colorado could spread directly to all farms that purchased hogs from the affected farm, but it could not spread directly to farms that did not purchase hogs from the affected farm. The structure of the contact network places hard constraints on how infec-
tious diseases can spread, which is relevant for determining how large a shock in the form of an epidemic can be and how much it can reduce the U.S. population of hogs.
This structure provides a way of assessing the impact of an epidemic, Schweinberger suggested. A survey with two questions could be used to collect additional data: (1) Is there a hog-related disease on your property? and (2) Did you sell hogs, and if so, to whom? Answers to these questions could help to assess the extent of a problem, where disease might have traveled, and which farms to monitor. Understanding the network structure might help in assessing how large the impact of an epidemic might be.
Johnson noted epidemiologists in her group look at different scenarios with different start points. Simulation models help predict the proportion of the population affected through space and time. She said APHIS also manages the Emergency Management Response System (EMRS). The EMRS maintains outbreak response information, including asking questions to farmers about where they source and sell hogs. They commonly do tracebacks with cattle, which tend to be sold in smaller lots than hogs. That information is used during an outbreak to predict spread. They also compare different response strategies to curb disease spread. Strategies include enforcement of boundary stop points for quarantine and deployment of vaccines.
In answer to a question from Schweinberger about the epidemiological models used, Johnson replied that the main model for swine diseases is InterSpread PLUS, developed in New Zealand. APHIS has adapted it for the United States and uses it to prepare national and regional analyses. It started with classical swine fever and have recently adapted the parameters for African swine fever, because those diseases are similar in many ways. APHIS has also been developing an animal disease spread model, but it is not yet operational.
Schweinberger asked about use of a susceptible-infectious-recovered model. This model is applied to determine how infections spread in a population such as a herd. These kinds of models make the implicit assumption that the disease could travel from each unit to every other unit in the population. More metric-based approaches are also available,
and they are more realistic in that they acknowledge a disease can only spread from an infectious unit to other units that are in contact with that infectious unit.
Johnson said they use herd-level models; if the herd tests positive, not all animals on the site will be infected at that point in time. Predicting the spread from one herd to another herd is the aim to looking at spread across multiple sites as opposed to within the herd or flock. The model has a spatial component.
Ron Plain noted the focus on big shocks and new diseases, but he asked about data to estimate death loss for chronic diseases, such as PRRS or swine dysentery, to determine whether levels are high, low, or typical. Even though they may not be big shocks, it would be interesting to see whether the deviation is enough to impact pork production, he said. Branan said FSIS condemnation rates capture part of that information. Codes indicate why particular animals are condemned at slaughter and could track deviation from baseline.
Johnson added that for many production diseases, the prevalence estimates are done through NAHMS studies. The studies provide an update every 5 years to see how the prevalence level is changing. She asked about industry tracking, perhaps by the Swine Health Information Center. Schulz said a private/public partnership at the University of Minnesota, called the Swine Health Monitoring Project (SHMP), started with PEDv and now monitors PEDv and PRRS. Johnson observed SHMP provides more frequent reporting of disease than the NAHMS study that comes out every 5 years.
Yijun Wei asked whether the simulation study mentioned by Johnson was an epidemiological or another type of model. Johnson said that she and Branan are not experts in the epidemiological model, but they would be happy to work with NASS after the workshop and bring in experts with more in-depth information.