# Designing and Implementing Animal Disease Surveillance Systems


## Key Takeaways

- **Objective-Driven Design is Paramount:** Surveillance system architecture must be explicitly defined by measurable objectives, such as demonstrating freedom from disease for trade certification or early detection of exotic pathogens, as attempting to serve multiple, conflicting objectives with a single design compromises effectiveness.
- **Passive vs. Active Surveillance Trade-offs:** Passive systems leverage existing clinical submissions for low-cost, broad coverage but are prone to reporting bias and incomplete denominators, making them suitable for detecting unusual events but poor for proving disease absence; active systems employ standardized, scheduled sampling for unbiased prevalence estimates and subclinical detection but require significant resources.
- **Data Quality and Evaluation are Integral:** Robust data quality assurance, including standardized forms, training, and validation checks, is critical, and system evaluation should be planned from the outset using attributes like sensitivity (diagnostic and population coverage), specificity, timeliness (interval from event to action), and representativeness, as defined by frameworks like the systematic review of surveillance evaluation approaches.
- **International Standards Guide Reporting:** Adherence to WOAH (World Organisation for Animal Health) standards, such as those in the Terrestrial Animal Health Code, is essential for national surveillance programs, dictating notification obligations for listed diseases and providing a framework for trade-related surveillance requirements.
- **Recognizing and Mitigating Failure Modes:** Common surveillance failures include submission fatigue (declining reports due to lack of perceived action), case definition drift (inconsistent interpretation of criteria), laboratory submission bias (reporting influenced by access or cost), and data latency (delays in data availability), all of which require proactive monitoring and corrective actions like audits and workflow reviews.
- **Wildlife Surveillance Presents Unique Challenges:** Wildlife surveillance necessitates adaptations due to the absence of individual identification, movement records, and owner accountability; sampling is often convenience-based, and passive surveillance relies on opportunistic reporting of found-dead animals, requiring integration with domestic surveillance at interface zones.

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Animal disease surveillance systems generate the evidence base for detecting emerging pathogens, monitoring endemic disease trends, supporting trade certification, and evaluating control programs. This article provides a structured framework for veterinary researchers and practitioners who design, implement, or appraise such systems across production animal, companion animal, and wildlife contexts. It addresses the conceptual foundations of surveillance, the distinction between passive and active approaches, the operational decisions that determine data quality, and the methods used to evaluate system performance. The guidance assumes familiarity with epidemiological principles and clinical terminology, and it directs readers to international standards where formal requirements apply.

Surveillance is not a single activity but a coordinated set of processes that collect, analyze, interpret, and disseminate health-related data for action. The design choices made at the outset determine what the system can detect, how quickly it can detect it, and whether the resulting information supports defensible decisions. A system designed without explicit objectives will produce data that cannot be interpreted, resources that cannot be justified, and findings that cannot withstand scrutiny. The [WOAH animal health surveillance standards](https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection/) provide the international reference framework for these design decisions, including notification obligations and the expectations for national surveillance programs.

The reader should approach this material as a design reference. The article answers three questions. What surveillance architectures exist and how do their strengths and limitations differ? Which parameters must be specified before data collection begins? And how does an investigator determine whether a system is fit for its stated purpose? The practical consequences of each decision are emphasized throughout, because surveillance failures more often arise from mismatched design than from technical error in the laboratory.

## At a Glance

| Parameter | Decision or Fact |
|---|---|
| Primary objective | Define the surveillance question before selecting a system architecture |
| Passive surveillance | Relies on routine clinical submissions, low cost, high coverage, but subject to reporting bias and incomplete denominators |
| Active surveillance | Uses standardized, scheduled sampling, provides unbiased prevalence estimates but requires dedicated resources |
| Case definition | Must be explicit, repeatable, and matched to the surveillance objective |
| Target population | Specify species, production class, geography, and time frame |
| Sampling strategy | Probability-based sampling supports inference, purposive sampling supports detection |
| Data elements | Minimum dataset must include identifiers, dates, location, species, and test results |
| Evaluation attributes | Use the framework of the [systematic review of surveillance evaluation approaches](https://pubmed.ncbi.nlm.nih.gov/25928645/) to structure assessment |
| International reporting | Follow [WOAH terrestrial animal health code](https://www.woah.org/en/what-we-do/standards/codes-and-manuals/terrestrial-code-online-access/) requirements for notifiable diseases |

## Defining Surveillance Objectives

Every surveillance system exists to support a decision. The decision may be whether a disease is absent from a population, whether an emerging pathogen has entered a region, whether a control program is reducing incidence, or whether antimicrobial resistance is increasing in a bacterial population. The objective must be stated in measurable terms before any other design work proceeds. A vague objective such as "monitor disease trends" cannot guide sample size calculations, data collection protocols, or resource allocation.

