Designing Participatory Disease Surveillance in Livestock Systems

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-11

Key Takeaways

• Participatory disease surveillance (PDS) leverages livestock keepers' syndromic knowledge, local terminology, and ranking of health problems as primary data inputs, particularly in low-resource settings lacking formal diagnostic capacity. This approach validates indigenous knowledge as a legitimate epidemiological data source, comparable to veterinary classifications when analyzed using methods like matrix scoring.
• Effective PDS design necessitates a systematic approach to local syndromic terminology, involving documentation, translation, and validation against clinical signs and, where possible, biomedical confirmation. This ensures that descriptive local terms, which may encompass multiple pathogens or diseases, are accurately interpreted for surveillance purposes.
• Participatory tools such as semi-structured interviews, ranking, matrix scoring, proportional piling, and mapping are selected based on specific surveillance objectives, ranging from epidemic detection to endemic disease prioritization. Triangulation across these methods, informant groups, and external data sources (clinical examination, lab results) is crucial for validation.
• Reporting pathways must be tailored to local infrastructure, literacy, and network coverage, with mobile technology offering enhanced speed and data quality but requiring robust offline functionality and training. Feedback loops and community engagement are paramount to sustain reporting motivation and prevent system failure due to perceived lack of response.
• System monitoring employs indicators like reporting rate, timeliness, terminology concordance, feedback receipt, and action rate, drawing on WHO and WOAH frameworks where applicable. Common failure modes include erosion of motivation, misinterpretation of local terms, and extractive data capture, which can be mitigated by strong local ownership and clear response protocols.

Participatory disease surveillance (PDS) places livestock keepers at the center of disease detection and reporting, using their observations, local terminology, and ranking of health problems as primary data inputs. This article addresses the design decisions that determine whether such systems produce actionable intelligence in low-resource livestock settings, where conventional diagnostic capacity and formal reporting infrastructure are intermittent or absent. It serves veterinary researchers and field epidemiologists who must plan, implement, or evaluate community-based surveillance programs and who need a procedural framework grounded in published field experience.

The clinical question at the core of PDS design is not whether livestock keepers can recognize disease, but how their syndromic knowledge can be structured, validated, and integrated with biomedical confirmation where available. Pastoralist diagnostic criteria for cattle diseases have shown good agreement with veterinary classifications when compared using matrix scoring methods, which suggests that indigenous knowledge can be treated as a legitimate epidemiological data source instead of a substitute to be tolerated. The design challenge is therefore methodological: selecting participatory tools, defining local syndromic terms, establishing reporting pathways, and building verification mechanisms that function under severe resource constraints.

This article covers the conceptual foundations of participatory surveillance, the selection of appropriate participatory epidemiology techniques, the role of local language syndromic terminology, the integration of mobile technology, and the governance and ethical boundaries that shape program success. It excludes passive surveillance systems, which rely on routine submission of samples or reports without active community engagement, and it does not address laboratory-based surveillance networks.

At a Glance

Conceptual Foundations of Participatory Surveillance

Participatory disease surveillance evolved from participatory epidemiology (PE), a methodological approach developed during rinderpest eradication campaigns in Africa and Pakistan. The core premise is that livestock keepers who observe their animals daily possess detailed, structured knowledge of disease syndromes, seasonal patterns, and risk factors that formal surveillance systems fail to capture. In settings where veterinary services are sparse, this knowledge represents the only continuous source of health information available.

The scientific basis for treating local knowledge as valid epidemiological data rests on studies comparing pastoralist and veterinary classifications of cattle disease. Matrix scoring, in which informants score the association between clinical signs and named diseases, has produced results that agree with veterinary diagnoses when analyzed using hierarchical cluster analysis and multidimensional scaling. This agreement is not perfect, and the authors of such studies recommend combining participatory methods with conventional diagnostic techniques to confirm findings. The practical implication for system design is that local terminology should be treated as a syndromic case definition, subject to the same validation and refinement as any diagnostic criterion.

