# Participatory Epidemiology Methods for Livestock Disease Surveillance


## Key Takeaways

- Participatory Epidemiology (PE) empowers livestock keepers as active data sources, particularly valuable in resource-limited settings with weak conventional surveillance infrastructure by eliciting local disease knowledge and management practices. Core qualitative and semi-quantitative methods include semi-structured interviews, participatory mapping, matrix scoring, proportional piling, and ranking exercises to define local disease syndromes and risk patterns.
- The scientific foundation of PE rests on the premise that livestock keepers develop locally valid disease classifications that can map onto biomedical entities when systematically elicited and triangulated with clinical or laboratory findings, offering utility comparable to conventional reporting. This approach is especially relevant in pastoral and transhumant systems, and for One Health investigations due to the encompassing nature of local disease terminology.
- Triangulation is a critical requirement for validating PE findings, involving cross-checking informant reports against clinical examination, laboratory confirmation, or review of existing records to ensure defensible data and avoid overstating the correspondence between local syndromes and specific pathogens. Method triangulation across different PE techniques and informant groups, alongside biomedical verification, strengthens confidence in the data.
- Power dynamics within informant groups pose a significant validity threat, where vocal or socially dominant individuals can skew results. Practitioners must actively manage group composition and facilitation to mitigate this, ensuring representation from diverse segments of the livestock-keeping community (e.g., gender, age, wealth) to capture a comprehensive picture of disease knowledge and priorities.
- Local disease terminology is often syndromic and descriptive, mapping onto clinical signs rather than specific etiologies, presenting both opportunities and risks. For instance, a single local term might encompass multiple pathogens, or one biomedical disease might have several local names depending on presentation, necessitating careful interpretation and explicit reporting of diagnostic uncertainty.
- PE methods complement formal surveillance by identifying locally important diseases for resource allocation, detecting unusual events between formal rounds, and generating hypotheses for further epidemiological studies, particularly in low-resource or remote settings. Integration models range from outbreak characterization to surveillance enhancement, with data outputs requiring verification based on the specific surveillance objective.

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Participatory epidemiology (PE) is a structured approach to gathering animal health information that positions livestock keepers as active interpreters of disease patterns instead of passive respondents. The methods described in this article serve veterinary researchers and field epidemiologists who work in production systems where conventional surveillance infrastructure is limited, records are sparse, or disease reporting is incomplete. The procedural focus here covers the core techniques, their analytical logic, and the conditions under which each method yields defensible data. Quantitative survey instruments and statistical sampling frameworks are excluded, the emphasis rests on qualitative and semi-quantitative participatory tools and their interpretation.

The scientific basis of PE rests on a simple premise: livestock keepers who manage animals through seasonal stress, disease outbreaks, and market pressures develop locally valid disease classifications that often map onto biomedical entities. When those classifications are elicited systematically and triangulated against clinical or laboratory findings, they can produce surveillance information comparable in utility to conventional reporting. The methods are particularly valuable in pastoral and transhumant systems, where animals move across administrative boundaries and formal veterinary services may be intermittent. The techniques also serve One Health investigations, since local disease terminology frequently encompasses conditions that affect humans, livestock, and wildlife within a shared ecosystem.

## At a Glance

| Parameter | Detail |
|---|---|
| Primary purpose | Elicit local disease knowledge, priorities, and management practices from livestock keepers |
| Core methods | Semi-structured interviews, participatory mapping, matrix scoring, proportional piling, simple and pairwise ranking |
| Key analytical output | Relative importance rankings, disease syndrome definitions, seasonal and spatial risk patterns |
| Triangulation requirement | Cross-check informant reports against clinical examination, laboratory confirmation, or record review |
| Main validity threat | Informant groups dominated by vocal individuals or powerful community members |
| Typical setting | Pastoral, agro-pastoral, and smallholder systems with limited surveillance infrastructure |
| Data format | Qualitative narratives plus semi-quantitative scores from ranking and piling exercises |
| Reporting standard | WOAH surveillance guidelines for member countries |

## Conceptual Foundations of Participatory Epidemiology

Participatory epidemiology emerged from participatory rural appraisal, a development methodology that challenged top-down research and intervention models. In veterinary applications, PE methods aim to include livestock keepers in research processes and in the development of solutions to animal health problems, including zoonotic diseases. The approach also represents an attempt to bring social science insights into a discipline shaped largely by natural science methods. This intellectual lineage matters for the practitioner because it explains why PE protocols emphasize group dynamics, local terminology, and informant agency instead of standardized questionnaires.