The [CDC principles of epidemiology in public health practice](https://www.cdc.gov/csels/dsepd/ss1978/index.html) frame surveillance as the ongoing systematic collection, analysis, and interpretation of health data, closely integrated with the timely dissemination of findings to those who need them. This definition carries two implications for design. First, surveillance is continuous, not a one-time survey. Second, dissemination is part of the system, not an afterthought. A system that collects data but does not return information to stakeholders will lose its reporting base and its justification.

Objectives should be prioritized when resources are limited. A system designed to detect exotic disease introduction requires high sensitivity and rapid reporting. A system designed to estimate prevalence for trade certification requires statistical representativeness. These objectives are not interchangeable, and attempting to serve both with one design usually compromises both.

## Passive Surveillance Systems

Passive surveillance operates through the existing clinical infrastructure. Veterinarians, laboratory diagnosticians, and producers report suspected cases through established channels, and the system records what arrives. This architecture is inexpensive to operate because it uses data generated for clinical purposes, and it provides broad coverage across the species and regions served by the reporting network.

The principal limitation of passive surveillance is that it depends on the willingness and ability of reporters to recognize, sample, and submit cases. Reporting rates vary with clinical awareness, perceived value of reporting, access to diagnostic services, and the economic consequences of a positive finding. A disease that produces mild or non-specific signs will be under-reported, while a disease with dramatic clinical presentation will be over-represented relative to its true incidence. The denominator, the total population at risk, is often unknown, which prevents calculation of reliable incidence rates.

Passive systems are best suited to detecting unusual clinical events and monitoring trends in diseases with characteriztic presentations. They are poorly suited to demonstrating freedom from disease, because the absence of reports cannot be distinguished from the absence of surveillance activity. For antimicrobial resistance monitoring, passive systems capture resistant isolates from clinical cases but provide no information about the susceptible organizms that were never cultured, a limitation discussed in the [working party review of antibiotic resistance surveillance systems](https://pubmed.ncbi.nlm.nih.gov/11442565/).

## Active Surveillance Systems

Active surveillance involves the deliberate, systematic collection of data according to a predetermined protocol, with the investigating authority initiating contact with data providers instead of waiting for reports to arrive. This design is resource-intensive but offers the advantage of standardized data collection, defined sampling frames, and the capacity to detect infections that would otherwise escape notice because they produce no clinical signs or because affected animals are not presented for examination.

The decision to deploy active surveillance should follow directly from the objectives defined in the planning phase. When the goal is to demonstrate freedom from a specific pathogen, to estimate prevalence with a defined level of confidence, or to monitor trends in a disease that produces subclinical infection, passive reporting will rarely suffice. Active surveillance is also indicated when the consequences of missing an introduction are severe, such as with transboundary diseases that trigger trade restrictions or zoonoses with public health implications.

Sampling strategies for active surveillance fall into several categories. Cross-sectional surveys provide a point estimate of prevalence and are appropriate for certification of freedom when combined with a specified design prevalence and confidence level. Repeated cross-sectional surveys can track changes in prevalence over time, though they are inefficient for detecting rare events. Longitudinal cohorts, in which the same animals are sampled repeatedly, offer greater power to detect incidence and to identify risk factors for infection, but they are costly to maintain and suffer from attrition.

Risk-based sampling concentrates surveillance effort on subpopulations with a higher probability of infection or of introducing infection to naive populations. This approach improves the efficiency of detection for a given budget. For diseases that spread through animal movements, network analysis of movement data can identify premises that are likely to be infected early in an epidemic and that provide critical information about outbreak origin, allowing surveillance resources to be targeted at these sentinel locations [WOAH animal health surveillance standards](https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection/). The temporal instability of movement networks means that static centrality measures are unreliable, and the identification of sentinel premises should be based on simulation of spreading paths across multiple initial conditions [optimizing surveillance for livestock disease spreading through animal movements](https://pubmed.ncbi.nlm.nih.gov/22728387/).