Participatory surveillance differs from passive reporting in its active generation of data. instead of waiting for livestock keepers to submit reports through official channels, PDS programs send trained facilitators into communities to conduct structured discussions, observe animals directly, and elicit rankings of disease importance. This active engagement produces data on disease prioritization, management practices, and perceived impacts that passive systems cannot provide. In a study of transhumant cattle in Karamoja, Uganda, pairwise ranking by focus groups produced consistent disease prioritization across communities, with Kendall's coefficient of concordance values of 0.568 and 0.682 indicating strong agreement among informant groups.

Defining the Surveillance Objective

The design of a PDS system begins with a precise statement of what the system must detect, for whom, and with what urgency. Surveillance objectives differ fundamentally between programs aimed at epidemic disease detection, endemic disease prioritization, and zoonotic disease control. Highly pathogenic avian influenza surveillance in Indonesia required rapid detection and response at household level in sector 4 poultry, which demanded different tools and reporting timelines than a program tracking chronic tick-borne disease burdens in transhumant cattle.

The objective determines the choice of participatory methods. Semi-structured interviews suit exploratory work where the range of local disease concepts is unknown. Simple ranking establishes which diseases communities consider most important. Pairwise ranking generates a more rigorous ordinal comparison across a defined list of conditions. Matrix scoring tests the association between clinical signs and named diseases, and proportional piling quantifies relative burdens or impacts. Participatory mapping locates disease occurrence in space and can reveal environmental risk factors. These methods are not interchangeable, and a well-designed system selects tools based on the specific information needed.

The surveillance objective also determines the geographic and population scope. Transhumant and pastoralist systems present particular challenges because animal movements cross administrative boundaries and communities may be seasonally dispersed. The Karamoja study operated across 24 settlement areas in two districts, using focus group discussions of 8 to 12 people per manyatta, key informant interviews, and direct observation to build a picture of disease importance across a mobile population. Designers must decide whether the system will follow animals across seasonal migration routes or sample settlements at fixed intervals, and this decision shapes both the sampling frame and the reporting logistics.

Local Terminology as a Surveillance Instrument

Local language terms for livestock diseases are typically syndromic and descriptive instead of aetiological. The Maasai of Ngorongoro District, Tanzania, use terms that describe clinical presentations, and these terms do not map cleanly onto biomedical disease classifications. A single local term may encompass multiple pathogens, or a single disease may carry several names depending on the syndrome it presents. This linguistic complexity is not a barrier to surveillance, but it must be managed systematically.

The first design step is a terminology study to document local disease names, their clinical descriptors, and their perceived causes. This requires semi-structured interviews with community members, followed by translation attempts into veterinary terminology. The translation process is iterative and should involve both veterinary professionals and community informants. Where biomedical confirmation is available, the accuracy of local terms can be tested directly. Where it is not, the system must operate with explicit acknowledgement that syndromic terms carry diagnostic uncertainty.

Matrix scoring provides a structured method for comparing local and veterinary disease concepts. Informants score the likelihood that a named disease produces each of a list of clinical signs, and the resulting matrix is compared with one generated by veterinarians using the same sign list. This comparison can be done visually or statistically, and it reveals both areas of agreement and points of divergence. The output is a validated translation table that links local terms to veterinary diagnoses with defined confidence levels, which then becomes the basis for case definitions in the surveillance system.

Selecting Communities and Participants

Community selection follows directly from the surveillance objective. A system designed to detect emerging zoonoses requires different entry points than one tracking endemic production diseases. In low-resource settings, administrative boundaries rarely align with livestock-keeping communities, so selection should proceed through a staged process: mapping livestock-keeping populations, consulting district veterinary officers and local leaders, and verifying access constraints before committing resources.