The operational logic of PE differs fundamentally from that of conventional surveys. A structured questionnaire assumes the researcher knows which questions matter and how to phrase them. PE assumes the opposite: the livestock keeper holds the relevant knowledge, and the researcher's task is to create conditions for that knowledge to emerge. This inversion has practical consequences. Question design gives way to conversation prompts. Random sampling gives way to purposive selection of informant groups. Statistical generalization gives way to analytical triangulation across multiple methods and informant groups.

### The Role of Local Disease Terminology

Livestock keepers commonly describe diseases using local language terms that are syndromic and descriptive instead of aetiological. Among the Maasai of Ngorongoro, Tanzania, for example, disease names encode clinical signs, affected body systems, and sometimes seasonality, but they do not distinguish between pathogens that produce similar presentations. This creates both opportunity and risk. The opportunity is that syndromic terms can be mapped onto biomedical disease entities when clinical and laboratory confirmation is available. The risk is that a single local term may cover several distinct pathogens, or conversely, that one biomedical disease may carry multiple local names depending on its presentation.

Practitioners of PE recommend confirming findings with triangulation, including biomedical diagnostic techniques. In practice, however, laboratory confirmation is not always performed, usually due to time, financial, or logistical constraints. Researchers must therefore report the diagnostic certainty associated with each local term and avoid overstating the correspondence between local syndromes and specific pathogens.

## Core Participatory Methods

The standard PE toolkit comprises several distinct techniques, each designed to answer a different type of question. Field studies typically combine multiple methods within a single investigation, using each technique to cross-check and enrich the others.

### Semi-Structured Interviews

Semi-structured interviews use a flexible question guide instead of a fixed questionnaire. The interviewer follows a checklist of topics but allows the conversation to develop according to informant responses. This format suits exploratory investigations where the researcher does not yet know the full range of relevant issues. In a study of tick-borne diseases among transhumant zebu cattle in Karamoja, Uganda, semi-structured interviews were conducted within focus group discussions to establish disease management options and to understand how herders prioritized different conditions. The technique generated information on treatment practices, seasonal disease patterns, and the relative importance of different diseases that would have been difficult to capture with closed questions.

### Participatory Mapping

Participatory mapping asks informants to draw or annotate a map of their area, marking features relevant to animal health. These features may include water sources, grazing areas, cattle routes, markets, veterinary facilities, and locations of past disease outbreaks. The resulting map serves as a shared visual reference that anchors discussion and reveals spatial relationships that might not emerge from verbal questioning alone. Maps are particularly useful for understanding disease transmission dynamics in systems where animals move across large areas. In the Karamoja study, participatory mapping helped identify seasonal grazing movements and their relationship to tick-borne disease risk.

### Matrix Scoring

Matrix scoring is a technique for comparing diseases across multiple attributes. The researcher constructs a table with diseases listed along one axis and attributes along the other. Informants score each disease against each attribute, typically using stones, seeds, or other counters to indicate relative magnitude. Common attributes include frequency, severity, seasonality, treatment cost, mortality, and impact on milk or traction. The resulting matrix reveals how communities differentiate diseases and which attributes drive their priorities. Matrix scoring proved valuable in the Karamoja investigation for understanding why herders ranked East Coast fever above other tick-borne diseases despite similar clinical presentations.

### Proportional Piling

Proportional piling quantifies relative importance using a fixed number of counters. The researcher presents a set of disease names or other items and asks informants to divide a pile of counters among them in proportion to their importance, frequency, or impact. The technique produces semi-quantitative data that can be compared across informant groups and analyzed statistically. Proportional piling is often used in conjunction with ranking methods to validate results through triangulation.

### Ranking Methods

Simple ranking asks informants to order a list of diseases from most to least important. Pairwise ranking presents diseases in pairs and asks informants to choose the more important member of each pair. The pairwise approach generates a complete preference ordering and permits statistical testing of agreement across informant groups. In the Karamoja study, pairwise ranking results were analyzed using Kendall's coefficient of concordance, which demonstrated significant agreement between informant groups on disease priorities. This statistical validation distinguishes pairwise ranking from simpler methods and makes it the preferred technique when research questions require defensible priority ordering.