The choice between census and sample-based approaches depends on the size of the target population, the expected prevalence, and the resources available. A census is feasible only for small, well-defined populations such as registered breeding herds. For larger populations, sample size calculations must account for the expected prevalence, the desired confidence level, the test sensitivity and specificity, and the clustering of infection within herds or flocks.

## Selecting Between Passive and Active Designs

The distinction between passive and active surveillance is not a binary choice but a continuum, and most national surveillance programs combine elements of both. The selection of a particular design should be guided by the surveillance objective, the epidemiology of the target disease, the structure of the animal population, and the resources available for sustained operation.

| Design feature | Passive surveillance | Active surveillance |
| --- | --- | --- |
| Data initiation | Producer or veterinarian submits report | Authority initiates contact and sampling |
| Cost per case detected | Low | High |
| Sensitivity for subclinical infection | Very low | Moderate to high |
| Representativeness | Biased toward clinically apparent cases | Can be designed to be representative |
| Timeliness of detection | Variable, depends on reporting behavior | Predictable, depends on sampling interval |
| Data standardization | Limited | High |
| Suitability for freedom from disease | Poor | Good |
| Suitability for early warning of exotic disease | Good if reporting culture is strong | Good if targeted at high-risk populations |

A well-functioning passive system is often the most cost-effective means of detecting the first introduction of an exotic disease, because it draws on the clinical observations of thousands of practitioners at no direct cost to the surveillance authority. The sensitivity of this approach depends on the probability that an affected animal is examined by a veterinarian, that the veterinarian recognizes the condition as reportable, and that the report is submitted and acted upon. Each of these steps is a potential point of failure, and the system should be designed to minimize losses at each stage.

Active surveillance is generally reserved for situations where passive reporting is known to be incomplete, where the disease is not clinically apparent, or where quantitative estimates of prevalence or incidence are required. Many national programs use active surveillance to verify the performance of passive systems, for example by conducting periodic targeted sampling in high-risk populations and comparing the results with the number of passive reports received.

## Surveillance System Evaluation

Evaluation should be planned from the outset, not conducted as an afterthought once the system is operational. The evaluation process follows a common structure: defining the system under evaluation, designing the evaluation process, implementing the evaluation, and drawing conclusions and recommendations [surveillance systems evaluation: a systematic review of the existing approaches](https://pubmed.ncbi.nlm.nih.gov/25928645/). The attributes to be assessed include sensitivity, specificity, timeliness, representativeness, simplicity, flexibility, acceptability, and stability, with the relative importance of each attribute determined by the system's stated objectives.

Sensitivity in surveillance has two components: the ability to detect true cases among those examined, and the ability of the system to capture a sufficient proportion of all cases occurring in the population. The first component is a function of the diagnostic tests used and the case definition applied. The second depends on the coverage of the surveillance population and the probability of reporting. Both components should be assessed separately, because a system with excellent diagnostic sensitivity but poor population coverage will miss most cases.

Timeliness is measured as the interval between a disease event and the point at which the information is available for action. The relevant intervals differ by disease. For a rapidly spreading epidemic disease such as foot-and-mouth disease, the interval between infection and detection must be measured in days. For a slowly progressive disease such as bovine tuberculosis, a delay of several weeks may be acceptable. The evaluation should specify the critical time intervals for each objective and measure the system's performance against them.

Representativeness refers to the degree to which the reported cases accurately reflect the distribution of disease in the target population. Passive systems are frequently unrepresentative because reporting is more likely from regions with better veterinary coverage, from production systems with closer veterinary supervision, and for diseases that produce dramatic clinical signs. Active surveillance can be designed to achieve representativeness through probability sampling, but the design must be implemented faithfully for the results to be valid.

## Data Management and Quality Assurance

The value of a surveillance system depends entirely on the quality of the data it produces. Data quality issues arise at every stage, from the initial observation in the field to the final analysis. Common failure modes include incomplete submission forms, inconsistent use of case definitions, errors in species or breed identification, missing denominator data, and delays between data collection and entry into the central database.