Within selected communities, participant recruitment must balance representativeness against practical feasibility. The Ugandan tick-borne disease study recruited 24 focus groups of 8 to 12 participants across two districts, supplemented by 30 key informant interviews, a design that captured variation in transhumant management while remaining logistically manageable. Group composition matters. Mixed groups of men and women may suppress contributions from either sex, whereas separate groups often reveal different disease priorities because herding and milking responsibilities differ. Age stratification serves a similar purpose, as younger herders may recognize different clinical presentations than elders who hold accumulated diagnostic knowledge.

Key informants should include community animal health workers, traditional healers, and livestock owners recognized for diagnostic skill. The Indonesian highly pathogenic avian influenza program found that engaging village-level stakeholders, also official veterinary staff, was essential for sustained reporting. Selection criteria for informants should be explicit and recorded: duration of livestock-keeping experience, species managed, geographic mobility, and willingness to participate over the surveillance period.

Structured and Semi-Structured Data Collection Tools

Semi-structured interviews form the backbone of participatory surveillance. Unlike questionnaires, they allow the interviewer to pursue unexpected information while maintaining a thematic framework. Question design should move from broad to specific, opening with general inquiries about livestock health problems before probing particular syndromes. Closed questions that presuppose biomedical categories should be avoided in early interviews, as they force local knowledge into imported frameworks.

Simple ranking establishes disease priorities by asking participants to list important diseases and then order them by perceived impact. Pairwise ranking compares diseases in all possible pairs, producing a matrix from which a preference order emerges. The Ugandan study used pairwise ranking to show that East Coast fever, anaplasmosis, and contagious bovine pleuropneumonia were consistently ranked most important across informant groups, with Kendall's coefficient of concordance confirming agreement between groups. Proportional piling asks participants to distribute a fixed number of counters or stones among disease categories, yielding quantitative estimates of relative importance that can be compared across groups.

Matrix scoring combines local disease names with clinical signs, species affected, seasonality, and other attributes. Participants score each disease against each attribute using counters, and the resulting matrix can be compared statistically across informant groups. The method has been validated as a means of comparing pastoralist diagnostic criteria with veterinary knowledge, with good agreement between the two knowledge systems. Matrix scoring is particularly valuable when local terminology does not map cleanly onto biomedical disease categories.

Participatory mapping locates disease events in space and time. Participants draw maps of grazing areas, water points, and settlement patterns, then mark where disease outbreaks occur and when. This technique generates hypotheses about transmission dynamics that conventional surveillance would miss, especially in transhumant systems where animals move across administrative boundaries.

Validation and Triangulation

Local syndromic terminology requires validation before it can support surveillance decisions. The Maasai study in Ngorongoro documented that livestock keepers use descriptive terms that are syndromic instead of aetiological, and the authors noted that confirmatory biomedical diagnostics are often omitted due to time, financial, or logistical constraints. This omission carries risk. A single local term may encompass multiple diseases, or one disease may carry several names across communities.

Triangulation operates at three levels. First, methodological triangulation compares findings from different data collection tools, such as ranking results against matrix scoring or participatory mapping. Second, informant triangulation compares responses across groups, using measures like Kendall's coefficient of concordance to quantify agreement. Third, source triangulation compares participatory findings with clinical examination, laboratory results, and existing surveillance records. The Ugandan study combined all three, conducting clinical examinations and laboratory confirmation alongside participatory exercises.

When laboratory confirmation is unavailable, the surveillance system should document this limitation explicitly and flag unconfirmed diagnoses as provisional. The Ngorongoro study illustrates the practical compromise: local terminology was characterized and its limitations assessed, but diagnoses were not confirmed biomedically. A well-designed system records the confidence level attached to each report and distinguishes confirmed from suspected cases in its outputs.

Digital Data Capture and Reporting Pathways

Mobile technology has expanded the reach of participatory surveillance. The AfyaData application was developed through an EpiHack process in Tanzania, bringing together human and animal health experts with ICT programrs to address challenges in early detection and timely reporting. The system trains community-based reporters to submit disease events through smartphones, with data flowing from community level to national authorities.