## Study Design Considerations

The choice of informant groups substantially influences data quality. Focus group discussions typically comprise 8 to 12 participants, selected to represent different segments of the livestock-keeping community. Gender, age, wealth, and herd size all shape disease knowledge and priorities, so a single group drawn from one demographic segment will produce a partial picture. The Karamoja study conducted 24 focus group discussions across 24 settlement areas, supplemented by 30 key informant interviews, direct observation, surveillance data review, clinical examination, and laboratory confirmation. This multi-method design allowed the researchers to cross-validate participatory findings against biomedical evidence.

Power dynamics within informant groups present a persistent methodological challenge. Vocal or socially dominant participants can steer discussions toward their own priorities, while marginalised participants may defer or remain silent. Researchers must actively manage group composition and facilitation style to mitigate these effects. The participatory epidemiology literature identifies power dynamics as a central limitation of the approach, particularly in One Health research where community engagement extends beyond data extraction.

## Field Implementation Sequence

Participatory epidemiology fieldwork proceeds through a defined sequence that balances information quality against community engagement. The sequence begins with site entry and rapport building, moves through progressively more structured data collection, and ends with feedback and verification. Each stage has distinct failure modes that the practitioner must anticipate.

### Site Entry and Group Composition

The first contact determines much of what follows. Entry should be arranged through recognized community leaders or livestock extension officers, as unsolicited arrival undermines trust. The composition of participant groups requires explicit planning. Mixed groups of men and women, or of different wealth strata, may suppress contributions from less powerful members. [Power dynamics within participatory research](https://pubmed.ncbi.nlm.nih.gov/32244084/) can distort findings when facilitators fail to recognize who speaks, who remains silent, and whose knowledge is treated as authoritative. Separate groups by gender, age, or production role where preliminary inquiry suggests this will improve data quality.

Group size for focus discussions typically ranges from 8 to 12 participants, as used in the Karamoja study of tick-borne diseases [participatory investigation of tick-borne disease management options in Ugandan cattle](https://pubmed.ncbi.nlm.nih.gov/26527312/). Smaller groups allow deeper probing but reduce the range of experience represented. Larger groups become unwieldy and allow dominant individuals to steer discussion.

### Facilitator Conduct

The facilitator asks, listens, and records. They do not lecture, correct, or introduce biomedical terminology prematurely. Questions move from general to specific. Open the session with broad inquiry about livestock keeping and current problems, then narrow toward disease-specific topics. The facilitator must resist the urge to fill silences or to rephrase local terms into veterinary language, as this can signal that local knowledge is inadequate.

A second team member should record responses verbatim where possible, noting non-verbal cues and group dynamics. Audio recording requires consent and may inhibit discussion in some communities. Written notes with a structured template are often more practical and less intrusive.

## Triangulation and Verification

Participatory findings require verification through independent methods. The recommended approach combines multiple participatory techniques with clinical examination and laboratory confirmation where feasible [lessons for One Health practitioners using local syndromic terminology](https://pubmed.ncbi.nlm.nih.gov/28364831/). Triangulation operates at several levels: across participant groups, across methods, and against biomedical data.

### Method Triangulation

Each participatory method captures different aspects of local knowledge. Ranking reveals relative importance, matrix scoring reveals syndromic recognition, and mapping reveals spatial patterns. When these methods converge on the same disease priorities, confidence increases. When they diverge, the divergence itself is informative and should be explored with participants instead of dismissed.

### Biomedical Verification

Clinical examination of affected animals and laboratory testing of samples provide the definitive check on local terminology. In the Karamoja study, participatory findings were confirmed through clinical examination and laboratory confirmation of tick-borne disease cases [participatory investigation of tick-borne disease management options in Ugandan cattle](https://pubmed.ncbi.nlm.nih.gov/26527312/). This verification step is essential when participatory data will inform disease control policy or outbreak response.

Resource constraints often limit laboratory confirmation. Where verification is impossible, the limitations must be stated explicitly in any report. Local syndromic terms may map imperfectly onto biomedical disease categories, and this uncertainty should be preserved instead of hidden [lessons for One Health practitioners using local syndromic terminology](https://pubmed.ncbi.nlm.nih.gov/28364831/).

## Documentation and Data Management

Participatory data are qualitative and semi-quantitative. They require documentation protocols that preserve context while enabling analysis.

### Recording Formats

Standardized field forms should capture: session date and location, group composition, facilitator and recorder names, methods used, and raw outputs. For ranking exercises, record the full ordering and the rationale given by participants. For matrix scoring, record the scores assigned and the discussion that accompanied them. For mapping, photograph or trace the final map and annotate it with participant explanations.