Quality assurance procedures should be built into the system design instead of added after problems emerge. These procedures include standardized data collection forms with defined fields and coding conventions, training for all personnel involved in data collection, automated validation checks at the point of data entry, and periodic audits of a sample of records against source documents. The case definition should be documented explicitly and should be applied consistently across all reporting sources. Where multiple diagnostic laboratories contribute data, inter-laboratory comparability should be verified through proficiency testing and standardized test protocols.

The analysis of surveillance data requires attention to the sampling design. Data from passive surveillance cannot be used to estimate prevalence without adjustment for reporting probability, which is rarely known. Data from active surveillance can support prevalence estimation only if the sampling frame is well defined and the sampling fraction is known. The distinction between surveillance data and research data should be maintained, and the limitations of each data source should be stated in any report.

## Documentation and Reporting

Surveillance systems generate value only when the information they produce reaches decision-makers in a usable form. Reporting protocols should specify the content, format, and frequency of reports for each audience. Veterinary authorities require regular summaries of disease occurrence with sufficient detail to support control decisions. Producers and practitioners require feedback that is timely enough to influence their management decisions and that demonstrates the value of their reporting effort.

The WOAH Terrestrial Animal Health Code sets out international standards for surveillance and notification, and member countries are expected to report the occurrence of listed diseases according to defined timelines [WOAH terrestrial animal health code](https://www.woah.org/en/what-we-do/standards/codes-and-manuals/terrestrial-code-online-access/). National reporting requirements vary, and system designers should be familiar with the obligations that apply in their jurisdiction. The documentation should include a clear description of the surveillance design, the case definition, the sampling strategy, the diagnostic tests used, and the methods of analysis, so that the system can be evaluated and compared with others.

## Recognized Failure Modes and Early Detection

Surveillance systems fail in characteriztic patterns. The most common is submission fatigue, where reporting rates decline as practitioners perceive that their reports produce no visible action. This failure is detected early by monitoring report volume per unit time against historical baselines, stratified by practice type and geographic region. A sustained decline of more than 20 percent over two consecutive reporting periods warrants investigation, as does a decline concentrated in one species or production sector.

A second failure mode is case definition drift, where the operational interpretation of a case definition changes over time without formal amendment. This occurs when different personnel apply clinical judgment inconsistently or when laboratory confirmation criteria shift. Early detection requires periodic audit of a random sample of reported cases against the original case definition, with particular attention to borderline presentations. The systematic review by Calba and colleagues identified inconsistent application of evaluation attributes as a common weakness across surveillance assessment approaches, which applies equally to case ascertainment within operational systems [Calba et al., systematic review of surveillance evaluation approaches](https://pubmed.ncbi.nlm.nih.gov/25928645/).

Laboratory submission bias constitutes a third failure mode. When submission decisions depend on client willingness to pay, diagnostic access, or practitioner familiarity with a laboratory, the resulting data reflect submission behavior instead of disease occurrence. Detection relies on comparing submission rates across demographic groups and geographic areas, then investigating disparities that cannot be explained by population distribution or known risk factors.

Data latency, the interval between event occurrence and data availability, degrades both detection and response. Monitoring the median time from specimen collection to laboratory result, and from result to database entry, identifies bottlenecks. Movement-based surveillance is particularly sensitive to latency, because the value of premises-level information decays rapidly as animals move through the production network [Bajardi et al., optimization of surveillance for livestock disease spread through animal movements](https://pubmed.ncbi.nlm.nih.gov/22728387/).

## Common Errors and Corrective Action

Less experienced personnel frequently confuse absence of reports with absence of disease. A passive system that produces no signals may indicate a functioning system or a disconnected one. The corrective action is to verify system function through sentinel practices, periodic active sampling, or review of complementary data sources such as abattoir findings and pharmaceutical sales.

A second error is over-interpreting small fluctuations. Surveillance data are noisy, and short-term variation in report counts often reflects reporting behavior, laboratory capacity, or seasonal factors instead of true disease change. Clinicians should apply statistical process control methods, such as Cusum or Shewhart charts, and interpret signals against pre-specified thresholds instead of visual inspection alone. The properties of good antimicrobial resistance surveillance systems, including the capacity to detect significant shifts in susceptibility, depend on adequate sample sizes and defined action criteria [Bax et al., surveillance of antimicrobial resistance](https://pubmed.ncbi.nlm.nih.gov/11442565/).