Digital tools change the design parameters of participatory surveillance. Data entry errors can be reduced through structured forms with dropdown menus and validation rules, but the forms must accommodate local terminology instead of forcing reporters into biomedical categories. Offline functionality is essential in areas with intermittent connectivity. The AfyaData experience demonstrates that community reporters require training also in the technology but also in disease recognition and reporting criteria.

Reporting pathways must specify what triggers a report, to whom it is sent, and what response is expected. The Indonesian PDSR program evolved from separate surveillance and response teams to an integrated approach that strengthened district livestock services and focused on village-level engagement. This evolution reflects a broader lesson: reporting systems fail when communities perceive that reports disappear into an unresponsive bureaucracy. Feedback loops, even simple acknowledgements or monthly summaries, sustain participation.

Monitoring System Performance

Participatory surveillance systems require their own monitoring framework. Timeliness measures the interval between disease event and report receipt. Completeness tracks the proportion of expected reports actually submitted. Sensitivity can be assessed through periodic active case finding that compares detected cases against a reference standard, though this is resource-intensive and rarely feasible at scale.

Table 1 presents performance indicators suitable for participatory surveillance systems.

Species and production system modify these parameters. Intensive poultry systems generate frequent, low-impact events that require different reporting thresholds than transhumant cattle systems where each report may represent substantial economic loss. The Indonesian program focused on sector 4 poultry at household level, where the density of susceptible birds and proximity to humans created particular risk. Surveillance objectives must therefore specify species, production system, and geographic scope before indicators are selected.

International standards provide a framework for evaluating surveillance outputs. The World Organization for Animal Health publishes surveillance standards and reporting requirements that member countries are expected to meet, and the Terrestrial Animal Health Code addresses surveillance in the context of trade-related disease control. Participatory systems should be designed so their outputs can feed into these formal reporting structures, even when the participatory component operates outside official channels.

Recognized Complications and Failure Modes

Participatory surveillance systems fail in predictable patterns. The most common complication is the gradual erosion of reporting motivation after the initial project enthusiasm fades. Community reporters stop submitting observations when they perceive no response, when incentives lapse, or when the reporting burden exceeds the perceived benefit. Early detection relies on monitoring reporting frequency per community over time, also total case counts. A sustained decline in reports from a specific village, while neighbouring villages maintain volume, signals a local motivation problem instead of a true epidemiological change.

A second failure mode is the misinterpretation of local syndromic terms. Livestock keepers use descriptive language that maps imperfectly onto biomedical diagnoses. The Maasai terminology study in Ngorongoro demonstrated that local names often group several clinically similar diseases under one term, and conversely split a single disease into multiple names based on host species or perceived cause Queenan et al., local language syndromic terminology in participatory epidemiology. When surveillance staff translate these terms without verification, case definitions drift and incidence estimates become unreliable. The discriminating check is triangulation: compare local term usage against clinical examination findings and, where feasible, laboratory confirmation.

A third complication involves the capture of surveillance data by external actors. Systems designed with strong community input but weak local ownership become extractive. Communities report into a system they cannot access, and feedback loops collapse. The Indonesian highly pathogenic avian influenza program addressed this by shifting from separate surveillance and response teams to an integrated model embedded within district livestock services, which improved accountability and community trust Azhar et al., participatory disease surveillance and response in Indonesia.

Common Errors in Implementation

Less experienced practitioners frequently skip the terminology validation phase and proceed directly to data collection. This produces surveillance data that cannot be compared across communities or aggregated at regional level. The corrective action is to invest in structured terminology work before deployment, using matrix scoring and pairwise ranking to establish agreed translations Byaruhanga et al., participatory epidemiology of tick-borne diseases in Karamoja.

A second recurring error is the conflation of community participation with community consultation. Genuine participatory surveillance delegates decision authority over data use, feedback mechanisms, and response priorities. Consultation merely seeks input while retaining control centrally. The brucellosis control analysis in Israel highlighted how participatory boundaries, including who is allowed to contribute and how, shape intervention effectiveness Hermesh et al., rethinking One Health through brucellosis. Practitioners should audit whether communities can view their own data and influence subsequent actions.