### Data Quality Checks

Review completed forms at the end of each day. Identify missing data, ambiguous entries, and contradictions. Return to the community for clarification where needed, as this is often easier than attempting to interpret unclear records later. Flag any session where group dynamics may have compromised data quality, such as dominance by one participant or obvious reluctance to speak.

## Interpretation and Reporting

### Translating Local Terms

Local disease names are often syndromic instead of aetiologic. The Ngakarimojong term *lokit* corresponds to East Coast fever, but it may also encompass other conditions presenting with similar signs [participatory investigation of tick-borne disease management options in Ugandan cattle](https://pubmed.ncbi.nlm.nih.gov/26527312/). Reports must present local terms alongside their interpreted biomedical equivalents and note the degree of correspondence. A term that maps cleanly onto a single disease entity is more useful for surveillance than one that spans several conditions.

### Presenting Uncertainty

Participatory findings carry uncertainty from multiple sources: translation error, group dynamics, recall bias, and the inherent imprecision of syndromic diagnosis. Reports should quantify agreement between groups where possible. The Karamoja study reported Kendall's coefficient of concordance for pairwise rankings across informant groups, providing a formal measure of inter-group agreement [participatory investigation of tick-borne disease management options in Ugandan cattle](https://pubmed.ncbi.nlm.nih.gov/26527312/). Similar approaches should be used where the data permit.

## Integration with Formal Surveillance

Participatory methods complement, instead of replace, conventional surveillance. They are particularly valuable in resource-limited settings where diagnostic capacity is intermittent [lessons for One Health practitioners using local syndromic terminology](https://pubmed.ncbi.nlm.nih.gov/28364831/). Community-based reports can trigger formal investigation, as illustrated by the use of participatory epidemiology to characterize recurrent anthrax outbreaks in Kenya, where community knowledge helped establish the temporal and spatial distribution of prior outbreaks [characterization of recurrent anthrax outbreaks in Kenya](https://pubmed.ncbi.nlm.nih.gov/30105965/).

The relationship between participatory and conventional surveillance should be defined before fieldwork begins. Will participatory findings trigger laboratory confirmation? Will they be used to prioritize diseases for formal investigation? Will they feed into national reporting systems? These decisions shape the data collection protocol.

| Integration Model | Primary Purpose | Data Outputs | Verification Required | Best Suited To |
|---|---|---|---|---|
| Outbreak characterization | Define extent and history of known outbreak | Spatial and temporal mapping, attack rate estimation | Laboratory confirmation of index cases | Acute epidemic events |
| Priority setting | Identify locally important diseases for resource allocation | Rankings, proportional piling scores | Clinical examination of representative cases | Routine surveillance planning |
| Surveillance enhancement | Detect unusual events between formal surveillance rounds | Syndromic reports, community alerts | Laboratory confirmation of alerts | Low-resource or remote settings |
| Research hypothesis generation | Develop hypotheses for formal epidemiological studies | Qualitative descriptions, local terminology | Full diagnostic workup | Academic or investigative research |

The choice of integration model depends on the surveillance objective, available diagnostic capacity, and the relationship between veterinary services and the community. Each model places different demands on the participatory team and produces different types of outputs.

## Recognized Complications and Failure Modes

Participatory epidemiology fails in characteriztic ways that are identifiable during fieldwork. The most common complication is capture of the group by dominant individuals, which distorts rankings and matrix scores toward the views of older men or wealthier herd owners. Detect this early by comparing responses across subgroups within the same community, or by repeating a single ranking exercise with the group split by gender or age. A second failure mode is respondent fatigue, which produces stereotyped answers and declining participation in later exercises. Monitor engagement directly and shorten sessions when attention wavers. A third complication is deliberate misreporting, often motivated by fear of compulsory culling, taxation, or movement restrictions. Cross-check reported disease events against treatment records, slaughter slabs, and neighbouring herds. Power dynamics within communities can also suppress minority views, and facilitators should actively solicit input from women, herders of small ruminants, and younger livestock keepers, as these groups often hold distinct disease knowledge.

A fourth failure mode is the uncritical acceptance of local terminology as equivalent to biomedical diagnoses. Syndromic terms may map imperfectly onto recognized diseases, and the same word can denote different conditions in different communities or seasons. Detect this by collecting specimens for laboratory confirmation whenever feasible and by probing the clinical signs associated with each term during interviews.