A third error involves designing surveillance around diagnostic convenience instead of epidemiological objectives. Sampling the most accessible population, such as hospitalized animals or university clinic cases, produces biased estimates when the objective is population-level prevalence. Corrective action requires explicit articulation of the target population and deliberate sampling strategy before data collection begins.

## Limitations of Current Evidence

The evidence base for surveillance system design contains substantial gaps. Comparative studies of different system architectures under equivalent conditions are scarce, and most published evaluations are descriptive instead of experimental. The systematic review by Calba and colleagues found that existing evaluation approaches provide broad recommendations but lack detailed guidance on selecting and applying specific assessment methods and tools [Calba et al., systematic review of surveillance evaluation approaches](https://pubmed.ncbi.nlm.nih.gov/25928645/).

Economic evaluation remains particularly underdeveloped. Few published frameworks integrate cost-effectiveness analysis into surveillance design decisions, and those that do often rely on assumptions that are difficult to verify in field conditions. Expert opinion differs on the appropriate balance between sensitivity and specificity in surveillance case definitions, on the minimum acceptable reporting rate for passive systems, and on the value of syndromic surveillance relative to laboratory-confirmed case reporting. These disagreements reflect genuine uncertainty instead of resolvable factual disputes.

## Escalation and Referral Pathways

Clinicians should escalate when surveillance findings exceed their scope of practice or when regulatory obligations apply. Suspicion of a notifiable disease, as defined by national veterinary authorities and consistent with [WOAH terrestrial animal health standards](https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection/), requires immediate reporting to the competent authority. The WOAH Terrestrial Animal Health Code provides the international framework for notification obligations and trade-related surveillance requirements [WOAH Terrestrial Animal Health Code](https://www.woah.org/en/what-we-do/standards/codes-and-manuals/terrestrial-code-online-access/).

Specialist consultation is warranted when diagnostic interpretation exceeds local capacity, when unusual clinical presentations suggest emerging disease, or when surveillance findings imply a need for population-level intervention beyond individual animal care. Laboratory involvement is appropriate for confirmatory testing, strain characterization, antimicrobial susceptibility profiling, and archiving of isolates for retrospective analysis. Referral criteria should be documented in the surveillance protocol so that escalation decisions are consistent and defensible.

| Observation | Likely cause | Discriminating check |
|---|---|---|
| Declining report volume | Submission fatigue or loss of practitioner engagement | Survey reporters on perceived value, compare volume against historical baseline |
| Sudden spike in reports | Case definition drift, heightened awareness, or true outbreak | Audit recent cases against original definition, verify laboratory confirmation |
| Geographic clustering of reports | Submission bias or true focal disease | Compare submission rates to population denominators, conduct targeted active sampling |
| Prolonged time from event to database entry | Laboratory or data entry bottleneck | Measure each interval separately, review workflow for redundant steps |
| Zero reports from a region | Disconnected system or genuinely absent disease | Verify contact information, conduct sentinel visits, review complementary data sources |

## Frequently Asked Questions

### How do I choose surveillance activities when the budget covers only one or two approaches?

Prioritize activities that address the highest-risk pathways and the objectives you defined first. If the primary objective is early detection of an exotic disease, allocate resources to risk-based sampling of high-risk premises instead of broad coverage of low-risk populations. If the objective is demonstrating freedom from disease for trade, the sampling frame and statistical power matter more than the number of samples. Movement data can identify sentinel premises with high infection probability, allowing targeted sampling that outperforms random sampling at equal cost. Document the trade-offs explicitly so stakeholders understand what the chosen design cannot detect.

### What can I do when laboratory capacity or cold-chain logistics are unreliable?

Design the system around the available infrastructure instead of the ideal specification. Use pooled sampling where the diagnostic test permits it, reduce sample numbers but increase sampling frequency, or switch to tests that tolerate ambient temperatures. Preserve samples appropriately for later confirmatory testing when point-of-care results are positive. Record all deviations from the standard protocol, because test sensitivity and specificity estimates assume specified handling conditions. If sample quality is compromised, interpret negative results with caution and state that limitation in reports. Consider participatory approaches that gather clinical observations from producers when laboratory confirmation is not feasible.