A third error is the over-reliance on digital tools without addressing connectivity and literacy constraints. Mobile applications such as AfyaData improve reporting speed and data quality, but they presuppose network access and reporter training Karimuribo et al., AfyaData smartphone app for One Health surveillance. Where these assumptions fail, paper-based or voice-based reporting pathways must remain available as fallbacks.

Limitations of the Evidence Base

The published literature on participatory surveillance consists largely of program descriptions and cross-sectional studies instead of controlled evaluations. Comparative effectiveness data, such as sensitivity and specificity of participatory methods against active surveillance gold standards, are sparse. The Indonesian program reports describe evolution and lessons learned but do not provide quantitative performance metrics Azhar et al., participatory disease surveillance and response in Indonesia. Expert opinion differs on whether participatory methods should be considered complementary to conventional surveillance or a primary system in low-resource settings. The WOAH surveillance standards describe general principles for surveillance system design and evaluation but do not prescribe participatory methods in detail WOAH animal health surveillance standards. Practitioners should therefore document local performance indicators and publish outcomes to strengthen the evidence base.

Escalation and Referral Criteria

Referral to specialist services is warranted when local terminology validation reveals a syndrome that cannot be matched to a known veterinary disease, when unusual clinical presentations cluster in time or space, or when laboratory confirmation is required to distinguish between diseases with similar syndromic presentations. The tick-borne disease study in Karamoja combined participatory ranking with clinical examination and laboratory confirmation precisely because local names such as lokit and lopid required laboratory differentiation Byaruhanga et al., participatory epidemiology of tick-borne diseases in Karamoja.

Regulatory reporting obligations apply when participatory surveillance detects a notifiable disease. The WOAH terrestrial animal health code defines notification requirements for listed diseases, and national veterinary authorities determine local reporting pathways WOAH terrestrial animal health code. Surveillance managers must establish these pathways before deployment, not after detection. Where zoonotic diseases are suspected, coordination with human health authorities follows the same escalation logic, and the One Health framework provides the structural basis for such collaboration Queenan et al., local language syndromic terminology in participatory epidemiology.

Frequently Asked Questions

How much does participatory surveillance cost compared with conventional active surveillance?

Participatory approaches typically cost less than conventional active surveillance because they rely on existing community structures, local informants, and minimal laboratory confirmation. The main expenditures are training facilitators, transport to settlements, and time for repeated community engagement. Digital reporting platforms add development and maintenance costs but reduce data entry and transmission expenses. In the Indonesian highly pathogenic avian influenza program, the participatory disease surveillance and response model evolved specifically to remain sustainable within district livestock service budgets while still achieving early detection and response objectives. Budget realistically for repeated visits, because trust and data quality depend on continuity instead of one-off contact. Costs vary substantially by livestock system, geography, and the number of species under surveillance.

What should be done when digital devices or network coverage are unavailable?

Paper-based tools remain fully viable. Semi-structured interview guides, ranking cards, proportional piling materials, and printed mapping templates require no electricity and work in remote settings. The AfyaData experience in Africa demonstrated that digital tools enhance reporting speed and completeness, but the same project began with participatory workshops that identified local constraints before technology was introduced. When devices are scarce, designate one community reporter per settlement and rotate devices among villages on a fixed schedule. Record data on standardized paper forms first, then transcribe when connectivity returns. Ensure that the surveillance objective, not the technology, drives tool selection. If digital capture is impossible, maintain a physical register at a central point and transmit summaries through existing livestock service channels.

How do participatory surveillance methods need to be adapted for monogastric or intensive production systems?