## Common Errors and Corrective Actions

Less experienced practitioners frequently make errors in exercise design and interpretation. The most common is leading the group during ranking or scoring, often by phrasing questions that imply a preferred answer. Correct this by using open questions and by having a second facilitator observe and record the phrasing used. A second error is conflating perceived importance with incidence. A disease that is rare but catastrophic may rank higher than a common but mild condition, and the distinction matters for surveillance priorities. Record both frequency and impact separately during proportional piling and pairwise ranking. A third error is over-interpreting small numbers of informant groups. Agreement between groups can be tested statistically, for example with Kendall's coefficient of concordance, but low agreement does not necessarily mean the data are wrong, it may reflect genuine heterogeneity in disease experience between settlements. A fourth error is failing to triangulate participatory findings with other data sources, which leaves conclusions vulnerable to systematic bias in community reporting.

## Limitations of the Evidence Base

The evidence base for participatory epidemiology rests heavily on case studies from pastoralist systems in sub-Saharan Africa, with comparatively little published work from intensive livestock production in high-income regions. Methods that work with transhumant zebu cattle may transfer poorly to housed dairy herds or intensive pig units. Expert opinion differs on how far participatory findings can be quantified. Some authors treat proportional piling and matrix scoring as producing ordinal data suitable for non-parametric testing, while others regard them as purely descriptive. The role of participatory methods in formal surveillance also remains contested. The World Organization for Animal Health recognizes community-based surveillance as a component of national systems, but the standards do not specify how participatory data should be weighted against laboratory-confirmed case reports. Where diagnostic confirmation is absent, findings should be reported as syndromic and clearly labelled as unconfirmed.

## Escalation and Referral

Referral is warranted when participatory findings suggest a notifiable disease, a zoonosis with human cases, or a condition requiring laboratory confirmation for differential diagnosis. Anthrax outbreaks involving livestock and wildlife require immediate engagement with public health authorities and wildlife services, as human cases may accompany livestock deaths. When community reports suggest a disease of trade significance, the WOAH Terrestrial Animal Health Code notification procedures apply. Laboratory involvement is indicated whenever local terminology cannot be mapped confidently to a recognized disease, when matrix scoring suggests a disease that was not previously known to be present, or when control decisions depend on species-level diagnosis, as with the tick-borne disease complex where clinical signs overlap. Regulatory reporting should follow national requirements, and the clinician should document the participatory evidence that triggered the report, including the local terms used, the number of informants, and the geographic extent of the reports.

## Troubleshooting Table

| Observation | Likely cause | Discriminating check |
|---|---|---|
| One informant dominates all exercises | Unequal power within group | Repeat exercise with subgroups, compare scores |
| Responses become short or repetitive | Respondent fatigue | Shorten session, break into smaller groups |
| Reported disease events cannot be verified | Deliberate misreporting or recall bias | Cross-check with treatment records and neighbouring herds |
| Local term maps to different syndromes across villages | Dialect variation or genuine syndromic overlap | Collect clinical descriptions and specimens from each site |
| Matrix scores show no agreement between groups | Heterogeneous disease experience or facilitator bias | Calculate concordance statistic, review question phrasing |
| Participatory findings conflict with laboratory results | Terminology mismatch or sampling error | Re-interview with reference to laboratory findings |

## Frequently Asked Questions

### How much time and budget should be allocated for a participatory epidemiology study?

A full participatory study with 20 to 30 focus groups, key informant interviews, and field verification typically requires 4 to 8 weeks of field time for a two-person team, plus preparation and analysis phases. Budget estimates must include translator costs, transport to remote settlements, recording equipment, and community compensation where culturally expected. The [participatory epidemiology study of tick-borne diseases in Ugandan transhumant cattle](https://pubmed.ncbi.nlm.nih.gov/26527312/) completed 24 focus group discussions, 30 key informant interviews, and clinical sampling within a three month field window. When resources are constrained, reduce the number of sites before reducing the number of methods applied per site, because method triangulation protects data quality more than geographic breadth.

### What can be done when recording equipment or printed materials are unavailable?

Participatory methods were designed for low-resource settings and function without digital tools. Use the ground, a cleared wall, or a large flat stone for proportional piling with seeds, stones, or dried beans. Matrix scoring can be conducted with locally available objects placed on a grid scratched into soil. Record responses by hand in a field notebook with a structured template prepared before the session. The [Maasai community study in Ngorongoro](https://pubmed.ncbi.nlm.nih.gov/28364831/) relied on semi-structured interviews with small groups and manual documentation in settings with limited infrastructure. Photograph the ground-based outputs before disturbing them. If notebooks are unavailable, dictate observations to a second team member immediately after each session.