### How does the surveillance design change for wildlife compared with domestic livestock?

Wildlife populations lack the individual identification, movement records, and owner accountability that domestic systems rely on. Sampling is often convenience-based, biased toward accessible or harvested animals, and the population denominator is unknown. Passive surveillance depends on reporting of found-dead animals, which varies with species visibility and public awareness. Active surveillance may require capture and sampling, which is costly and can itself affect the population. For diseases shared between wildlife and domestic animals, integrate wildlife sampling with domestic surveillance at the interface. The WOAH terrestrial animal health standards provide guidance on surveillance approaches applicable to different population types.

### What records must I keep to make the surveillance system defensible in an audit?

Keep the surveillance protocol, including case definitions and sampling strategy, as a controlled document. Record every submission with collection date, location, species, specimen type, and the unique identifier linking the animal to its premises. Document laboratory results, including test method and any deviations from the validated protocol. Record the denominator data used for rate calculations, whether that is population estimates, premises counts, or movement records. Log all communication of results to stakeholders. The evaluation literature emphasizes that documenting the system under evaluation is a prerequisite for assessing its performance.

### How do I explain a surveillance finding to a producer whose animals are affected?

State what was found, what it means for their animals, and what actions are recommended, in that order. Avoid speculative interpretation of results that are pending confirmatory testing. Explain the difference between a screening result and a confirmatory result when both are used. Be clear about what is known and what is not, particularly when sample quality was compromised or the test has imperfect sensitivity. Provide the producer with written documentation of the result and the next steps. If the finding triggers regulatory reporting, explain the reporting obligation before the producer hears about it from another source.

### When should I seek external advice or escalate a surveillance finding?

Escalate when the finding meets the case definition for a notifiable disease, when the result suggests a novel or unexpected epidemiological pattern, or when the laboratory result conflicts with clinical observations. Escalate when the surveillance system detects an event that exceeds the expected baseline, even if the cause is not yet confirmed. Seek external advice when designing surveillance for a disease you have not worked with before, because the biological characteriztics of the agent determine the appropriate sampling strategy and diagnostic tests. The WOAH reporting framework defines which findings require international notification, and national authorities can advise on jurisdiction-specific requirements.

## Related Clinical & Scientific Guides

* [Evaluating Veterinary Surveillance System Attributes](/knowledge/veterinary-medicine/veterinary-epidemiology/evaluating-veterinary-surveillance-system-attributes)
* [Network Analysis for Infectious Disease Spread in Animal Populations](/knowledge/veterinary-medicine/veterinary-epidemiology/network-analysis-infectious-disease-spread-animal-populations)
* [Randomized Controlled Trials in Veterinary Field Settings](/knowledge/veterinary-medicine/veterinary-epidemiology/randomized-controlled-trials-veterinary-field-settings)


## References and Further Reading

- [Surveillance systems evaluation: a systematic review of the existing approaches.](https://pubmed.ncbi.nlm.nih.gov/25928645/). 2015.
- [Surveillance of antimicrobial resistance--what, how and whither?](https://pubmed.ncbi.nlm.nih.gov/11442565/). 2001.
- [Modulation of host cell responses and evasion strategies for porcine reproductive and respiratory syndrome virus.](https://pubmed.ncbi.nlm.nih.gov/20655963/). 2010.
- [Clustering of dietary variables and other lifestyle factors (Dutch Nutritional Surveillance System).](https://pubmed.ncbi.nlm.nih.gov/1431719/). 1992.
- [Maternal obesity and breast-feeding practices.](https://pubmed.ncbi.nlm.nih.gov/12663294/). 2003.
- [Optimizing surveillance for livestock disease spreading through animal movements.](https://pubmed.ncbi.nlm.nih.gov/22728387/). 2012.
- [WOAH Animal Health Surveillance Standards](https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection/). WOAH.
- [CDC Principles of Epidemiology in Public Health Practice](https://www.cdc.gov/csels/dsepd/ss1978/index.html). CDC.
- [MSD Veterinary Manual, Professional Edition](https://www.msdvetmanual.com/). MSD Veterinary Manual.

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- [Time Series Analysis for Veterinary Disease Surveillance](/knowledge/veterinary-medicine/veterinary-epidemiology/time-series-analysis-veterinary-disease-surveillance)
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> This article is educational professional reference material for veterinary audiences. It is not a substitute for veterinary diagnosis, individual clinical judgment, current product labeling, or applicable regulatory requirements.