The methods were developed largely with pastoralist cattle systems, but they transfer to other species with adjustments to sampling frames and local terminology. For poultry, household-level focus groups work well because birds are often managed by women and children, and the Indonesian program successfully used village-level participatory response teams for highly pathogenic avian influenza. For pigs, engage with the individuals responsible for feeding and breeding decisions, who may differ from the household head. Intensive systems require attention to farm staff hierarchies and biosecurity protocols that can discourage open reporting. Matrix scoring and proportional piling remain useful, but disease names may be less standardized than in pastoralist systems, so invest more time in terminology validation before ranking exercises.

What records should be kept to allow later evaluation of the surveillance system?

Maintain a surveillance register that logs each community contact, participant numbers, methods used, local disease terms elicited, and any clinical or laboratory findings. Record the raw outputs of ranking and scoring exercises, also the final disease list, because these allow recalculation of concordance statistics such as Kendall's coefficient of concordance. Document all triangulation steps, including which findings were confirmed by clinical examination or laboratory testing and which were not. Keep a decisions log that records how each report was escalated, what response occurred, and the time interval between report and action. These records support both performance monitoring and retrospective evaluation. The World Organization for Animal Health surveillance standards require documentation that demonstrates the system's purpose, design, and outputs.

How should findings be communicated to veterinary authorities or funding agencies?

Present results using the same triangulated evidence that guided internal decisions. Show the convergence between community reports, clinical findings, and laboratory confirmation where available. Report local disease names alongside standard veterinary terminology, with an explicit statement of how the translation was validated. Use ranking outputs to demonstrate which conditions communities perceive as most important, and pair this with any quantitative data on case frequency or mortality. The comparison of pastoralist and veterinarian diagnostic criteria in East Africa showed that structured matrix scoring can produce agreement that is visually and statistically demonstrable, which strengthens the credibility of community-derived data for external audiences. Describe system performance using the monitoring indicators defined at the design stage, including timeliness of reporting and completeness of community coverage.

How can a clinician explain participatory surveillance to a livestock owner who expects individual animal diagnosis?

Frame the approach as an extension of clinical reasoning. Explain that the clinician is gathering the same information a careful owner would provide, but from many herds at once, to detect problems before they spread. Emphasize that individual sick animals still receive clinical examination and treatment when indicated. Clarify that group discussions, ranking exercises, and mapping are efficient ways to understand which diseases affect the whole community, not a substitute for examining their animals. In the Karamoja study, livestock keepers readily engaged with semi-structured interviews and ranking methods when the purpose was explained in terms of managing tick-borne diseases that they themselves had named and prioritized. Reassure owners that their local disease terms are valued and will be interpreted carefully, not dismissed as unscientific.

Related Clinical & Scientific Guides

• Evaluating Veterinary Surveillance System Attributes
• Network Analysis for Infectious Disease Spread in Animal Populations
• Randomized Controlled Trials in Veterinary Field Settings

References and Further Reading

• Using participatory epidemiology to investigate management options and relative importance of tick-borne diseases among transhumant zebu cattle in Karamoja Region, Uganda.. 2015.
• Using local language syndromic terminology in participatory epidemiology: Lessons for One Health practitioners among the Maasai of Ngorongoro, Tanzania.. 2017.
• A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance.. 2017.
• Participatory disease surveillance and response in Indonesia: strengthening veterinary services and empowering communities to prevent and control highly pathogenic avian influenza.. 2010.
• Use of participatory epidemiology to compare the clinical veterinary knowledge of pastoralists and veterinarians in East Africa.. 2006.
• Rethinking "One Health" through Brucellosis: ethics, boundaries and politics.. 2019.
• WOAH Animal Health Surveillance Standards. WOAH.
• CDC Principles of Epidemiology in Public Health Practice. CDC.
• MSD Veterinary Manual, Professional Edition. MSD Veterinary Manual.

Related Articles

• Designing and Implementing Animal Disease Surveillance Systems
• Participatory Epidemiology Methods for Livestock Disease Surveillance
• Time Series Analysis for Veterinary Disease Surveillance
• Data Quality Assurance in Veterinary Surveillance Systems
• Designing Case Definitions for Veterinary Surveillance

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.