### How do participatory methods transfer from cattle to small ruminants, pigs, or poultry?

The core methods transfer directly, but disease terminology and husbandry context change. Small ruminants and poultry are often managed by women and children, so group composition must reflect this or key information will be missed. Disease names may be less specific for species with lower economic value per animal, and keepers may group several conditions under one term. For pigs and poultry, ask about mortality events and seasonal patterns before asking about named diseases, because keepers often recognize syndromes better than discrete entities. The [Kenyan anthrax outbreak investigation](https://pubmed.ncbi.nlm.nih.gov/30105965/) demonstrated that participatory methods can capture multi-species disease information when wildlife, livestock, and human health records are reviewed together. Adapt proportional piling denominators to flock or herd size, which varies more for poultry than cattle.

### What records should be kept during a participatory study to support later analysis?

Maintain three record layers: raw session notes, a standardized session summary form, and a master data log. The session summary should capture date, location, group size and composition, facilitator and translator names, methods used, and all rankings or piles with their original units. Record local disease terms verbatim with phonetic spelling and any biomedical terms offered by participants. Note disagreements within groups and how they were resolved. The [Ugandan tick-borne disease study](https://pubmed.ncbi.nlm.nih.gov/26527312/) used Kendall's coefficient of concordance to assess agreement between informant groups, which requires that ranking data be recorded consistently across all sessions. Review the master log daily to identify missing data while communities remain accessible.

### How should findings be explained to a veterinary authority or funding body that expects quantitative outputs?

Frame participatory outputs as complementary to conventional surveillance instead of a substitute. Present rankings and proportional piles as ordinal and semi-quantitative data that generate hypotheses for laboratory confirmation. The [Colombian mastitis study](https://pubmed.ncbi.nlm.nih.gov/30542654/) combined a participatory workshop with a structured cross-sectional survey and somatic cell count testing, demonstrating how qualitative farmer perception data can be linked to quantitative prevalence estimates. Explain that participatory methods identify which diseases communities prioritize and why, information that passive surveillance cannot capture. Offer to convert participatory findings into testable questions for a follow-up prevalence survey. Reference [WOAH animal health surveillance standards](https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection/) when discussing how community-based information can support official reporting obligations.

### When is participatory epidemiology inappropriate or likely to fail?

Avoid participatory methods when communities are experiencing active conflict, when trust in authorities is severely damaged, or when recent disease control interventions have caused economic losses without compensation. The [power dynamics analysis of participatory epidemiology](https://pubmed.ncbi.nlm.nih.gov/32244084/) notes that participatory methods can reproduce existing social hierarchies if facilitators are not attentive to who speaks and who is silent. Do not use these methods when the surveillance question requires precise incidence or prevalence estimates, because participatory data are not designed for that purpose. If communities have been repeatedly surveyed without visible benefit, participation will be low and responses may be strategic instead of accurate. In such settings, invest first in relationship building or use key informant interviews instead of group methods.

## 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

- [Assessing Financial Impacts of Subclinical Mastitis on Colombian Dairy Farms.](https://pubmed.ncbi.nlm.nih.gov/30542654/). 2018.
- [Recurrent Anthrax Outbreaks in Humans, Livestock, and Wildlife in the Same Locality, Kenya, 2014-2017.](https://pubmed.ncbi.nlm.nih.gov/30105965/). 2018.
- [Using participatory epidemiology to investigate management options and relative importance of tick-borne diseases among transhumant zebu cattle in Karamoja Region, Uganda.](https://pubmed.ncbi.nlm.nih.gov/26527312/). 2015.
- [Using local language syndromic terminology in participatory epidemiology: Lessons for One Health practitioners among the Maasai of Ngorongoro, Tanzania.](https://pubmed.ncbi.nlm.nih.gov/28364831/). 2017.
- [Power, participation and their problems: A consideration of power dynamics in the use of participatory epidemiology for one health and zoonoses research.](https://pubmed.ncbi.nlm.nih.gov/32244084/). 2020.
- [Social factors affecting seasonal variation in bovine trypanosomiasis on the Jos Plateau, Nigeria.](https://pubmed.ncbi.nlm.nih.gov/24172046/). 2013.
- [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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> 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.